Encyclopedia of Ocean Sciences, Second Edition, Volume 6: T-Z

ENCYCLOPEDIA OF OCEAN SCIENCES SECOND EDITION Editor-in-chief JOHN H. STEELE Editors STEVE A. THORPE KARL K. TUREK...

Author: John H. Steele | Steve A. Thorpe | Karl K. Turekian

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ENCYCLOPEDIA OF

OCEAN SCIENCES SECOND EDITION

Editor-in-chief

JOHN H. STEELE

Editors

STEVE A. THORPE KARL K. TUREKIAN

Boston • Heidelberg • London • New York • Oxford Paris • San Diego • San Francisco • Singapore • Sydney • Tokyo Academic Press is an imprint of Elsevier

(c) 2011 Elsevier Inc. All Rights Reserved.

ENCYCLOPEDIA OF

OCEAN SCIENCES SECOND EDITION


Subject Area Volumes from the Second Edition Climate & Oceans edited by Karl K. Turekian Elements of Physical Oceanography edited by Steve A. Thorpe Marine Biology edited by John H. Steele Marine Chemistry & Geochemistry edited by Karl K. Turekian Marine Ecological Processes edited by John H. Steele Marine Geology & Geophysics edited by Karl K. Turekian Marine Policy & Economics guest edited by Porter Hoagland, Marine Policy Center, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts Measurement Techniques, Sensors & Platforms edited by Steve A. Thorpe Ocean Currents edited by Steve A. Thorpe The Coastal Ocean edited by Karl K. Turekian The Upper Ocean edited by Steve A. Thorpe


ENCYCLOPEDIA OF

OCEAN SCIENCES SECOND EDITION Volume 6: T - Z Editor-in-chief

JOHN H. STEELE

Editors

STEVE A. THORPE KARL K. TUREKIAN

Boston • Heidelberg • London • New York • Oxford Paris • San Diego • San Francisco • Singapore • Sydney • Tokyo Academic Press is an imprint of Elsevier


Academic Press is an imprint of Elsevier 32 Jamestown Road, London NW1 7BY, UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA Copyright ^ 2009 Elsevier Ltd. All rights reserved

The following articles are US government works in the public domain and are not subject to copyright: Fish Predation and Mortality; International Organizations; Large Marine Ecosystems; Ocean Circulation: Meridional Overturning Circulation; Salt Marsh Vegetation; Satellite Passive-Microwave Measurements of Sea Ice; Satellite Oceanography, History and Introductory Concepts; Satellite Remote Sensing: Ocean Color; Science of Ocean Climate Models; Wind- and Buoyancy-Forced Upper Ocean. Fish Migration, Horizontal Crown Copyright 2001 Turbulence Sensors Canadian Crown Copyright 2001 No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher

Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44)(0) 1865 843830; fax (+44)(0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier website at (http://elsevier.com/locate/permissions), and selecting Obtaining permissions to use Elsevier material

Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein, Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made

British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library

Library of Congress Control Number: 2009935908

ISBN: 978-0-12-375044-0

For information on all Elsevier publications visit our website at www.elsevierdirect.com

PRINTED AND BOUND IN ITALY 09 10 11 12 13 10 9 8 7 6 5 4 3 2 1


Editors

Editor-in-chief John H. Steele Marine Policy Center, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA

Editors Steve A. Thorpe National Oceanography Centre, University of Southampton Southampton, UK School of Ocean Sciences, University of Bangor, Menai Bridge, Anglesey, UK Karl K. Turekian Yale University, Department of Geology and Geophysics, New Haven, Connecticut, USA


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Editorial Advisory Board John H. S. Blaxter Scottish Association for Marine Science Dunstaffnage Marine Laboratory Oban Argyll, UK Quentin Bone The Marine Biological Association of the United Kingdom Plymouth, UK Kenneth H. Brink Woods Hole Oceanographic Institution Woods Hole MA, USA Harry L. Bryden School of Ocean and Earth Science James Rennell Division University of Southampton Empress Dock Southampton, UK Robert Clark University of Newcastle upon Tyne Marine Sciences and Coastal Management Newcastle upon Tyne, UK J. Kirk Cochran State University of New York at Stony Brook Marine Sciences Research Center Stony Brook NY, USA Jeremy S. Collie Coastal Institute Graduate School of Oceanography University of Rhode Island South Ferry Road Narragansett RI, USA

Paul G. Falkowski Departments of Geological Sciences & Marine & Coastal Sciences Institute of Marine & Coastal Sciences School of Environmental & Biological Sciences Rutgers University New Brunswick NJ, USA Mike Fashamw Southampton Oceanography Centre University of Southampton Southampton UK John G. Field MArine REsearch (MA-RE) Institute University of Cape Town Rondebosch South Africa Michael Fogarty NOAA, National Marine Fisheries Service Woods Hole MA, USA Wilford D. Gardner Department of Oceanography Texas A&M University College Station TX, USA Ann Gargett Old Dominion University Center for Coastal Physical Oceanography Crittenton Hall Norfolk VA, USA

Peter J. Cook Australian Petroleum Cooperative Research Centre Canberra, Australia

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Robert A. Duce Departments of Oceanography and Atmospheric Sciences Texas A&M University College Station TX, USA

w

deceased


Editorial Advisory Board

Christopher Garrett University of Victoria Department of Physics Victoria British Columbia, Canada

Lindsay Lairdw Aberdeen University Zoology Department Aberdeen UK

W. John Gould Southampton Oceanography Centre University of Southampton Southampton UK

Peter S. Liss University of East Anglia School of Environmental Sciences Norwich, UK

John S. Grayw Institute of Marine Biology and Limnology University of Oslo Blindern Oslo, Norway

Ken Macdonald University of California Department of Geological Sciences Santa Barbara CA, USA

Gwyn Griffiths Southampton Oceanography Centre University of Southampton Southampton UK

Dennis McGillicuddy Woods Hole Oceanographic Institution Woods Hole MA, USA Alasdair McIntyre University of Aberdeen Department of Zoology Aberdeen UK

Stephen J. Hall World Fish Center Penang Malaysia Roger Harris Plymouth Marine Laboratory West Hoe Plymouth, UK Porter Hoagland Woods Hole Oceanographic Institution Woods Hole MA, USA George L. Hunt Jr. University of California, Irvine Department of Ecology and Evolutionary Biology Irvine CA, USA William J. Jenkins Woods Hole Oceanographic Institution Woods Hole MA, USA

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deceased

W. Kendall Melville Scripps Institution of Oceanography UC San Diego La Jolla CA, USA John Milliman College of William and Mary School of Marine Sciences Gloucester Point VA, USA James N. Moum College of Oceanic and Atmospheric Sciences Oregon State University Corvallis OR, USA Michael M. Mullinw Scripps Institution of Oceanography Marine Life Research Group University of California San Diego La Jolla CA, USA


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Editorial Advisory Board

Yoshiyuki Nozakiw University of Tokyo The Ocean Research Institute Nakano-ku Tokyo Japan

Ellen Thomas Yale University Department of Geology and Geophysics New Haven CT, USA

John Orcutt Scripps Institution of Oceanography Institute of Geophysics and Planetary Physics La Jolla CA, USA Richard F. Pittenger Woods Hole Oceanographic Institution Woods Hole MA, USA Gerold Siedler Universita¨t Kiel Institut fua¨r Meereskunde Kiel Germany

Peter L. Tyack Woods Hole Oceanographic Institution Woods Hole MA, USA Bruce A. Warren Woods Hole Oceanographic Institution Woods Hole MA, USA Wilford F. Weeks University of Alaska Fairbanks Department of Geology and Geophysics Fairbanks AK, USA

Robert C. Spindel University of Washington Applied Physics Laboratory Seattle WA, USA

Robert A. Weller Woods Hole Oceanographic Institution Woods Hole MA, USA

Colin P. Summerhayes Scientific Committee on Antarctic Research (SCAR) Scott Polar Institute Cambridge, UK

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Stewart Turner Australian National University Research School of Earth Sciences Canberra Australia

James A. Yoder Woods Hole Oceanographic Institution Woods Hole MA, USA

deceased


Preface to Second Print Edition The first edition of the Encyclopedia of Ocean Sciences, published in print form in 2001, has proven to be a valuable asset for the marine science community – and more generally. The continuing rapid increase in electronic access to academic material led us initially to publish the second edition electronically. We have now added this print version of the second edition because of a demonstrated need for such a product. The encyclopedia can now be accessed in print or electronic format according to the preferences and needs of individuals and institutions. In this edition there are 54 new articles, 67 revisions of previous articles, and a completely revised and improved index. We are grateful to the members of the Editorial Advisory Board, nearly all of whom have stayed with us during the lengthy process of going electronic. The transition from Academic Press to Elsevier occurred between the two editions. We thank Dr. Debbie Tranter of Elsevier for her efforts to see this edition through its final stages.

Preface to First Edition In 1942, a monumental volume was published on The Oceans by H. U. Sverdrup, M. W. Johnson, and R. H. Fleming. It was comprehensive and covered the knowledge at that time of the scientific study of the oceans. This seminal book helped to initiate the tremendous burgeoning of marine research that occurred during the following decades. The Encyclopedia of Ocean Sciences aims to embody the great growth of knowledge in a major new reference work. There have been remarkable new approaches to the study of the oceans that blur the distinctions between the physical, chemical, biological, and geological disciplines. New theories and technologies have expanded our knowledge of ocean processes. For example, plate tectonics has revolutionized our view not only of the geology and geophysics of the seafloor but also of ocean chemistry and biology. Satellite remote sensing provides a global vision as well as detailed understanding of the close coupling of ocean physics and biology at local and regional scales. Exploration, fishing, warfare, and the impact of storms have driven the past study of the seas, but we now have a great public awareness of and concern with broader social and economic issues affecting the oceans. For this reason, we have invited articles explicitly on marine policy and environmental topics, as well as encouraged authors to address these aspects of their particular subjects. We believe the encyclopedia should be of use to those involved with policy and management as well as to students and researchers. Over 400 scientists have contributed to this description of what we now know about the oceans. They are distinguished researchers who have generously shared their knowledge of this ever-growing body of science. We are extremely grateful to all these authors, whose ability to write concisely on complex subjects has generated a perspective on our science that we, as editors, believe will enhance the appreciation of the oceans, their uses, and the research ahead. It has been a major challenge for the members of the Editorial Advisory Board to cover such a heterogeneous subject. Their knowledge of the diverse areas of research has guaranteed comprehensive coverage of the ocean sciences. The Board contributed significantly by suggesting topics, persuading authors to contribute, and reviewing drafts. Many of them wrote Overviews that give broad descriptions of major parts of the ocean sciences. Clearly, it was the dedicated involvement of the Editorial Advisory Board that made this venture successful. Such a massive enterprise as a multivolume encyclopedia would not be possible without the long-term commitment of the staff of the Major Reference Works team at Academic Press. In particular, we are very grateful for the consistent support of our Senior Developmental Editor, Colin McNeil, who has worked so well with us throughout the whole process. Also, we are very pleased that new technology permits enhanced search and retrieval through the Internet. We believe this will make the encyclopedia much more accessible to individual researchers and students.


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Preface to Second Print Edition

In Memoriam During the creation of the Encyclopedia of Ocean Sciences and also in several cases prior to the publication of the electronic Second Edition, several Associate Editors or designated Associate Editors died. We specifically acknowledge their role in making this work an effective publication. They are Mike Fasham, John S. Gray, Lindsay Laird, Michael Mullin and Yoshiyuki Nozaki. J. H. Steele, S. A. Thorpe, and K. K. Turekian Editors


Guide to Use of the Encyclopedia

Introductory Points In devising the vision and structure for the Encyclopedia, the Editors have striven to unite and interrelate all current knowledge that can be designated ‘‘Ocean Sciences’’. To aid users of the Encyclopedia, this new reference work offers intuitive searching and extensive cross-linking of content. These features are explained in more detail below.

Structure of the Encyclopedia The material in the Encyclopedia is arranged as a series of articles in alphabetical order. To help you realize the full potential of the material in the Encyclopedia we have provided three features to help you find the topic of your choice.

1. Contents Lists Your first point of reference will probably be the contents list. The contents list appearing in each volume will provide you with the page number of the article. Alternatively you may choose to browse through a volume using the alphabetical order of the articles as your guide. To assist you in identifying your location within the Encyclopedia a running headline indicates the current article.

2. Cross References All of the articles in the encyclopedia have heen extensively cross referenced. The cross references, which appear at the end of each article, have heen provided at three levels: i. To indicate if a topic is discussed in greater detail elsewhere.

ACOUSTICS, ARCTIC See also: Acoustics in Marine Sediments. Acoustic Noise. Acoustics, Shallow Water. Arctic Ocean Circulation. Bioacoustics. Ice–ocean interaction. Nepheloid Layers. North Atlantic Oscillation (NAO). Ocean Circulation: Meridional Overturning Circulation. Platforms: Autonomous Underwater Vehicles. Satellite Passive-Microwave Measurements of Sea Ice. Sea Ice. Sea Ice: Overview. Seals. Seismic Structure. Tomography. Under-Ice Boundary Layer. Water Types and Water Masses.

ii. To draw the reader’s attention to parallel discussions in other articles. ACOUSTICS, ARCTIC See also: Acoustics in Marine Sediments. Acoustic Noise. Acoustics, Shallow Water. Arctic Ocean Circulation. Bioacoustics. Ice–ocean interaction. Nepheloid Layers. North Atlantic Oscillation (NAO). Ocean Circulation: Meridional Overturning Circulation. Platforms: Autonomous Underwater Vehicles. Satellite Passive-Microwave Measurements of Sea Ice. Sea Ice. Sea Ice: Overview. Seals. Seismic Structure. Tomography. Under-Ice Boundary Layer. Water Types and Water Masses.


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Guide to Use of the Encyclopedia

iii. To indicate material that broadens the discussion.

ACOUSTICS, ARCTIC See also: Acoustics in Marine Sediments. Acoustic Noise. Acoustics, Shallow Water. Arctic Ocean Circulation. Bioacoustics. Ice–ocean interaction. Nepheloid Layers. North Atlantic Oscillation (NAO). Ocean Circulation: Meridional Overturning Circulation. Platforms: Autonomous Underwater Vehicles. Satellite Passive-Microwave Measurements of Sea Ice. Sea Ice. Sea Ice: Overview. Seals. Seismic Structure. Tomography. Under-Ice Boundary Layer. Water Types and Water Masses.

3. Index The index will provide you with the volume and page number where the material is to be located, and the index entries differentiate between material that is a whole article, is part of an article or is data presented in a table or figure. On the opening page of the index detailed notes are provided.

4. Appendices In addition to the articles that form the main body of the encyclopedia, there are a number of appendices which provide bathymetric charts and lists of data used throughout the encyclopedia. The appendices are located in volume 6, before the index.

5. Contributors A full list of contributors appears at the beginning of volume 1.


Contributors Volume 1 E E Adams

N Caputi

Massachusetts Institute of Technology, Cambridge, MA, USA

Fisheries WA Research Division, North Beach, WA, Australia

T Akal

C A Carlson

NATO SACLANT Undersea Research Centre, La Spezia, Italy

University of California, Santa Barbara, CA, USA H Chamley

R Arimoto New Mexico State University, Carlsbad, NM, USA

Universite´ de Lille 1, Villeneuve d’Ascq, France R Chester

J L Bannister The Western Australian Museum, Perth, Western Australia

Liverpool University, Liverpool, Merseyside, UK V Christensen University of British Columbia, Vancouver, BC, Canada

E D Barton University of Wales, Bangor, UK

J W Dacey

N R Bates Bermuda Biological Station for Research, St George’s, Bermuda, USA

Woods Hole Oceanographic Institution, Woods Hole, MA, USA R A Duce

A Beckmann

Texas A&M University, College Station, TX, USA

Alfred-Wegener-Institut fu¨r Polar und Meeresforschung, Bremerhaven, Germany

H W Ducklow

P S Bell

The College of William and Mary, Gloucester Point, VA, USA

Proudman Oceanographic Laboratory, Liverpool, UK I Dyer G Birnbaum

Marblehead, MA, USA

Alfred-Wegener-Institut fu¨r Polar und Meeresforschung, Bremerhaven, Germany

D W Dyrssen Gothenburg University, Go¨teborg, Sweden

B O Blanton The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA E A Boyle Massachusetts Institute of Technology, Cambridge, MA, USA

S M Evans Newcastle University, Newcastle, UK I Everson Anglia Ruskin University, Cambridge, UK

P Boyle

J W Farrington

University of Aberdeen, Aberdeen, UK

Woods Hole Oceanographic Institution, MA, USA

D M Bush

M Fieux

State University of West Georgia, Carrollton, GA, USA

Universite´ Pierre et Marie Curie, Paris, France

K Caldeira

R A Fine

Stanford University, Stanford, CA, USA

University of Miami, Miami, FL, USA


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Contributors

K G Foote Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA L François University of Lie`ge, Lie`ge, Belgium M A M Friedrichs Old Dominion University, Norfolk, VA, USA T Gaston National Wildlife Research Centre, Quebec, Canada J Gemmrich University of Victoria, Victoria, BC, Canada Y Godde´ris University of Lie`ge, Lie`ge, Belgium D R Godschalk University of North Carolina, Chapel Hill, NC, USA A J Gooday Southampton Oceanography Centre, Southampton, UK A L Gordon Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA D A Hansell University of Miami, Miami FL, USA L W Harding Jr University of Maryland, College Park, MD, USA R Harris Plymouth Marine Laboratory, Plymouth, UK P J Herring Southampton Oceanography Centre, Southampton, UK B M Hickey University of Washington, Seattle, WA, USA M A Hixon Oregon State University, Corvallis, OR, USA E E Hofmann Old Dominion University, Norfolk, VA, USA S Honjo Woods Hole Oceanographic Institution, Woods Hole, MA, USA D J Howell Newcastle University, Newcastle, UK J M Huthnance CCMS Proudman Oceanographic Laboratory, Wirral, UK B Ja¨hne University of Heidelberg, Heidelberg, Germany F B Jensen SACLANT Undersea Research Centre, La Spezia, Italy A John Sir Alister Hardy Foundation for Ocean Science, Plymouth, UK

C D Jones University of Washington, Seattle, WA, USA P F Kingston Heriot-Watt University, Edinburgh, UK W Krauss Institut fu¨r Meereskunde an der Universita¨t Kiel, Kiel, Germany W A Kuperman Scripps Institution of Oceanography, University of California, San Diego, CA, USA D Lal Scripps Institute of Oceanography, University of California San Diego, La Jolla, CA, USA C S Law Plymouth Marine Laboratory, The Hoe, Plymouth, UK W J Lindberg University of Florida, Gainesville, FL, USA J R E Lutjeharms University of Cape Town, Rondebosch, South Africa P Malanotte-Rizzoli Massachusetts Institute of Technology, Cambridge, MA, USA W R Martin Woods Hole Oceanographic Institution, Woods Hole, MA, USA R P Matano Oregon State University, Corvallis, OR, USA J W McManus University of Miami, Miami, FL, USA G M McMurtry University of Hawaii at Manoa, Honolulu, HI, USA R Melville-Smith Fisheries WA Research Division, North Beach, WA, Australia P N Mikhalevsky Science Applications International Corporation, McLean, VA, USA W D Miller University of Maryland, College Park, MD, USA D Monahan University of New Hampshire, Durham, NH, USA J C Moore University of California at Santa Cruz, Santa Cruz, CA, USA A Morel Universite´ Pierre et Marie Curie, Villefranche-sur-Mer, France R Narayanaswamy The University of Manchester, Manchester, UK


Contributors W J Neal Grand Valley State University, Allendale, MI, USA D Pauly University of British Columbia, Vancouver, BC, Canada J W Penn Fisheries WA Research Division, North Beach, WA, Australia L C Peterson University of Miami, Miami, FL, USA S G Philander Princeton University, Princeton, NJ, USA N J Pilcher Universiti Malaysia Sarawak, Sarawak, Malaysia O H Pilkey Duke University, Durham, NC, USA

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D H Shull Western Washington University, Bellingham, WA, USA D K Steinberg College of William and Mary, Gloucester Pt, VA, USA L Stramma University of Kiel, Kiel, Germany R N Swift NASA Goddard Space Flight Center, Wallops Island, VA, USA T Takahashi Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA P D Thorne Proudman Oceanographic Laboratory, Liverpool, UK

A R Piola Universidad de Buenos Aires, Buenos Aires, Argentina J M Prospero University of Miami, Miami, FL, USA S Rahmstorf Potsdam Institute for Climate Impact Research, Potsdam, Germany P C Reid SAHFOS, Plymouth, UK G Reverdin LEGOS, Toulouse Cedex, France S R Rintoul CSIRO Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, TAS, Australia J M Roberts Scottish Association for Marine Science, Oban, UK P A Rona Rutgers University, New Brunswick, NJ, USA T C Royer Old Dominion University, Norfolk, VA, USA B Rudels Finnish Institute of Marine Research, Helsinki, Finland

P L Tyack Woods Hole Oceanographic Institution, Woods Hole, USA T Tyrrell National Oceanography Centre, Southampton, UK F E Werner The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA E A Widder Harbor Branch Oceanographic Institution, Fort Pierce, FL, USA D J Wildish Fisheries and Oceans Canada, St. Andrews, NB, Canada A J Williams, III Woods Hole Oceanographic Institution, Woods Hole, MA, USA D K Woolf Southampton Oceanography Centre, Southampton, UK

W Seaman University of Florida, Gainesville, FL, USA

C W Wright NASA Goddard Space Flight Center, Wallops Island, VA, USA

F Sevilla, III, University of Santo Tomas, Manila,The Philippines

J D Wright Rutgers University, Piscataway, NJ, USA

L V Shannon University of Cape Town, Cape Town, South Africa

J R Young The Natural History Museum, London, UK

G I Shapiro University of Plymouth, Plymouth, UK

H J Zemmelink University of Groningen, Haren, The Netherlands

A D Short University of Sydney, Sydney, Australia

W Zenk Universita¨t Kiel, Kiel, Germany


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Contributors

Volume 2 G P Arnold Centre for Environment, Fisheries & Aquaculture Science, Suffolk, UK

K Dyer University of Plymouth, Plymouth, UK

K M Bailey Alaska Fisheries Science Center, Seattle, WA, USA

M Elliott Institute of Estuarine and Coastal Studies, University of Hull, Hull, UK

J G Baldauf Texas A&M University, College Station, TX, USA

D M Farmer Institute of Ocean Sciences, Sidney, BC, Canada

J Bascompte CSIC, Seville, Spain

A V Fedorov Yale University, New Haven, CT, USA

A Belgrano Institute of Marine Research, Lysekil, Sweden

M J Fogarty Northeast Fisheries Science Center, National Marine Fisheries Service, Woods Hole, MA, USA

O A Bergstad Institute of Marine Research, Flødevigen His, Norway J H S Blaxter Scottish Association for Marine Science, Argyll, UK

R Fonteyne Agricultural Research Centre, Ghent, Oostende, Belgium

Q Bone The Marine Biological Association of the United Kingdom, Plymouth, UK

D J Fornari Woods Hole Oceanographic Institution, Woods Hole, USA

I Boyd University of St. Andrews, St. Andrews, UK

A E Gargett Old Dominion University, Norfolk, VA, USA

K M Brander DTU Aqua, Charlottenlund, Denmark and International Council for the Exploration of the Sea (ICES), Copenhagen, Denmark

C H Gibson University of California, San Diego, La Jolla, CA, USA

J N Brown Yale University, New Haven, CT, USA T K Chereskin University of California San Diego, La Jolla, CA, USA J S Collie Danish Institute for Fisheries Research, Charlottenlund, Denmark and University of Rhode Island, Narragansett, RI, USA G Cresswell CSIRO Marine Research, Tasmania, Australia

J D M Gordon Scottish Association for Marine Science, Argyll, UK J F Grassle Rutgers University, New Brunswick, New Jersey, USA S J Hall Flinders University, Adelaide, SA, Australia N Hanson University of St. Andrews, St. Andrews, UK P J B Hart University of Leicester, Leicester, UK

J Davenport University College Cork, Cork, Ireland

K R Helfrich Woods Hole Oceanographic Institution, Woods Hole, MA, USA

R H Douglas City University, London, UK

D M Higgs University of Windsor, Windsor, ON, Canada

S Draxler Karl-Franzens-Universita¨t Graz, Graz, Austria

N G Hogg Woods Hole Oceanographic Institution, Woods Hole, MA, USA

J T Duffy-Anderson Alaska Fisheries Science Center, Seattle, WA, USA J A Dunne Santa Fe Institute, Santa Fe, NM, USA and Pacific Ecoinformatics and Computational Ecology Lab, Berkely, CA, USA

E D Houde University of Maryland, Solomons, MD, USA V N de Jonge Department of Marine Biology, Groningen University, Haren, The Netherlands


Contributors K Katsaros Atlantic Oceanographic and Meteorological Laboratory, NOAA, Miami, FL, USA J M Klymak University of Victoria, Victoria, BC, Canada M Kucera Eberhard Karls Universita¨t Tu¨bingen, Tu¨bingen, Germany R S Lampitt University of Southampton, Southampton, UK J R N Lazier Bedford Institute of Oceanography, NS, Canada J R Ledwell Woods Hole Oceanographic Institution, Woods Hole, MA, USA P F J Lermusiaux Harvard University, Cambridge, MA, USA M E Lippitsch Karl-Franzens-Universita¨t Graz, Graz, Austria

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T J Pitcher University of British Columbia, Vancouver, Canada A N Popper University of Maryland, College Park, MD, USA J F Price Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA R D Prien Southampton Oceanography Centre, Southampton, UK A-L Reysenbach Portland State University, Portland, OR, USA P L Richardson Woods Hole Oceanographic Institution, Woods Hole, MA, USA A R Robinson Harvard University, Cambridge, MA, USA M D J Sayer Dunstaffnage Marine Laboratory, Oban, Argyll, UK

B J McCay Rutgers University, New Brunswick, NJ, USA

R W Schmitt Woods Hole Oceanographic Institution, Woods Hole, MA, USA

J D McCleave University of Maine, Orono, ME, USA

J Scott DERA Winfrith, Dorchester, Dorset, UK

D Minchin Marine Organism Investigations, Killaloe, Republic of Ireland

M P Sissenwine Northeast Fisheries Science Center, Woods Hole, MA, USA

C M Moore University of Essex, Colchester, UK K Moran University of Rhode Island, Narragansett, RI, USA G R Munro University of British Columbia, Vancouver, BC, Canada J D Nash Oregon State University, Corvallis, Oregon, OR, USA A C Naveira Garabato University of Southampton, Southampton, UK

T P Smith Northeast Fisheries Science Center, Woods Hole, MA, USA P V R Snelgrove Memorial University of Newfoundland, St John’s, NL, Canada M A Spall Woods Hole Oceanographic Institution, Woods Hole, MA, USA A Stigebrandt University of Gothenburg, Gothenburg, Sweden D A V Stow University of Southampton, Southampton, UK

J D Neilson Department of Fisheries and Oceans, New Brunswick, Canada

D J Suggett University of Essex, Colchester, UK

Y Nozakiw University of Tokyo, Tokyo, Japan

U R Sumaila University of British Columbia, Vancouver, BC, Canada

R I Perry Department of Fisheries and Oceans, British Columbia, Canada S G Philander Princeton University, Princeton, NJ, USA w

Deceased.

K S Tande Norwegian College of Fishery Science, Tromsø, Norway S A Thorpe National Oceanography Centre, Southampton, UK R S J Tol Economic and Social Research Institute, Dublin, Republic of Ireland


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Contributors

K E Trenberth National Center for Atmospheric Research, Boulder, CO, USA J J Videler Groningen University, Haren, The Netherlands

R S Wells Chicago Zoological Society, Sarasota, FL, USA D C Wilson Institute for Fisheries Management and Coastal Community Development, Hirtshals, Denmark

Volume 3 S Ali Plymouth Marine Laboratory, Plymouth, UK

K H Coale Moss Landing Marine Laboratories, CA, USA

J T Andrews University of Colorado, Boulder, CO, USA

M F Coffins University of Texas at Austin, Austin, TX, USA

M A de Angelis Humboldt State University, Arcata, CA, USA

P J Corkeron James Cook University, Townsville, Australia

A J Arp Romberg Tiburon Center for Environment Studies, Tiburon, CA, USA

B C Coull University of South Carolina, Columbia, SC, USA

T Askew Harbor Branch Oceanographic Institute, Ft Pierce, FL, USA

R Cowen University of Miami, Miami, FL, USA

R D Ballard Institute for Exploration, Mystic, CT, USA

G Cresswell CSIRO Marine and Atmospheric Research, Hobart, TAS, Australia

G Barnabe´ Universite´ de Montpellier II, France

D S Cronan Royal School of Mines, London, UK

R S K Barnes University of Cambridge, Cambridge, UK

J Csirke Food and Agriculture Organization of the United Nations, Rome, Italy

E D Barton University of Wales, Bangor, Menai Bridge, Anglesey, UK

G A Cutter Old Dominion University, Norfolk, VA, USA

D Bhattacharya University of Iowa, Iowa City, IA, USA

D J DeMaster North Carolina State University, Raleigh, NC, USA

F von Blanckenburg Universita¨t Bern, Bern, Switzerland

T D Dickey University of California, Santa Barbara, CA, USA

D R Bohnenstiehl North Carolina State University, Raleigh, NC, USA

D Diemand Coriolis, Shoreham, VT, USA

H L Bryden University of Southampton, Southampton, UK J Burger Rutgers University, Piscataway, NJ, USA S M Carbotte Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA G T Chandler University of South Carolina, Columbia, SC, USA M A Charette Woods Hole Oceanographic Institution, Woods Hole, MA, USA

C S M Doake British Antarctic Survey, Cambridge, UK C M Domingues CSIRO Marine and Atmospheric Research, Hobart, TAS, Australia C J Donlon Space Applications Institute, Ispra, Italy F Doumenge Museé Oceánographique de Monaco, Monaco R A Dunn University of Hawaii at Manoa, Honolulu, HI, USA


Contributors R P Dziak Oregon State University/National Oceanic and Atmospheric Administration, Hatfield Marine Science Center, Newport, OR, USA O Eldholm University of Oslo, Oslo, Norway

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S K Hooker University of St. Andrews, St. Andrews, UK H Hotta Japan Marine Science & Technology Center, Japan G R Ierley University of California San Diego, La Jolla, CA, USA

A E Ellis Marine Laboratory, Aberdeen, Scotland, UK C R Engle University of Arkansas at Pine Bluff, Pine Bluff, AR, USA C C Eriksen University of Washington, Seattle, WA, USA V Ettwein University College London, London, UK S Farrow Carnegie Mellon University, Pittsburgh, PA, USA M Fieux Universite´ Pierre et Marie Curie, Paris Cedex, France N Forteath Inspection Head Wharf, TAS, Australia J D Gage Scottish Association for Marine Science, Oban, UK S M Garcia Food and Agriculture Organization of the United Nations, Rome, Italy

G Ito University of Hawaii at Manoa, Honolulu, HI, USA J Jacoby Woods Hole Oceanographic Institution, Woods Hole, MA, USA M J Kaiser Bangor University, Bangor, UK A E S Kemp University of Southampton, Southampton Oceanography Centre, Southampton, UK W M Kemp University of Maryland Center for Environmental Science, Cambridge, MD, USA V S Kennedy University of Maryland, Cambridge, MD, USA P F Kingston Heriot-Watt University, Edinburgh, UK G L Kooyman University of California San Diego, CA, USA

C Garrett University of Victoria, VIC, Canada

W Krijgsman University of Utrecht, Utrecht, The Netherlands

R N Gibson Scottish Association for Marine Science, Argyll, Scotland

J B Kristoffersen University of Bergen, Bergen, Norway

M Gochfeld Environmental and Community Medicine, Piscataway, NJ, USA

K Lambeck Australian National University, Canberra, ACT, Australia

H O Halvorson University of Massachusetts Boston, Boston, MA, USA

R S Lampitt University of Southampton, Southampton, UK

B U Haq Vendome Court, Bethesda, MD, USA

M Landry University of Hawaii at Manoa, Department of Oceanography, Honolulu, HI, USA

G R Harbison Woods Hole Oceanographic Institution, Woods Hole, MA, USA

C G Langereis University of Utrecht, Utrecht, The Netherlands A Lascaratos University of Athens, Athens, Greece

R M Haymon University of California, CA, USA

S Leibovich Cornell University, Ithaca, NY, USA

D L Hebert University of Rhode Island, RI, USA J E Heyning The Natural History Museum of Los Angeles County, Los Angeles, CA, USA P Hoagland Woods Hole Oceanographic Institution, Woods Hole, MA, USA

W G Leslie Harvard University, Cambridge, MA, USA C Llewellyn Plymouth Marine Laboratory, Plymouth, UK R A Lutz Rutgers University, New Brunswick, NJ, USA


xx

Contributors

K C Macdonald Department of Geological Sciences and Marine Sciences Institute, University of California, Santa Barbara, CA, USA F T Mackenzie University of Hawaii, Honolulu, HI, USA L P Madin Woods Hole Oceanographic Institution, Woods Hole, MA, USA M Maslin University College London, London, UK G A Maul Florida Institute of Technology, Melbourne, FL, USA M McNutt MBARI, Moss Landing, CA, USA M G McPhee McPhee Research Company, Naches, WA, USA A D Mclntyre University of Aberdeen, Aberdeen, UK J Mienert University of Tromsø, Tromsø, Norway G E Millward University of Plymouth, Plymouth, UK H Momma Japan Marine Science & Technology Center, Japan J H Morison University of Washington, Seattle, WA, USA A E Mulligan Woods Hole Oceanographic Institution, Woods Hole, MA, USA

J E Petersen Oberlin College, Oberlin, OH, USA M Phillips Network of Aquaculture Centres in Asia-Pacific (NACA), Bangkok, Thailand B Qiu University of Hawaii at Manoa, Hawaii, USA F Quezada Biotechnology Center of Excellence Corporation, Waltham, MA, USA N N Rabalais Louisiana Universities Marine Consortium, Chauvin, LA, USA R D Ray NASA Goddard Space Flight Center, Greenbelt, MD, USA M R Reeve National Science Foundation, Arlington VA, USA R R Reeves Okapi Wildlife Associates, QC, Canada A Reyes-Prieto University of Iowa, Iowa City, IA, USA P B Rhines University of Washington,Seattle, WA, USA A R Robinson Harvard University, Cambridge, MA, USA H T Rossby University of Rhode Island, Kingston, RI, USA H M Rozwadowski Georgia Institute of Technology, Atlanta, Georgia, USA

W Munk University of California San Diego, La Jolla, CA, USA

A G V Salvanes University of Bergen, Bergen, Norway

E J Murphy British Antarctic Survey, Marine Life Sciences Division, Cambridge, UK

R Schlitzer Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany

P D Naidu National Institute of Oceanography, Dona Paula, India

M E Schumacher Woods Hole Oceanographic Institution, Woods Hole, MA, USA

N Niitsuma Shizuoka University, Shizuoka, Japan

M I Scranton State University of New York, Stony Brook, NY, USA

D B Olson University of Miami, Miami, FL, USA G-A Paffenho¨fer Skidaway Institute of Oceanography, Savannah, GA, USA C Paris University of Miami, Miami, FL, USA M R Perfit Department of Geological Sciences, University of Florida, Gainsville, FL, USA

K Sherman Narragansett Laboratory, Narragansett, RI, USA M D Spalding UNEP World Conservation Monitoring Centre and Cambridge Coastal Research Unit, Cambridge, UK J Sprintall University of California San Diego, La Jolla, CA, USA J H Steele Woods Hole Oceanographic Institution, MA, USA


Contributors C A Stein University of Illinois at Chicago, Chicago, IL, USA

S M Van Parijs Norwegian Polar Institute, Tromsø, Norway

C Stickley University College London, London, UK

L M Ver University of Hawaii, Honolulu, HI, USA

U R Sumaila University of British Columbia, Vancouver, BC, Canada

F J Vine University of East Anglia, Norwich, UK

S Takagawa Japan Marine Science & Technology Center, Japan

K L Von Damm University of New Hampshire, Durham, NH, USA

P K Taylor Southampton Oceanography Centre, Southampton, UK

R P Von Herzen Woods Hole Oceanographic Institution, Woods Hole, MA, USA

A Theocharis National Centre for Marine Research (NCMR), Hellinikon, Athens, Greece

xxi

D Wartzok Florida International University, Miami, FL, USA

P C Ticco Massachusetts Maritime Academy, Buzzards Bay, MA, USA R P Trask Woods Hole Oceanographic Institution, Woods Hole, MA, USA

W F Weeks Portland, OR, USA R A Weller Woods Hole Oceanographic Institution, Woods Hole, MA, USA

A W Trites University of British Columbia, British Columbia, Canada

J A Whitehead Woods Hole Oceanographic Institution, Woods Hole, MA, USA

A Turner University of Plymouth, Plymouth, UK

J C Wiltshire University of Hawaii, Manoa, Honolulu, HA, USA

P L Tyack Woods Hole Oceanographic Institution, Woods Hole, MA, USA

C Woodroffe University of Wollongong, Wollongong, NSW, Australia

G J C Underwood University of Essex, Colchester, UK

C Wunsch Massachusetts Institute of Technology, Cambridge, MA, USA

C L Van Dover The College of William and Mary, Williamsburg, VA, USA

H S Yoon University of Iowa, Iowa City, IA, USA

Volume 4 A Alldredge University of California, Santa Barbara, CA, USA D M Anderson Woods Hole Oceanographic Institution, Woods Hole, MA, USA O R Anderson Columbia University, Palisades, NY, USA

J M Bewers Bedford Institute of Oceanography, Dartmouth, NS, Canada N V Blough University of Maryland, College Park, MD, USA W Bonne

P G Baines CSIRO Atmospheric Research, Aspendale, VIC, Australia

Federal Public Service Health, Food Chain Safety and Environment, Brussels, Belgium

J M Baker Clock Cottage, Shrewsbury, UK

University of Cape Town, Cape Town, Republic of South Africa

J G Bellingham Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA

R D Brodeur

G M Branch

Northwest Fisheries Science Center, Newport, OR, USA


xxii

Contributors

H Burchard Baltic Sea Research Institute Warnemu¨nde, Warnemu¨nde, Germany P H Burkill Plymouth Marine Laboratory, West Hoe, Plymouth, UK Francois Carlotti C.N.R.S./Universite´ Bordeaux 1, Arachon, France K L Casciotti Woods Hole Oceanographic Institution, Woods Hole, MA, USA

J S Grayw University of Oslo, Oslo, Norway A G Grottoli University of Pennsylvania, Philadelphia, PA, USA N Gruber Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Switzerland K C Hamer University of Durham, Durham, UK D Hammond University of Southern California, Los Angeles, CA, USA

A Clarke British Antarctic Survey, Cambridge, UK

W W Hay Christian-Albrechts University, Kiel, Germany

M B Collins National Oceanography Centre, Southampton, UK

J W Heath Coastal Fisheries Institute, CCEER Louisiana State University, Baton Rouge, LA, USA

J J Cullen Department of Oceanography, Halifax, NS, Canada D H Cushing Lowestoft, Suffolk, UK

D Hedgecock University of Southern California, Los Angeles, CA, USA C Hemleben Tu¨bingen University, Tu¨bingen, Germany

K L Denman University of Victoria, Victoria, BC, Canada S C Doney Woods Hole Oceanographic Institution, Woods Hole, MA, USA

T D Herbert Brown University, Providence, RI, USA I Hewson University of California Santa Cruz, Santa Cruz, CA, USA

J F Dower University of British Columbia, Vancouver, BC, Canada

Richard Hey University of Hawaii at Manoa, Honolulu, HI, USA

K Dysthe University of Bergen, Bergen, Norway

P Hoagland Woods Hole Oceanographic Institution, Woods Hole, MA, USA

H N Edmonds University of Texas at Austin, Port Aransas, TX, USA

N Hoepffner Institute for Environment and Sustainability, Ispra, Italy

L Føyn Institute of Marine Research, Bergen, Norway

M Hood Intergovernmental Oceanographic Commission, Paris, France

J Fuhrman University of Southern California, Los Angeles, CA, USA

M J Howarth Proudman Oceanographic Laboratory, Wirral, UK

C P Gallienne Plymouth Marine Laboratory, West Hoe, Plymouth, UK

M Huber Purdue University, West Lafayette, IN, USA

E Garel CIACOMAR, Algarve University, Faro, Portugal

J W Hurrell National Center for Atmospheric Research, Boulder, CO, USA

D M Glover Woods Hole Oceanographic Institution, Woods Hole, MA, USA S L Goodbred Jr State University of New York, Stony Brook, NY, USA J D M Gordon Scottish Association for Marine Science, Oban, Argyll, UK

D R Jackett CSIRO Marine and Atmospheric Research, Hobart, TAS, Australia R A Jahnke Skidaway Institute of Oceanography, Savannah, GA, USA w

Deceased.


Contributors

xxiii

A Jarre University of Cape Town, Cape Town, South Africa

A F Michaels University of Southern California, Los Angeles, CA, USA

J Joseph La Jolla, CA, USA

J D Milliman College of William and Mary, Gloucester, VA, USA

D M Karl University of Hawaii at Manoa, Honolulu, HI, USA

C D Mobley Sequoia Scientific, Inc., WA, USA

K L Karsh Princeton University, Princeton, NJ, USA

M M Mullinw Scripps Institution of Oceanography, La Jolla, CA, USA

J Karstensen Universita¨t Kiel (IFM-GEOMAR), Kiel, Germany

P Mu¨ller University of Hawaii, Honolulu, HI, USA

R M Key Princeton University, Princeton, NJ, USA

L A Murray The Centre for Environment, Fisheries and Aquaculture Sciences, Lowestoft, UK

P D Killworth Southampton Oceanography Centre, Southampton, UK B Klinger Center for Ocean-Land-Atmosphere Studies (COLA), Calverton, MD, USA H E Krogstad NTNU, Trondheim, Norway I Laing Centre for Environment Fisheries and Aquaculture Science, Weymouth, UK

T Nagai Tokyo University of Marine Science and Technology, Tokyo, Japan K H Nisancioglu Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway Y Nozakiw University of Tokyo, Tokyo, Japan

G F Lane-Serff University of Manchester, Manchester, UK

K J Orians The University of British Columbia, Vancouver, BC, Canada

A Longhurst Place de I’Eglise, Cajarc, France

C A Paulson Oregon State University, Corvallis, OR, USA

R Lukas University of Hawaii at Manoa, Hawaii, USA

W G Pearcy Oregon State University, Corvallis, OR, USA

M Lynch University of California Santa Barbara, Santa Barbara, CA, USA

W S Pegau Oregon State University, Corvallis, OR, USA

M Macleod World Wildlife Fund, Washington, DC, USA E Maranõń University of Vigo, Vigo, Spain S Martin University of Washington, Seattle, WA, USA S M Masutani University of Hawaii at Manoa, Honolulu, HI, USA I N McCave University of Cambridge, Cambridge, UK T J McDougall CSIRO Marine and Atmospheric Research, Hobart, TAS, Australia C L Merrin The University of British Columbia, Vancouver, BC, Canada

T Platt Dalhousie University, NS, Canada J J Polovina National Marine Fisheries Service, Honolulu, HI, USA D Quadfasel Niels Bohr Institute, Copenhagen, Denmark J A Raven Biological Sciences, University of Dundee, Dundee, UK G E Ravizza Woods Hole Oceanographic Institution, Woods Hole, MA, USA A J Richardson University of Queensland, St. Lucia, QLD, Australia M Rubega University of Connecticut, Storrs, CT, USA w

Deceased.


xxiv

Contributors

K C Ruttenberg Woods Hole Oceanographic Institution, Woods Hole, MA, USA

K K Turekian Yale University, New Haven, CT, USA T Tyrrell University of Southampton, Southampton, UK

A G V Salvanes University of Bergen, Bergen, Norway

O Ulloa Universidad de Concepcioń, Concepcioń, Chile

S Sathyendranath Dalhousie University, NS, Canada

C M G Vivian The Centre for Environment, Fisheries and Aquaculture Sciences, Lowestoft, UK

R Schiebel Tu¨bingen University, Tu¨bingen, Germany F B Schwing NOAA Fisheries Service, Pacific Grove, CA, USA

J J Walsh University of South Florida, St. Petersburg, FL, USA

M P Seki National Marine Fisheries Service, Honolulu, HI, USA

R M Warwick Plymouth Marine Laboratory, Plymouth, UK

L J Shannon Marine and Coastal Management, Cape Town, South Africa

N C Wells Southampton Oceanography Centre, Southampton, UK

K Shepherd Institute of Ocean Sciences, Sidney, BC, Canada

J A Whitehead Woods Hole Oceanographic Institution, Woods Hole, MA, USA

D Siegel-Causey Harvard University, Cambridge MA, USA D M Sigman Princeton University, Princeton, NJ, USA A Soloviev Nova Southeastern University, FL, USA J H Steele Woods Hole Oceanographic Institution, MA, USA P K Takahashi University of Hawaii at Manoa, Honolulu, HI, USA L D Talley Scripps Institution of Oceanography, La Jolla, CA, USA E Thomas Yale University, New Haven, CT, USA J R Toggweiler NOAA, Princeton, NJ, USA

M Wilkinson Heriot-Watt University, Edinburgh, UK R G Williams University of Liverpool, Oceanography Laboratories, Liverpool, UK C A Wilson III Department of Oceanography and Coastal Sciences, and Coastal Fisheries Institute, CCEER Louisiana State University, Baton Rouge, LA, USA H Yamazaki Tokyo University of Marine Science and Technology, Tokyo, Japan B deYoung Memorial University, St. John’s, NL, Canada G Zibordi Institute for Environment and Sustainability, Ispra, Italy

Volume 5 D G Ainley H.T. Harvey Associates, San Jose CA, USA W Alpers University of Hamburg, Hamburg, Germany J R Apelw Global Ocean Associates, Silver Spring, MD, USA w

Deceased.

A B Baggeroer Massachusetts Institute of Technology, Cambridge, MA, USA L T Balance NOAA-NMFS, La Jolla, CA, USA R Batiza Ocean Sciences, National Science Foundation, VA, USA W H Berger Scripps Institution of Oceanography, La Jolla, CA, USA


Contributors J L Bodkin US Geological Survey, AK, USA

I Everson British Antarctic Survey Cambridge, UK

I L Boyd Natural Environment Research Council, Cambridge, UK

I Fer University of Bergen, Bergen, Norway

A C Brown University of Cape Town, Cape Town, Republic of South Africa

M Fieux Universite´-Pierre et Marie Curie, Paris, France

xxv

J Burger Rutgers University, Piscataway, NJ, USA

R A Flather Proudman Oceanographic Laboratory, Bidston Hill, Prenton, UK

C J Camphuysen Netherlands Institute for Sea Research, Texel, The Netherlands

G S Giese Woods Hole Oceanographic Institution, Woods Hole, MA, USA

D C Chapman Woods Hole Oceanographic Institution, Woods Hole, MA, USA

J M Gregory Hadley Centre, Berkshire, UK

R E Cheney Laboratory for Satellite Altimetry, Silver Spring, Maryland, USA T Chopin University of New Brunswick, Saint John, NB, Canada J A Church Antarctic CRC and CSIRO Marine Research, TAS, Australia J K Cochran State University of New York, Stony Brook, NY, USA P Collar Southampton Oceanography Centre, Southampton, UK R J Cuthbert University of Otago, Dunedin, New Zealand L S Davis University of Otago, Dunedin, New Zealand K L Denman University of Victoria, Victoria BC, Canada R P Dinsmore Woods Hole Oceanographic Institution, Woods Hole, MA, USA G J Divoky University of Alaska, Fairbanks, AK, USA

S M Griffies NOAA/GFDL, Princeton, NJ, USA G Griffiths Southampton Oceanography Centre, Southampton, UK A Harding University of California, San Diego, CA, USA W S Holbrook University of Wyoming, Laramie, WY, USA G L Hunt, Jr University of Washington, Seattle, WA, USA and University of California, Irvine, CA, USA P Hutchinson North Atlantic Salmon Conservation Organization, Edinburgh, UK K B Katsaros Atlantic Oceanographic and Meteorological Laboratory, NOAA, Miami, FL, USA H L Kite-Powell Woods Hole Oceanographic Institution, Woods Hole, MA, USA M A Kominz Western Michigan University, Kalamazoo, MI, USA

L M Dorman University of California, San Diego, La Jolla, CA, USA

R G Kope Northwest Fisheries Science Center, Seattle, WA, USA

J F Dower University of British Columbia, Vancouver, BC, Canada

G S E Lagerloef Earth and Space Research, Seattle, WA, USA

J B Edson Woods Hole Oceanographic Institution, Woods Hole, MA, USA

L M Lairdw Aberdeen University, Aberdeen, UK

T I Eglinton Woods Hole Oceanographic Institution, Woods Hole, MA, USA

M Leppa¨ranta University of Helsinki, Helsinki, Finland w

Deceased.


xxvi

Contributors

E J Lindstrom NASA Science Mission Directorate, Washington, DC, USA

C T Roman University of Rhode Island, Narragansett, RI, USA

A K Liu NASA Goddard Space Flight Center, Greenbelt, MD, USA

M Sawhney University of New Brunswick, Saint John, NB, Canada

C R McClain NASA Goddard Space Flight Center, Greenbelt, MD, USA

G Shanmugam The University of Texas at Arlington, Arlington, TX, USA

D J McGillicuddy Jr Woods Hole Oceanographic Institution, Woods Hole, MA, USA

J Sharples Proudman Oceanographic Laboratory, Liverpool, UK

W K Melville Scripps Institution of Oceanography, La Jolla CA, USA

J H Simpson Bangor University, Bangor, UK R K Smedbol Dalhousie University, Halifax, NS, Canada

D Mills Atlantic Salmon Trust, UK

L B Spear H.T. Harvey Associates, San Jose, CA, USA

P J Minnett University of Miami, Miami, FL, USA W A Montevecchi Memorial University of Newfoundland, NL, Canada W S Moore University of South Carolina, Columbia, SC, USA S J Morreale Cornell University, Ithaca, NY, USA K W Nicholls British Antarctic Survey, Cambridge, UK T J O’Shea Midcontinent Ecological Science Center, Fort Collins, CO, USA T E Osterkamp University of Alaska, Alaska, AK, USA F V Paladino Indiana-Purdue University at Fort Wayne, Fort Wayne, IN, USA C L Parkinson NASA Goddard Space Flight Center, Greenbelt, MD, USA A Pearson Woods Hole Oceanographic Institution, Woods Hole, MA, USA J T Potemra SOEST/IPRC, University of Hawaii, Honolulu, HI, USA J A Powell Florida Marine Research Institute, St Petersburg, FL, USA T Qu SOEST/IPRC, University of Hawaii, Honolulu, HI, USA

R L Stephenson St. Andrews Biological Station, St. Andrews, NB, Canada J M Teal Woods Hole Oceanographic Institution, Rochester, MA, USA K K Turekian Yale University, New Haven, CT, USA P Wadhams University of Cambridge, Cambridge, UK W F Weeks Portland, OR, USA G Wefer Universita¨t Bremen, Bremen, Germany W S Wilson NOAA/NESDIS, Silver Spring, MD, USA M Windsor, North Atlantic Salmon Conservation Organization, Edinburgh, UK S Y Wu NASA Goddard Space Flight Center, Greenbelt, MD, USA L Yu Woods Hole Oceanographic Institution, Woods Hole, MA, USA


Contributors

xxvii

Volume 6 A V Babanin Swinburne University of Technology, Melbourne, VIC, Australia

S E Humphris Woods Hole Oceanographic Institution, Woods Hole, MA, USA

R T Barber Duke University Marine Laboratory, Beaufort, NC, USA

W J Jenkins University of Southampton, Southampton, UK

J Bartram World Health Organization, Geneva, Switzerland

D R B Kraemer The Johns Hopkins University, Baltimore, MD, USA

A Beckmann Alfred-Wegener-Institut fu¨r Polar- und Meeresforschung, Bremerhaven, Germany M C Benfield Louisiana State University, Baton Rouge, LA, USA P S Bogden Maine State Planning Office, Augusta, ME, USA J A T Bye The University of Melbourne, Melbourne, VIC, Australia M F Cronin NOAA Pacific Marine Environmental Laboratory, Seattle, WA, USA A R J David Bere Alston, Devon, UK W Deuser Woods Hole Oceanographic Institution, Woods Hole, MA, USA J Donat Old Dominion University, Norfolk, VA, USA C Dryden Old Dominion University, Norfolk, VA, USA A Dufour United States Environmental Protection Agency, OH, USA C A Edwards University of Connecticut, Groton, CT, USA W J Emery University of Colorado, Boulder, CO, USA E Fahrbach Alfred-Wegener-Institut fu¨r Polar- und Meeresforschung, Bremerhaven, Germany

S Krishnaswami Physical Research Laboratory, Ahmedabad, India E L Kunze University of Washington, Seattle, WA, USA T E L Langford University of Southampton, Southampton, UK J R Ledwell Woods Hole Oceanographic Institution, Woods Hole, MA, USA P L-F Liu Cornell University, Ithaca, NY, USA M M R van der Loeff Alfred-Wegener-Institut fu¨r Polar und Meereforschung Bremerhaven, Germany R Lueck University of Victoria, Victoria, BC, Canada J E Lupton Hatfield Marine Science Center, Newport, OR, USA L P Madin Woods Hole Oceanographic Institution, Woods Hole, MA, USA M E McCormick The Johns Hopkins University, Baltimore, MD, USA M G McPhee McPhee Research Company, Naches, WA, USA J H Middleton The University of New South Wales, Sydney, NSW, Australia P J Minnett University of Miami, Miami, FL, USA E C Monahan University of Connecticut at Avery Point, Groton, CT, USA

A M Gorlov Northeastern University, Boston, Massachusetts, USA

C Moore WET Labs Inc., Philomath, OR, USA

I Helmond CSIRO Marine Research, TAS, Australia

J H Morison University of Washington, Seattle, WA, USA

R A Holman Oregon State University, Corvallis, OR, USA

J N Moum Oregon State University, Corvallis, OR, USA


xxviii

Contributors

N S Oakey Bedford Institute of Oceanography, Dartmouth, NS, Canada D T Pugh University of Southampton, Southampton, UK D L Rudnick University of California, San Diego, CA, USA H Salas CEPIS/HEP/Pan American Health Organization, Lima, Peru L K Shay University of Miami, Miami, FL, USA W D Smyth Oregon State University, Corvallis, OR, USA J Sprintall University of California San Diego, La Jolla, CA, USA

L St. Laurrent University of Victoria, Victoria, BC, Canada W G Sunda National Ocean Service, NOAA, Beaufort, NC, USA M Tomczak Flinders University of South Australia, Adelaide, SA, Australia A J Watson University of East Anglia, Norwich, UK P H Wiebe Woods Hole Oceanographic Institution, Woods Hole, MA, USA P F Worcester University of California at San Diego, La Jolla, CA, USA


Contents Volume 1 Abrupt Climate Change

S Rahmstorf

1

Absorbance Spectroscopy for Chemical Sensors Abyssal Currents

R Narayanaswamy, F Sevilla, III

W Zenk

Accretionary Prisms

15

J C Moore

31

Acoustic Measurement of Near-Bed Sediment Transport Processes Acoustic Noise

Acoustic Scintillation Thermography Acoustics In Marine Sediments

K G Foote

62

P A Rona, C D Jones

71

T Akal

75

P N Mikhalevsky

Acoustics, Deep Ocean

92

W A Kuperman

101

F B Jensen

112

Acoustics, Shallow Water R Chester

Agulhas Current

120

J R E Lutjeharms

Aircraft Remote Sensing

128

L W Harding Jr, W D Miller, R N Swift, C W Wright

Air–Sea Gas Exchange

38 52

Acoustic Scattering by Marine Organisms

Aeolian Inputs

P D Thorne, P S Bell

I Dyer

Acoustics, Arctic

7

B Ja¨hne

138 147

Air–Sea Transfer: Dimethyl Sulfide, COS, CS2, NH4, Non-Methane Hydrocarbons, Organo-Halogens J W Dacey, H J Zemmelink

157

Air–Sea Transfer: N2O, NO, CH4, CO

163

Alcidae

C S Law

T Gaston

171

Antarctic Circumpolar Current Antarctic Fishes

S R Rintoul

I Everson

191

Anthropogenic Trace Elements in the Ocean Antifouling Materials

E A Boyle

211

W Seaman, W J Lindberg

234

R A Duce

Atmospheric Transport and Deposition of Particulate Material to the Oceans R Arimoto Authigenic Deposits

226

S G Philander

Atmospheric Input of Pollutants

Baleen Whales

203

B Rudels

Atlantic Ocean Equatorial Currents

Bacterioplankton

195

D J Howell, S M Evans

Arctic Ocean Circulation Artificial Reefs

178

G M McMurtry H W Ducklow

J L Bannister

238 J M Prospero, 248 258 269 276


xxix

xxx

Contents

Baltic Sea Circulation Bathymetry

W Krauss

288

D Monahan

297

Beaches, Physical Processes Affecting Benguela Current

Benthic Foraminifera

316 D J Wildish

328

A J Gooday

Benthic Organisms Overview

336

P F Kingston

348

P L Tyack

357

Biogeochemical Data Assimilation

E E Hofmann, M A M Friedrichs

Biological Pump and Particle Fluxes Bioluminescence

Bioturbation

S Honjo

376

A Morel

385

D H Shull

Black Sea Circulation

395

G I Shapiro

Bottom Water Formation

401

A L Gordon

415

Brazil and Falklands (Malvinas) Currents

A R Piola, R P Matano

Breaking Waves and Near-Surface Turbulence

J Gemmrich

D K Woolf

Calcium Carbonates

L C Peterson

E D Barton

Carbon Dioxide (CO2) Cycle

467

T Takahashi

Cenozoic Climate – Oxygen Isotope Evidence Cenozoic Oceans – Carbon Cycle Models

J D Wright L François, Y Godde´ris

R A Fine W R Martin

J W Farrington

Coastal Zone Management

514

539 551 563

F E Werner, B O Blanton

Coastal Topography, Human Impact on Coastal Trapped Waves

502

531

H Chamley

Coastal Circulation Models

495

524

Chemical Processes in Estuarine Sediments

Coccolithophores

E E Adams, K Caldeira

P Boyle

Chlorinated Hydrocarbons

477 487

Carbon Sequestration via Direct Injection into the Ocean

Clay Mineralogy

455

C A Carlson, N R Bates, D A Hansell, D K Steinberg

CFCs in the Ocean

431

445

B M Hickey, T C Royer

Canary and Portugal Currents

Cephalopods

422

439

California and Alaska Currents

Carbon Cycle

364 371

P J Herring, E A Widder

Bio-Optical Models

Bubbles

305

L V Shannon

Benthic Boundary Layer Effects

Bioacoustics

A D Short

D M Bush, O H Pilkey, W J Neal

J M Huthnance D R Godschalk

T Tyrrell, J R Young


572 581 591 599 606

Contents

Cold-Water Coral Reefs Conservative Elements

J M Roberts

615

D W Dyrssen

626

Continuous Plankton Recorders Copepods

A John, P C Reid

R Harris

Coral Reefs

630 640

Coral Reef and Other Tropical Fisheries Coral Reef Fishes

xxxi

V Christensen, D Pauly

M A Hixon

655

J W McManus

660

Corals and Human Disturbance Cosmogenic Isotopes

N J Pilcher

671

D Lal

678

Coupled Sea Ice–Ocean Models Crustacean Fisheries

651

A Beckmann, G Birnbaum

688

J W Penn, N Caputi, R Melville-Smith

699

CTD (Conductivity, Temperature, Depth) Profiler Current Systems in the Atlantic Ocean Current Systems in the Indian Ocean

A J Williams, III

L Stramma M Fieux, G Reverdin

Current Systems in the Southern Ocean

A L Gordon

Current Systems in the Mediterranean Sea

P Malanotte-Rizzoli

708 718 728 735 744

Volume 2 Data Assimilation in Models Deep Convection

A R Robinson, P F J Lermusiaux

J R N Lazier

Deep Submergence, Science of

13 D J Fornari

22

K Moran

37

Deep-Sea Drilling Methodology Deep-Sea Drilling Results

1

J G Baldauf

45

Deep-Sea Fauna

P V R Snelgrove, J F Grassle

55

Deep-Sea Fishes

J D M Gordon

67

Deep-Sea Ridges, Microbiology

A-L Reysenbach

73

Deep-Sea Sediment Drifts

D A V Stow

80

Demersal Species Fisheries

K Brander

90

Determination of Past Sea Surface Temperatures Differential Diffusion

A E Gargett

Dispersion from Hydrothermal Vents Diversity of Marine Species Dolphins and Porpoises

R W Schmitt, J R Ledwell

K R Helfrich

P V R Snelgrove R S Wells

Double-Diffusive Convection

98 114

Dispersion and Diffusion in the Deep Ocean

Drifters and Floats

M Kucera

R W Schmitt

P L Richardson


122 130 139 149 162 171

xxxii

Contents

Dynamics of Exploited Marine Fish Populations East Australian Current

M J Fogarty

G Cresswell

179 187

Economics of Sea Level Rise

R S J Tol

197

Ecosystem Effects of Fishing

S J Hall

201

Eels

J D McCleave

208

Effects of Climate Change on Marine Mammals Ekman Transport and Pumping

T K Chereskin, J F Price

El Ninõ Southern Oscillation (ENSO)

Electrical Properties of Sea Water

Energetics of Ocean Mixing

228

S G Philander

R D Prien

Elemental Distribution: Overview

Y Nozaki

255

A C Naveira Garabato

261

Eutrophication

271

J M Klymak, J D Nash

Estuarine Circulation

288

K Dyer

299

V N de Jonge, M Elliott

Evaporation and Humidity

Fiord Circulation

306

K Katsaros

Exotic Species, Introduction of Expendable Sensors

241 247

w

A V Fedorov, J N Brown

Estimates of Mixing

218 222

K E Trenberth

El Ninõ Southern Oscillation (ENSO) Models

Equatorial Waves

I Boyd, N Hanson

324

D Minchin

332

J Scott

345

A Stigebrandt

353

Fiordic Ecosystems

K S Tande

359

Fish Ecophysiology

J Davenport

367

Fish Feeding and Foraging Fish Larvae

P J B Hart

E D Houde

Fish Locomotion

381

J J Videler

Fish Migration, Horizontal Fish Migration, Vertical

Fish Reproduction

Fish Vision

392

G P Arnold

402

J D Neilson, R I Perry

Fish Predation and Mortality

Fish Schooling

374

411

K M Bailey, J T Duffy-Anderson

J H S Blaxter

425

T J Pitcher

432

R H Douglas

445

Fish: Demersal Fish (Life Histories, Behavior, Adaptations) Fish: General Review

O A Bergstad

Q Bone

458 467

Fish: Hearing, Lateral Lines (Mechanisms, Role in Behavior, Adaptations to Life Underwater) A N Popper, D M Higgs w

417

Deceased.


476

Contents

Fisheries and Climate

K M Brander

Fisheries Economics

483

U R Sumaila, G R Munro

Fisheries Overview

491

M J Fogarty, J S Collie

Fisheries: Multispecies Dynamics Fishery Management

499

J S Collie

505

T P Smith, M P Sissenwine

Fishery Management, Human Dimension

513

D C Wilson, B J McCay

Fishery Manipulation through Stock Enhancement or Restoration Fishing Methods and Fishing Fleets Floc Layers

xxxiii

M D J Sayer

R Fonteyne

522 528 535

R S Lampitt

548

Florida Current, Gulf Stream, and Labrador Current Flow through Deep Ocean Passages Flows in Straits and Channels

P L Richardson

N G Hogg

554 564

D M Farmer

572

Fluid Dynamics, Introduction, and Laboratory Experiments

S A Thorpe

578

Fluorometry for Biological Sensing

D J Suggett, C M Moore

581

Fluorometry for Chemical Sensing

S Draxler, M E Lippitsch

589

Food Webs

A Belgrano, J A Dunne, J Bascompte

Forward Problem in Numerical Models Fossil Turbulence

596

M A Spall

604

C H Gibson

612

Volume 3 Gas Exchange in Estuaries

M I Scranton, M A de Angelis

Gelatinous Zooplankton

L P Madin, G R Harbison

General Circulation Models

Geomorphology

20

C G Langereis, W Krijgsman

C Woodroffe

Geophysical Heat Flow

C A Stein, R P Von Herzen

40 K Lambeck

C C Eriksen A D Mclntyre

Grabs for Shelf Benthic Sampling

67

P F Kingston

70

M McNutt

80

Groundwater Flow to the Coastal Ocean Habitat Modification

49 59

Global Marine Pollution

Gravity

25 33

Glacial Crustal Rebound, Sea Levels, and Shorelines Gliders

9

G R Ierley

Geomagnetic Polarity Timescale

1

A E Mulligan, M A Charette

M J Kaiser

Heat and Momentum Fluxes at the Sea Surface Heat Transport and Climate History of Ocean Sciences

88 99

P K Taylor

H L Bryden H M Rozwadowski


105 114 121

xxxiv

Contents

Holocene Climate Variability Hydrothermal Vent Biota

M Maslin, C Stickley, V Ettwein R A Lutz

125 133

Hydrothermal Vent Deposits

R M Haymon

144

Hydrothermal Vent Ecology

C L Van Dover

151

Hydrothermal Vent Fauna, Physiology of

A J Arp

159

Hydrothermal Vent Fluids, Chemistry of

K L Von Damm

164

Hypoxia

N N Rabalais

172

Icebergs

D Diemand

181

Ice-Induced Gouging of the Seafloor Ice–Ocean Interaction

W F Weeks

J H Morison, M G McPhee

191 198

Ice Shelf Stability

C S M Doake

209

Igneous Provinces

M F Coffins, O Eldholm

218

Indian Ocean Equatorial Currents Indonesian Throughflow

M Fieux

J Sprintall

237

Inherent Optical Properties and Irradiance Internal Tidal Mixing Internal Tides

T D Dickey

W Munk

258

C Garrett

266

International Organizations Intertidal Fishes

M R Reeve

274

R N Gibson

Intra-Americas Sea

244 254

R D Ray

Internal Waves

Intrusions

226

280

G A Maul

286

D L Hebert

295

Inverse Modeling of Tracers and Nutrients

R Schlitzer

300

Inverse Models

C Wunsch

312

IR Radiometers

C J Donlon

319

K H Coale

331

Iron Fertilization Island Wakes Krill

E D Barton

343

E J Murphy

349

Kuroshio and Oyashio Currents

B Qiu

Laboratory Studies of Turbulent Mixing Lagoons

358 J A Whitehead

R S K Barnes

Lagrangian Biological Models Land–Sea Global Transfers

377 D B Olson, C Paris, R Cowen F T Mackenzie, L M Ver

Langmuir Circulation and Instability Large Marine Ecosystems

S Leibovich

K Sherman

Laridae, Sternidae, and Rynchopidae Law of the Sea

371

389 394 404 413

J Burger, M Gochfeld

P Hoagland, J Jacoby, M E Schumacher (c) 2011 Elsevier Inc. All Rights Reserved.

420 432

Contents

Leeuwin Current

G Cresswell, C M Domingues

Long-Term Tracer Changes Macrobenthos Magnetics

444

F von Blanckenburg

455

J D Gage

467

F J Vine

478

Manganese Nodules Mangroves

xxxv

D S Cronan

488

M D Spalding

496

Manned Submersibles, Deep Water

H Hotta, H Momma, S Takagawa

Manned Submersibles, Shallow Water

T Askew

505 513

Mariculture Diseases and Health

A E Ellis

519

Mariculture of Aquarium Fishes

N Forteath

524

Mariculture of Mediterranean Species Mariculture Overview

G Barnabe´, F Doumenge

M Phillips

537

Mariculture, Economic and Social Impacts Marine Algal Genomics and Evolution Marine Biotechnology

532

C R Engle

545

A Reyes-Prieto, H S Yoon, D Bhattacharya

H O Halvorson, F Quezada

552 560

Marine Chemical and Medicine Resources

S Ali, C Llewellyn

567

Marine Fishery Resources, Global State of

J Csirke, S M Garcia

576

Marine Mammal Diving Physiology

G L Kooyman

Marine Mammal Evolution and Taxonomy

J E Heyning

Marine Mammal Migrations and Movement Patterns Marine Mammal Overview

582

P J Corkeron, S M Van Parijs

P L Tyack

Marine Mammal Trophic Levels and Interactions Marine Mammals and Ocean Noise

A W Trites

Marine Policy Overview Marine Protected Areas Marine Silica Cycle

635 643

654 G-A Paffenho¨fer

656

P Hoagland, P C Ticco

664

P Hoagland, U R Sumaila, S Farrow D J DeMaster

R S Lampitt

Maritime Archaeology

622

651

J H Steele

Marine Plankton Communities

Mediterranean Sea Circulation

672 678 686

R D Ballard

Meddies and Sub-Surface Eddies

Meiobenthos

S K Hooker

A E S Kemp

Marine Mesocosms

615

628

R R Reeves

Marine Mammals: Sperm Whales and Beaked Whales

Marine Snow

P L Tyack

D Wartzok

Marine Mammals, History of Exploitation

596 605

Marine Mammal Social Organization and Communication

Marine Mats

589

H T Rossby A R Robinson, W G Leslie, A Theocharis, A Lascaratos

B C Coull, G T Chandler (c) 2011 Elsevier Inc. All Rights Reserved.

695 702 710 726

xxxvi

Contents

Mesocosms: Enclosed Experimental Ecosystems in Ocean Science Mesopelagic Fishes

J E Petersen, W M Kemp

A G V Salvanes, J B Kristoffersen

Mesoscale Eddies

748

P B Rhines

Metal Pollution

755

G E Millward, A Turner

Metalloids and Oxyanions

732

768

G A Cutter

776

Methane Hydrates and Climatic Effects

B U Haq

784

Methane Hydrate and Submarine Slides

J Mienert

790

Microbial Loops

M Landry

Microphytobenthos

799

G J C Underwood

807

Mid-Ocean Ridge Geochemistry and Petrology Mid-Ocean Ridge Seismic Structure Mid-Ocean Ridge Seismicity

M R Perfit

815

S M Carbotte

826

D R Bohnenstiehl, R P Dziak

Mid-Ocean Ridge Tectonics, Volcanism, and Geomorphology

837 K C Macdonald

Mid-Ocean Ridges: Mantle Convection and Formation of the Lithosphere Millennial-Scale Climate Variability

J T Andrews

Mineral Extraction, Authigenic Minerals Molluskan Fisheries Monsoons, History of Moorings

G Ito, R A Dunn

852 867 881

J C Wiltshire

890

V S Kennedy

899

N Niitsuma, P D Naidu

910

R P Trask, R A Weller

919

Volume 4 Nekton

W G Pearcy, R D Brodeur

Nepheloid Layers

1

I N McCave

Network Analysis of Food Webs

8 J H Steele

Neutral Surfaces and the Equation of State Nitrogen Cycle

19 T J McDougall, D R Jackett

D M Karl, A F Michaels

Nitrogen Isotopes in the Ocean Noble Gases and the Cryosphere Non-Rotating Gravity Currents North Atlantic Oscillation (NAO) North Sea Circulation

25 32

D M Sigman, K L Karsh, K L Casciotti

40

M Hood

55

P G Baines

59

J W Hurrell

65

M J Howarth

73

Nuclear Fuel Reprocessing and Related Discharges

H N Edmonds

82

Ocean Biogeochemistry and Ecology, Modeling of

N Gruber, S C Doney

89

Ocean Carbon System, Modeling of Ocean Circulation

S C Doney, D M Glover

N C Wells

105 115

Ocean Circulation: Meridional Overturning Circulation

J R Toggweiler


126

Contents

Ocean Gyre Ecosystems

M P Seki, J J Polovina

Ocean Margin Sediments Ocean Ranching

132

S L Goodbred Jr

138

A G V Salvanes

146

R G Williams

156

Ocean Subduction

Ocean Thermal Energy Conversion (OTEC) Ocean Zoning

S M Masutani, P K Takahashi

M Macleod, M Lynch, P Hoagland

Offshore Sand and Gravel Mining Oil Pollution

Okhotsk Sea Circulation

E Garel, W Bonne, M B Collins

200

H Yamazaki, H Burchard, K Denman, T Nagai

Open Ocean Convection

A Soloviev, B Klinger

Open Ocean Fisheries for Deep-Water Species

Optical Particle Characterization

P H Burkill, C P Gallienne

265 272 274

R Lukas

287

E Thomas

295 W W Hay

Paleoceanography: Orbitally Tuned Timescales Paleoceanography: the Greenhouse World Particle Aggregation Dynamics Past Climate from Corals

T D Herbert

M Huber, E Thomas

A Alldredge

A G Grottoli

K L Denman, J F Dower

Pelagic Biogeography

A Longhurst

D H Cushing

Pelecaniformes

Peru–Chile Current System

C A Paulson, W S Pegau

J Karstensen, O Ulloa

319 330 338 348 356

379 385 393

K C Ruttenberg

Photochemical Processes

311

370

M Rubega

Phosphorus Cycle

303

364

D Siegel-Causey

Penetrating Shortwave Radiation

252 261

I Laing

Paleoceanography, Climate Models in

Phytobenthos

R A Jahnke

K K Turekian

Pacific Ocean Equatorial Currents

Phalaropes

243

G F Lane-Serff

Oysters – Shellfish Farming

Pelagic Fishes

234

K K Turekian

Oxygen Isotopes in the Ocean

Paleoceanography

226

J Joseph

Organic Carbon Cycling in Continental Margin Environments

Overflows and Cascades

208 218

J D M Gordon

Open Ocean Fisheries for Large Pelagic Species

Origin of the Oceans

182 191

L D Talley

One-Dimensional Models

167 174

J M Baker

Patch Dynamics

xxxvii

N V Blough

M Wilkinson

401 414 425


xxxviii

Contents

Phytoplankton Blooms

D M Anderson

Phytoplankton Size Structure Plankton

M M Mullin

Plankton and Climate Plankton Viruses

432

E Maranõń

445

w

453

A J Richardson

455

J Fuhrman, I Hewson

465

Platforms: Autonomous Underwater Vehicles Platforms: Benthic Flux Landers

J G Bellingham

R A Jahnke

485

Platinum Group Elements and their Isotopes in the Ocean

G E Ravizza

Plio-Pleistocene Glacial Cycles and Milankovitch Variability Polar Ecosystems

K H Nisancioglu

A Clarke

Pollution, Solids

494 504 514

C M G Vivian, L A Murray

Pollution: Approaches to Pollution Control Pollution: Effects on Marine Communities Polynyas

473

519

J S Grayw, J M Bewers R M Warwick

526 533

S Martin

540

Population Dynamics Models

Francois Carlotti

Population Genetics of Marine Organisms Pore Water Chemistry

546

D Hedgecock

556

D Hammond

Primary Production Distribution

563

S Sathyendranath, T Platt

572

Primary Production Methods

J J Cullen

578

Primary Production Processes

J A Raven

585

Procellariiformes

K C Hamer

590

Propagating Rifts and Microplates

Richard Hey

597

Protozoa, Planktonic Foraminifera

R Schiebel, C Hemleben

606

Protozoa, Radiolarians

O R Anderson

Radiative Transfer in the Ocean Radioactive Wastes Radiocarbon

C D Mobley

619

L Føyn

629

R M Key

637

Rare Earth Elements and their Isotopes in the Ocean Red Sea Circulation Redfield Ratio Refractory Metals

Y Nozaki

w

D Quadfasel

677

K J Orians, C L Merrin

Regime Shifts, Physical Forcing Regime Shifts: Methods of Analysis

653 666

T Tyrrell

Regime Shifts, Ecological Aspects

w

613

L J Shannon, A Jarre, F B Schwing F B Schwing B deYoung, A Jarre

Deceased.


687 699 709 717

Contents

Regional and Shelf Sea Models

J J Walsh

Remote Sensing of Coastal Waters

Rigs and offshore Structures River Inputs

722

N Hoepffner, G Zibordi

Remotely Operated Vehicles (ROVs)

xxxix

732

K Shepherd

742

C A Wilson III, J W Heath

748

J D Milliman

754

Rocky Shores

G M Branch

762

Rogue Waves

K Dysthe, H E Krogstad, P Mu¨ller

770

Rossby Waves

P D Killworth

Rotating Gravity Currents

781

J A Whitehead

790

Volume 5 Salmon Fisheries, Atlantic

P Hutchinson, M Windsor

Salmon Fisheries, Pacific Salmonid Farming Salmonids

1

R G Kope

L M Laird

12

w

23

D Mills

29

Salt Marsh Vegetation

C T Roman

Salt Marshes and Mud Flats Sandy Beaches, Biology of Satellite Altimetry

39

J M Teal

43

A C Brown

49

R E Cheney

58

Satellite Oceanography, History, and Introductory Concepts J R Apel w

W S Wilson, E J Lindstrom, 65

Satellite Passive-Microwave Measurements of Sea Ice

C L Parkinson

80

Satellite Remote Sensing of Sea Surface Temperatures

P J Minnett

91

Satellite Remote Sensing SAR

A K Liu, S Y Wu

Satellite Remote Sensing: Ocean Color

C R McClain

Satellite Remote Sensing: Salinity Measurements Science of Ocean Climate Models Sea Ice

103

G S E Lagerloef

S M Griffies

P Wadhams

114 127 133 141

Sea Ice Dynamics

M Leppa¨ranta

159

Sea Ice: Overview

W F Weeks

170

Sea Level Change

J A Church, J M Gregory

179

Sea Level Variations Over Geologic Time Sea Otters

w

M A Kominz

J L Bodkin

185 194

Deceased.


xl

Contents

Sea Surface Exchanges of Momentum, Heat, and Fresh Water Determined by Satellite Remote Sensing L Yu

202

Sea Turtles

212

F V Paladino, S J Morreale

Seabird Conservation

J Burger

Seabird Foraging Ecology Seabird Migration

220

L T Balance, D G Ainley, G L Hunt Jr

L B Spear

227 236

Seabird Population Dynamics

G L Hunt Jr

247

Seabird Reproductive Ecology

L S Davis, R J Cuthbert

251

Seabird Responses to Climate Change Seabirds and Fisheries Interactions

David G Ainley, G J Divoky C J Camphuysen

Seabirds as Indicators of Ocean Pollution Seabirds: An Overview Seals

265

W A Montevecchi

274

G L Hunt, Jr

279

I L Boyd

285

Seamounts and Off-Ridge Volcanism Seas of Southeast Asia

R Batiza

292

J T Potemra, T Qu

Seaweeds and their Mariculture Sediment Chronologies

305

T Chopin, M Sawhney

317

J K Cochran

327

Sedimentary Record, Reconstruction of Productivity from the Seiches

Seismic Structure

I Fer, W S Holbrook

L M Dorman

367

K B Katsaros

375

Sensors for Micrometeorological and Flux Measurements Shelf Sea and Shelf Slope Fronts

J B Edson

J Sharples, J H Simpson

H L Kite-Powell

Single Point Current Meters

T I Eglinton, A Pearson

P Collar, G Griffiths

436

Slides, Slumps, Debris Flows, and Turbidity Currents Small Pelagic Species Fisheries Small-Scale Patchiness, Models of

419 428

T J O’Shea, J A Powell G Shanmugam

R L Stephenson, R K Smedbol D J McGillicuddy Jr

Small-Scale Physical Processes and Plankton Biology

J F Dower, K L Denman

M Fieux

447 468 474 488 494

A B Baggeroer

Southern Ocean Fisheries

391

409

Single Compound Radiocarbon Measurements

Sonar Systems

382

401

R P Dinsmore

Somali Current

351 361

Sensors for Mean Meteorology

Shipping and Ports

333 344

A Harding

Seismology Sensors

Sirenians

G Wefer, W H Berger

D C Chapman, G S Giese

Seismic Reflection Methods for Study of the Water Column

Ships

257

504

I Everson


513

Contents

Sphenisciformes

L S Davis

520

Stable Carbon Isotope Variations in the Ocean Storm Surges

K K Turekian

529

R A Flather

530

Sub Ice-Shelf Circulation and Processes Submarine Groundwater Discharge Sub-Sea Permafrost Surface Films

xli

K W Nicholls

541

W S Moore

551

T E Osterkamp

559

W Alpers

570

Surface Gravity and Capillary Waves

W K Melville

573

Volume 6 Temporal Variability of Particle Flux Thermal Discharges and Pollution

W Deuser

1

T E L Langford

10

Three-Dimensional (3D) Turbulence Tidal Energy Tides

W D Smyth, J N Moum

A M Gorlov

26

D T Pugh

Tomography

32

P F Worcester

Topographic Eddies Towed Vehicles

40

J H Middleton

57

I Helmond

Trace Element Nutrients

65

W G Sunda

Tracer Release Experiments

75

A J Watson, J R Ledwell

Tracers of Ocean Productivity

Transmissometry and Nephelometry Tritium–Helium Dating

87

W J Jenkins

93

Transition Metals and Heavy Metal Speciation

Tsunami

18

J Donat, C Dryden

100

C Moore

109

W J Jenkins

119

P L-F Liu

127

Turbulence in the Benthic Boundary Layer Turbulence Sensors

R Lueck, L St. Laurrent, J N Moum

N S Oakey

Under-Ice Boundary Layer

148

M G McPhee, J H Morison

Upper Ocean Heat and Freshwater Budgets Upper Ocean Mean Horizontal Structure Upper Ocean Mixing Processes

155

P J Minnett

163

M Tomczak

175

J N Moum, W D Smyth

185

Upper Ocean Structure: Responses to Strong Atmospheric Forcing Events Upper Ocean Time and Space Variability Upper Ocean Vertical Structure Upwelling Ecosystems

141

L K Shay

192

D L Rudnick

211

J Sprintall, M F Cronin

217

R T Barber

Uranium-Thorium Decay Series in the Oceans: Overview

225 M M R van der Loeff


233

xlii

Contents

Uranium-Thorium Series Isotopes in Ocean Profiles Vehicles for Deep Sea Exploration

S E Humphris

Viral and Bacterial Contamination of Beaches Volcanic Helium

J Bartram, H Salas, A Dufour

285

Water Types and Water Masses

W J Emery

291

M E McCormick, D R B Kraemer

Waves on Beaches

267 277

E L Kunze

Wave Generation by Wind

244 255

J E Lupton

Vortical Modes

Wave Energy

S Krishnaswami

300

J A T Bye, A V Babanin

304

R A Holman

310

Weddell Sea Circulation

E Fahrbach, A Beckmann

318

Wet Chemical Analyzers

A R J David

326

Whitecaps and Foam

E C Monahan

Wind- and Buoyancy-Forced Upper Ocean Wind Driven Circulation

331 M F Cronin, J Sprintall

P S Bogden, C A Edwards

Zooplankton Sampling with Nets and Trawls

337 346

P H Wiebe, M C Benfield

355

Appendix 1. SI Units and Some Equivalences

373

Appendix 2. Useful Values

376

Appendix 3. Periodic Table of the Elements

377

Appendix 4. The Geologic Time Scale

378

Appendix 5. Properties of Seawater

379

Appendix 6. The Beaufort Wind Scale and Seastate

384

Appendix 7. Estimated Mean Oceanic Concentrations of the Elements

386

Appendix 8. Abbreviations

389

Appendix 9. Taxonomic Outline Of Marine Organisms

L P Madin

401

Appendix 10. Bathymetric Charts of the Oceans

412

Index

421


TEMPORAL VARIABILITY OF PARTICLE FLUX W. Deuser, Woods Hole Oceanographic Institution, Woods Hole, MA, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2925–2933, & 2001, Elsevier Ltd.

Ridge off Namibia, the Bransfield Strait, the Weddell Sea, the Arabian Sea, and the Bay of Bengal. No doubt, other parts of the ocean will be sampled in the future and, most likely, temporal variations in particle flux will be found.

Reality of ‘Temporal’ Flux Variations

Introduction Until the late 1960s the deep ocean was considered the least variable environment above the solid surface of the earth. It is always cold and dark, and no expressions of the diurnal or annual cycles shaping the subaerial environment were expected to penetrate the ocean beyond a depth of a few hundred meters. It was also believed that the fine particles constituting the bulk of deep-sea sediments took years to reach the seafloor. Then, in the 1970s and early 1980s, several lines of evidence contradicting this view emerged: what appeared to be annual varves were detected in the sediments of the Black Sea at a depth of more than 2000 m, and annual reproduction cycles and growth bands were reported for a few deep-benthic organisms. A plausible explanation for such periodicity in the deep sea was lacking, however, until time-series measurements by deep-ocean sediment traps at a depth of 3200 m in the Sargasso Sea revealed seasonal changes in the flux of particulate organic matter and, indeed, of all types of sedimenting particles. The variability of the deep flux could be attributed to changes in primary productivity in the euphotic zone about 1 month earlier. This finding demonstrated how unexpectedly rapid the transport of particles to the deep sea is and provided evidence for a seasonally variable food supply for deep-benthic organisms. Such variable food supply fosters uneven growth rates and cyclic reproduction.

Variations in the amount of sinking material per unit time recorded by stationary sediment traps in reality are convolutions of three components: (1) true temporal variations in the sinking flux, (2) spatial variations in the distribution of sinking particles moving past the trap site, and (3) variations in the retention efficiency of the traps caused by changes in trap tilt and ambient current speed. To some extent, the magnitudes of the second and third components can be reduced by the use of free-floating, neutrally buoyant traps, but the difficulties of deploying them at the desired depths and of tracking and recovering them have as yet prevented their widespread use. Further complications in interpreting apparent temporal flux variations recorded by stationary traps are introduced by the different sinking speeds of particles, typically ranging from 50 to 4500 m d 1. Rapidly sinking particles intercepted by traps carry signatures of conditions in overlying waters in a more recent time interval than do slowly sinking ones. This effect leads to both a mixing and ‘smearing’ of the real temporal variations of different particle classes prevailing during their departure intervals at or near the surface (Figure 1). In addition, apparentchanges in flux measured during successive sampling intervals of a given length (e. g. 15 d) in reality represent variations in near-surface conditions over significantly longer intervals (Figure 2). Nevertheless, some new and important insights into the inner workings of the ocean have been gained through the use of sediment traps.

Ubiquity of Temporal Flux Variations Time-series measurements of the sinking particle flux in many parts of the oceans and marginal seas have consistently revealed significant temporal variations. A partial listing includes the north-eastern, eastern, and central North Pacific, the Panama Basin, the Gulf of California, the Equatorial Pacific, the Greenland and Norwegian Seas, the north-eastern North Atlantic, Bay of Biscaye, Mediterranean, Sargasso Sea, the Atlantic off West Africa, the Caribbean Sea,the eastern Equatorial Atlantic, the Walvis

Causes of Flux Variability A variety of processes lead to variability in the flux of particulate matter to the seafloor. Variations in primary productivity in surface waters owing to seasonal (and shorter-term) changes in mixed-layer depth and attendant replenishment of nutrients in the euphotic zone are the primary cause of flux variations to the deep oceans of the temperate and subtropical latitudes. Intense storms, such as hurricanes, can also create localized flux pulses along their


1

2

TEMPORAL VARIABILITY OF PARTICLE FLUX

Overlapping departure intervals (76.5 d)

0

500

1500

50 m _1

d

Depth (m)

1000

2000 200 m

3200-m trap

_1

d

2500 1500-m trap 3000

500-m trap Collection interval (15 d)

3500

_ 80

_ 60

_ 20

_ 40

0

20

40

Time from cup opening (d)

Figure 1 Schematic illustration of the different time intervals of surface departure of particles with different sinking speeds sampled simultaneously by a deep-ocean sediment trap. (Reprinted from Deep-Sea Research I, 44, Siegel and Deuser, Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps. pp. 1519–1541, copyright (1997) with permission from Elsevier Science.)

tracks. Dust storms, as emanating from the western Sahara and reaching far across the Atlantic, for example, create pulses of lithogenic flux components in their wake. Iron, a growth-limiting micronutrient associated with the dust particles, can stimulate a primary production spike, especially of diatoms which, in turn, may boost the sinking flux of opaline silica and zooplankton fecal material and skeletal remains. The seasonal monsoons affecting the northern Indian Ocean have been shown to influence the primary production and flux of particles to the deep Arabian Sea and Bay of Bengal. Seasonal changes in flow of tropical rivers cause fluctuations in the supply of nutrients to their plumes and can cause seemingly erratic fluctuations in productivity and particle export as far as hundreds to thousands of kilometers from the river mouths. The annual cycle of waxing and waning ice cover on high-latitude oceans influences the amount of sinking material. Variations in upwelling intensity on seasonal

to multiannual timescales result in concomitant variations in sinking flux.

Timescales of Flux Variability Knowledge of timescales of flux variabilityis limited by the sampling schemes designed to intercept the sinking flux of particles. While in all likelihood particle fluxes vary on timescales as shortas minutes or less, the practicality of sampling measurable amounts in the deep ocean has placed a lower limit of daily sampling intervals on attempts to determine variability. Also, inasmuch as appropriate technology became available only in the late 1970s and only a few sites have been sampled for even a single decade, detection of true decadal variability thus far is restricted to decade-length trends only. There is no way of knowing whether such trends are parts of continuing unidirectional changes or parts of



3

Figure 2 Schematic of the overlapping, but widely different, time intervals which were sampled simultaneously by an array of three sediment traps at different depths on the same mooring. (Reprinted from Deep-Sea Research I, 44, Siegel and Deuser, Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps, pp. 1519–1541, copyright (1997) with permission from Elsevier Science.)

Figure 3 Example of daily mass flux differences at three depths in the Sargasso Sea. Note that the three traps sampled particles which departed the upper ocean at different times (compare with Figure 2).


4

TEMPORAL VARIABILITY OF PARTICLE FLUX Flux accumulation curves

Accumulated flux (% of total)

100

80

60

50% of total annual flux

40

20

0 0

50

100

150

200

250

300

350

Accumulated time (d)

Western Indian Ocean North-eastern Pacific North-eastern Atlantic Equatorial Pacific Figure 4 Yearly cumulative flux curves for four oceanic provinces. (Reprinted from Deep-Sea Research I, 44, from Lampitt and Antia, Particle flux in deep seas: regional characteristics and temporal variability, pp. 1377–1403, copyright (1997) with permission from Elsevier Science.)

long-term cycles. It is fair to say, however, that flux variability has been found wherever and whenever attempts were made to detect it. At least at one site in the Sargasso Sea it has been documented on timescales from diurnal to decadal. Evidence for even longer-term variability of sediment flux, on timescales of centuries and beyond, is found in the sedimentary record of agreat many geological periods. Diurnal

Diurnal flux variability is the rule in the upper ocean due to such causes as patchiness in the distribution of particle-producing organisms, eddies, day-to-day differences in solar heating, and wind speed. For technical and logistical reasons, there is less documentation of diurnal flux variability in the deep ocean. An example of this flux at three depths on the same mooring in the Sargasso Sea is shown in Figure 3. Even at a depth of 1500 m daily fluxes varied by a factor of two.

Monthly

There are hints of a lunar cycle (29.5 d). in some sediment trap records. Some organisms, such as planktonic foraminifera, have lunar reproduction cycles which ought to find an expression in the sinking flux of their skeletal parts. The difficulty in demonstrating lunar cyclicity in the flux intercepted by sediment traps lies in devising sampling schemes which avoid aliasing of the lunar period. With the widely employed monthly or semimonthly sampling frequency of traps this is not possible. Seasonal

The most widely detected temporal variation in the sinking flux is seasonal, i. e. an annual cycle. The practical reason for this is that the cycle of seasons fits best into the sampling schemes suitable to remote locations and into the funding schemes of agencies supporting oceanographic research. The fundamental



5

Figure 5 Average annual flux variation over a period of 18 years (1978–95) at 3200 m in the Sargasso Sea. The standard deviations around the semimonthly averages in the left panel give an indication of the interannual variability. The small panel at lower left indicates the number of years for which measurements were made at begining and middle of each month.

reason is that all parts of the ocean, including those at tropical latitudes, are subject to seasonal changes. Insolation, sea surface temperature, wind speed and direction, precipitation, and in some parts ice cover and surface currents, as well as human activities, all undergo seasonal changes. All of these factors have some bearing on biological productivity and/or detrital input into the surface ocean. Amplitudes of the annual cycle differ widely in different parts of the ocean. In the high latitudes, where the ocean is ice-free for only a short time, half of the annual particle flux may be delivered to deep water in a month or less. Annual cumulative flux curves for four different open-ocean regimes, calculated for a standard depth of 2000 m, are shown in Figure 4. In general, the lower the latitude, the less pronounced the annual cycle, but areas such as the Arabian Sea, which is strongly affected by the seasonal monsoons, deviate from this pattern.

The longest series of consistent measurements of the particle flux to the deep ocean is for a depth of 3200 m in the Sargasso Sea. The average annual cycle and its standard deviation over a period of 18 years for that site are shown in Figure 5. The cycle is quasisinusoidal, but there are several features worthy of note, as follows. (1) On average, even at the time of lowest flux, i. e. in the fall, the flux is still about half that at the time of maximum flux. (2) The greatest variance (a measure of interannual variability) occurs at the time of maximum flux, i. e. in the spring, followed by a secondary maximum in midsummer. Conversely, the lowest variance occurs at the time of lowest flux. (3) The transition from the autumnal flux minimum to the vernal flux maximum is not sudden, as might be expected based on a rather sudden spring bloom, but gradual. The reason for this is that there are actually a number of mixing events triggering minor blooms which increase in


TEMPORAL VARIABILITY OF PARTICLE FLUX Bimonthly flux anomalies at 3200 m

103

Flux anomalies (18 y)

102

Power density

7

101

100

Average annual flux cycle

_1

10

_2

10

_1

100

10

101

Frequency (1 per year) Figure 7 Power spectrum of the anomalies shown in Figure 6 (solid line) compared with that of the average annual flux cycle (dotted line). There are no significant peaks for periods longer than 1 year.

4.5 years, with the latter not being significant. This demonstrates clearly that patterns of multiannual variability can be detected only in sampling records of duration far exceeding those presently available.

column which, in turn, led to decreases in mixed-layer depth, nutrient supply to the euphotic zone, and primary production. Thus it appears that here, too, climatic change, whether of cyclic or unidirectional nature, affects the flux of particles to the deep ocean.

Decadal

True decadal variability is detectable only in time-series of several decades’ length, but trends may become apparent in shorter series. An example is a significant 14-year negative trend in the opal/calcium carbonate ratio in the sinking material in the Sargasso Sea (Figure 8). It appears that changes in the species assemblage of the silica-producing biota were responsible for the trend. But, while the trend parallels a significant trend of increasing wind speed in the Bermuda area, a causal connection between the two is not obvious. A 7-year trend of decreasing flux of particulate organic carbon was detected in the deep eastern North Pacific. In that case the trend was attributed to a longterm increase in the temperature of the upper water

Episodic

There is increasing evidence that episodic or ‘unusual’ events, i. e. events that fall outside the norm of commonly observed variability, can have significant and enduring effects on ecosystems and the sedimentary record. The question of what constitutes an unusual event, however, is not trivial. The evidence suggests that the frequency of such occurrences decreases, rather than increases, with increasing length of a series of observations or measurements. In other words, the lack of a long-term perspective causes the observer to attribute a deviation from the short-term norm to an unusual event although the longer-term norm may well encompass deviations of this magnitude. Even more difficult is the assignment of likely


8

TEMPORAL VARIABILITY OF PARTICLE FLUX Sargasso Sea, 3200 m

0.55

0.5 _

Slope of fit: _0.0105/yr 1 (significant at 99% confidence level)

n = 82

Opal/CaCO3 flux ratio

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1 1978

1980

1982

1984

1986

1988

1990

1992

Start of year Figure 8 The 14-year record of variations in the ratio of opaline silica to calcium carbonate in material sinking to the deep Sargasso Sea. The over all trend of a decrease in the ratio (dotted line) is probably due to changes in the species assemblage of opal-producing biota which,in turn, could be related to a subtle climatic trend over that period. (Reprinted from Deep-Sea Research I, 42, Deuser, Jickells,King and Commeau, Decadal and annual changes in biogenic opal and carbonate fluxes to the deep Sargasso Sea, pp. 1923– 1932, copyright (1995) with permission from Elsevier Science.)

causes to such events. There is a general lack of simultaneous, continuous monitoring of meteorological and oceanographic variables to identify chains of events which might trigger episodic peaks in the sinking flux. Even closely spaced snapshot measurements of those variables stand a good chance of missing the brief conditions initiating the chain. An example is the record of an event of very high coccolith flux in the Panama Basin. The flux during one of six bimonthly collection periods exceeded by orders of magnitude the flux observed during the other five. Most likely, the defining event was much shorter than 2 months, suggesting an even more pronounced transient. However, the lack of appropriate concurrent measurement series precluded the assignment of a likely cause to the event. It is hoped that with increasing recognition of the value of timeseries measurements and with the trend towards

developing instrumentation capable of long-term automated monitoring of meteorological and hydrographic variables it will become easier both to detect and identify the causes of unpredictable episodic events.

Conclusions Except near hydrothermal vents, the sinking flux of particles – whose formation ultimately depends on photosynthesis– provides the fuel for all life forms in the deep ocean. It is becoming increasingly clear that this ‘rain’ varies on all timescales up to decadal and beyond. The deep ocean is thus not in a steady state and its life forms are quite closely coupled to both gradual changes and sudden events near the surface.



See also Primary Production Distribution. Primary Production Methods. Primary Production Processes. River Inputs. Upper Ocean Mixing Processes. Upper Ocean Time and Space Variability.

Further Reading Berger WH and Wefer G (1990) Export production: seasonality and intermittency, and paleoceanographic implications. Palaeogeography, Palaeoclimatology and Palaeoecology 89: 245--254. Deuser WG and Ross EH (1980) Seasonal change in the flux of organic carbon to the deep Sargasso Sea. Nature 283: 364--365. Deuser WG (1996) Temporal variability of particle flux in the deep Sargasso Sea. In: Ittekkot V, Schafer P, Honjo

9

S, and Depetris PJ (eds.) Particle Flux in the Ocean. London: Wiley and Sons. Honjo S (1982) Seasonality and interaction of biogenic and lithogenic particulate flux at the Panama Basin. Science 218: 883--890. Lampitt RS and Antia AN (1997) Particle flux in deep seas: regional characteristics and temporal variability. DeepSea Research I 44: 1377--1403. Siegel DA and Deuser WG (1997) Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps. Deep-Sea Research I 44: 1519--1541. Smith KL jr and Kaufmann RS (1999) Long-term discrepancy between food supply and demand in the deep eastern North Pacific. Science 284: 1174--1177. Weatherhead PJ (1986) How unusual are unusual events? American Naturalist 128: 150--154.


THERMAL DISCHARGES AND POLLUTION T. E. L. Langford, University of Southampton, Southampton, UK Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2933–2940, & 2001, Elsevier Ltd.

Sources of Thermal Discharges The largest single source of heat to most water bodies, including the sea, is the sun. Natural thermal springs also occur in many parts of the world, almost all as fresh water, some of which discharge to the sea. In the deep oceans hydrothermal vents discharge mineral-rich hot water at temperatures greatly exceeding any natural temperatures either at depth or at the surface. To add to these natural sources of heat, industrial processes have discharged heated effluents into coastal waters in many parts of the world for at least 150 years. By far the largest volumes of these heated effluents reaching the sea in the past 60 years have originated from the electricity generation industry (power industry). Indeed more than 80% of the volume of heated effluents to the sea originate from the power industry compared with 3– 5% from the petroleum industries and up to 7% (in the USA) from chemical and steel industries. The process known as ‘thermal’ power generation, in which a fuel such as oil or coal or the process of nuclear fission is used to heat water to steam to drive turbines, requires large volumes of cooling water to remove the waste heat produced in the process. Where power stations are sited on or near the coast all of this waste heat, representing some 60–65% of that used in the process, is discharged to the sea. The heat is then dissipated through dilution, conduction, or convection. In a few, atypical coastal situations, where the receiving water does not have the capacity to dissipate the heat, artificial means of cooling the effluent such as ponds or cooling-towers are used. Here the effluent is cooled prior to discharge and much of the heat dissipated to the air. The waste heat is related to the theoretical thermal efficiency of the Rankine cycle, which is the modification of the Carnot thermodynamic cycle on which the process is based. This has a maximum theoretical efficiency of about 60% but because of environmental temperatures and material properties the practical efficiency is around 40%. Given this level of efficiency and the normal operating conditions of a modern coal- or oil-fired power station, namely

10

steam at 5501C and a pressure of 10.3 106 kg cm2 with corresponding heat rates of 2200 kg cal kWh1 of electricity, some 1400 kg cal kWh1 of heat is discharged to the environment, usually in cooling water at coastal sites. This assumes a natural water temperature of 101C. Nuclear power stations usually reject about 50% more heat per unit of electricity generated because they operate at lower temperatures and pressures. Since the 1920s efficiencies have increased from about 20% to 38–40% today with a corresponding reduction by up to 50% of the rate of heat loss. The massive expansion of the industry since the 1920s has, however, increased the total amounts of heat discharged to the sea. Thus for each conventional modern power station of 2000 MW capacity some 63 m3 s1 of cooling water is required to remove the heat. Modern developments such as the combined cycle gas turbine (CCGT) power stations with increased thermal efficiencies have reduced water requirements and heat loss further so that a 500 MW power station may require about 9–10 m3 s1 to remove the heat, a reduction of over 30% on conventional thermal stations.

Water Temperatures Natural sea surface temperatures vary widely both spatially and temporally throughout the world with overall ranges recorded from 21C to 301C in open oceans and 21C to 431C in coastal waters. Diurnal fluctuations at the sea surface are rarely more than 11C though records of up to 1.91C have been made in shallow seas. The highest temperatures have been recorded in sheltered tropical embayments where there is little exchange with open waters. Most thermal effluents are discharged into coastal waters and these are therefore most exposed to both physical and biological effects. In deeper waters vertical thermal stratification can often exceed 101C and a natural maximum difference of over 231C between surface and bottom has been recorded in some tropical waters. The temperatures of thermal discharges from power stations are typically 8–121C higher than the natural ambient water temperature though at some sites, particularly nuclear power stations, temperature rises can exceed 151C (Figure 1). Maximum discharge temperatures in some tropical coastal waters have reached 421C though 35–381C is more typical. There are seasonal and diurnal fluctuations


THERMAL DISCHARGES AND POLLUTION Tidal currents

20

Number of power units

18

11

Wind

Slackwater plume

16 14

Flood

12

Ebb

10 8 6

Coastline

(A)

4

Tidal currents

2 8 10

14

18

26

22

30

34

38

Wind

Slackwater plume

42

∆T, °F 5

7

9

11 13

15

17 19

Flood

21 23 25

∆T, °C

Ebb Coastline

Figure 1 Frequency distribution of designed temperature rises for thermal discharges in US power station cooling water systems. (Reproduced with permission from Langford, 1990.)

at many sites related to the natural seasonal temperature cycles and to the operating cycles of the power station.

(B) Figure 2 Movements of thermal plumes in tidal waters. (A) Offshore outfall; (B) onshore outfall. (Reproduced with permission from Langford, 1990.)

400

Thermal Plumes and Mixing Zones

40

Area within isotherm (ha)

Once discharged into the sea a typical thermal effluent will spread and form a three-dimensional layer with the temperature decreasing with distance from the outfall. The behavior and size of the plume will depend on the design and siting of the outfall, the tidal currents, the degree of exposure and the volume and temperature of the effluent itself. Very few effluents are discharged more than 1 km from the shore. Effects on the shore are, however, maximized by shoreline discharge (Figure 2). The concept of the mixing zone, usually in three components, near field, mid-field and far-field, related to the distance from the outfall, has mainly been used in determining legislative limits on the effluents. Most ecological studies have dealt with near and mid-field effects. The boundary of the mixing zone is, for most ecological limits, set where the water is at 0.51C above natural ambients though this tends to be an arbitrary limit and not based on ecological data. Mixing zones for coastal discharges can be highly variable in both temperature and area of effect (Figure 3). In addition to heat effects, thermal discharges also contain chemicals, mainly those used for the control of marine fouling in pipework and culverts. Chlorine compounds are the most common and because of its

Theoretical upper limit

4

0.4

0.1

7 1 2 3 4 5 0 6 Surface isotherm temperature minus intake temperature (°C) Figure 3 Relationship between surface-temperature elevation and surface area affected for nine different surveys at Moro Bay Power Plant, California. (Reproduced with permission from Langford, 1990.)

strong biocidal properties, the effects of chlorine are of primary concern in many coastal discharges, irrespective of the temperature. Measured chlorine


12

THERMAL DISCHARGES AND POLLUTION

residuals immediately after application vary between 0.5 and 10 mg l1 throughout the world but most are within the 0.5–1.0 mg l1 range. Because of the complex chemical reactions in sea water free chlorine residuals in discharges are usually a factor of 10 lower than the initial dosing rate. Even so, concentrations of chlorine compounds can exceed lethal limits for organisms at some sites.

environment. These effects can be defined briefly as follows.

Biological Effects of Temperature

•

The biological effects of temperature on marine and coastal organisms have been reviewed by a number of authors. Most animals and plants can survive ranges of temperature which are essentially genetically determined. The ultimate lethal temperature varies within poikilothermic groups but there is a trend of tolerance which is inversely related to the structural complexity of the organism (Table 1). Thus groups of microorganisms tend to contain species which have much higher tolerances than invertebrates, fish, or vascular plants. Because the life processes and survival of many organisms is so dependent on water temperature many species have developed physiological or behavioral strategies for optimizing temperature exposure and for survival in extremes of heat or cold. Examples are to be found among intertidal species and in polar fish. The effects of temperature on organisms can be classified mainly from experimental data as lethal, controlling, direct, and indirect and all are relevant to the effects of thermal discharges in the marine

Table 1 Upper temperature limits for aquatic organisms. Data from studies of geothermal watersa Group

Temperature (1C)

Animals Fish and other aquatic vertebrates Insects Ostracods (crustaceans) Protozoa

38 45–50 49–50 50

Plants Vascular plants Mosses Eukaryotic algae Fungi

45 50 56 60

Prokaryotic microorganisms Blue–green algae Photosynthetic bacteria Nonphotosynthetic bacteria a

70–73 70–73 499

Reproduced with permission from Langford (1990).

•

•

•

Lethal: high or low temperatures which will kill an organism within a finite time, usually less than its normal life span. The lethal temperature for any one organism depends on many factors within genetic limits. These include acclimatization, rate of change of temperature, physiological state (health) of the organism and any adaptive mechanisms. Controlling: temperatures below lethal temperatures which affect life processes, i.e., growth, oxygen consumption, digestive rates, or reproduction. There is a general trend for most organisms to show increases in metabolic activity with increasing temperature up to a threshold after which it declines sharply. Direct: temperatures causing behavioral responses such as avoidance or selection, movements, or migrations. Such effects have been amply demonstrated in experiments but for some work in situ the effects are not always clear. Indirect: where temperatures do not act directly but through another agent, for example poisons or oxygen levels or through effects on prey or predators. Temperature acts synergistically with toxic substances which can be important to its effects on chlorine toxicity in situ in thermal plumes. Where temperature immobilizes or kills prey animals they can become much more vulnerable to predation.

Biological Effects of Thermal Discharges The translation of data obtained from experimental studies to field sites is often not simple. The complexity of the natural environment can mask or exacerbate effects so that they bear little relation to experimental conditions and this has occurred in many studies of thermal discharges in situ. Further, factors other than that being studied may be responsible for the observed effects. Examples relevant to thermal discharges are discussed later in this article. Entrainment

The biological effects of any thermal discharge on marine organisms begin before the effluent is discharged. Cooling water abstracted from the sea usually contains many planktonic organisms notably bacteria, algae, small crustacea, and fish larvae. Within the power station cooling system these



organisms experience a sudden increase in temperature (10–201C, depending on the station) as they pass through the cooling condensers. They will also experience changes in pressure (1–2 atm) and be dosed with chlorine (0.5–5 mg l1) during this entrainment, with the effect that many organisms may be killed before they are discharged to the receiving water. Estimates for power stations in various countries have shown that if the ultimate temperatures are less than 231C the photosynthesis of entrained planktonic marine algae may increase, but at 27–281C a decrease of 20% was recorded. At 29–341C the rates decreased by 61–84% at one US power station. Only at temperatures exceeding 401C has total mortality been recorded. Concentrations of chlorine (total residual) of 1.0 mg l1 have been found to depress carbon fixation in entrained algae by over 90% irrespective of temperature (Figure 4). Diatoms were less affected than other groups and the effects in open coastal waters were less marked than in estuarine waters. Unless the dose was high enough to cause complete mortality many algae showed evidence of recovery.

1.2

Carbon fixation (discharge / intake)

1.0

0.8

0.6

13

The mean mortality rates for marine zooplankton entrained through cooling-water systems were shown to be less than 30% except in unusual cases where extreme temperatures and high chlorine doses caused 100% mortality. High mortalities can also occur where the entrained organisms are exposed to high temperatures in cooling ponds after discharge from the cooling system. In general, zooplankton do not suffer percentage mortalities as high as those of phytoplankton under typical cooling-water conditions. After passing through the cooling system at a US power station the dead or dying zooplankton were observed being eaten by large numbers of fish gathered at the outfall and hence passed into the food chain. There are few published observations of marine macro-invertebrates entrained in cooling-water systems though at a site in the UK, field experiments showed that specimens of the prawn Palaemonetes varians were killed by mechanical damage as they passed through a cooling system. Larval fish are probably most vulnerable to the effects of entrainment, mostly killed by the combination of mechanical, chemical, and temperature effects. Mortalities of ichthyoplankton have varied from 27 to 100% at sites in the US and UK. Many of these were, however, on estuaries or tidal reaches of rivers. At a coastal site in California the mortality rate increased from 10 to 100% as temperatures rose from 31 to 381C. Some 13% mortalities occurred with no heat, mainly as a result of pressure and abrasion in the system. The significance of 20% larval mortalities to the natural populations of flounders (Platichthyes americanus) calculated for a site on Long Island Sound indicated that the annual mortality was estimated at a factor of 0.01 which might cause a reduction of 9% of the adult population over 35 years provided the fish showed no compensatory reproductive or survival mechanism, or no immigration occurred.

0.4

Effects of Discharges in Receiving Waters 0.2

Algae

At some US power stations the metabolism of phytoplankton was found to be inhibited by chlorine as far as 200 m from the outfall. Also, intermittent 0 0.2 0.4 0.6 0.8 1.0 1.2 0 1.4 1.6 chlorination caused reductions of 80–90% of the _1 Residual chlorine concentration (mg l ) photosynthetic activity some 50 m from the outfall at a site on the Californian coast. From an assessment Figure 4 The effect of cooling water chlorination on carbon of the total entrainment and discharge effects, howfixation in marine phytoplankton. #, San Onofre; þ , Morgantown; , Hudson River; , Fawley; &, Dunkerque. ever, it was reported that where dilution was 300 (Reproduced with permission from Langford, 1990.) times per second the effect of even a 100% kill of


14


phytoplankton would not be detectable in the receiving water. In Southampton Water in the UK, mortality rates of 60% were estimated as causing about 1.2–3% reduction in the productivity of the tidal exchange volume where a power station used 6% of this for cooling. The main problem with most assessments is that replication of samples was typically low and estimates suggest that 88 samples would be needed from control and affected areas to detect a difference of 5% in productivity at a site, 22 samples for a 10% change and at least six for a 20% change. Such replication is rarely recorded in site studies. Temperatures of 35–361C killed shore algae at a coastal site in Florida, particularly Halimeda sp. and Penicillus spp. but factors other than temperature, most likely chlorine and scour, removed macro-algae at another tropical site. Blue–green algae were found where temperatures reached 401C intermittently and Enteromorpha sp. occurred where temperatures of 391C were recorded. In more temperate waters the algae Ascophyllum and Fucus were eliminated where temperatures reached 27–301C at an outfall but no data on chlorination were shown. Replacing the shoreline outfall with an offshore diffuser outfall (which increased dilution and cooling rates) allowed algae to recolonize and recover at a coastal power station in Maine (US). On the Californian coast one of the potentially most vulnerable algal systems, the kelps Macrocystis, were predicted to be badly affected by power station effluents, but data suggest that at one site only about 0.7 ha was affected near the outfall. The seagrass systems (Thalassia spp.) of the Florida coastal bays appeared to be affected markedly by the effluents from the Turkey Point power stations and a long series of studies indicated that within the þ 3 to þ 41C isotherm in the plume, seagrass cover declined by 50% over an area of 10–12 ha. However, the results from two sets of studies were unequivocal as to the effects of temperature. The data are outlined briefly in the following summary. First, the effluent was chlorinated. Second it contained high levels of copper and iron. Third, the main bare patch denuded of seagrass, according to some observations, may have been caused by the digging of the effluent canal. Although one set of data concluded that the threshold temperature for adverse effects on seagrass was þ 1.51C (summer 33–351C) a second series of observations noted that Thalassia persisted apparently unharmed in areas affected by the thermal discharge, though temperatures rarely exceed 351C. From an objective analysis, it would appear that the effects were caused mainly by a combination of thermal and chemical stresses.

Zooplankton and Microcrustacea

In Southampton Water in the UK, the barnacle Elminius modestus formed large colonies in culverts at the Marchwood power station and discharged large numbers of nauplii into the effluent stream increasing total zooplankton densities. Similar increases occurred where fouling mussels (Mytilus sp.) released veliger larvae into effluent streams. Data from 10 US coastal power stations were inconclusive about the effects of thermal discharges on zooplankton in receiving waters with some showing increases and others the reverse. Changes in community composition in some areas receiving thermal discharges were a result of the transport of species from littoral zones to offshore outfalls or vice versa. At Tampa Bay in Florida no living specimens of the benthic ostracod Haplocytheridea setipunctata were found when the temperature in the thermal plume exceeded 351C. Similarly the benthic ostracod Sarsiella zostericola was absent from the area of a power effluent channel in the UK experiencing the highest temperature range. Macro-invertebrates

As with other organisms there is no general pattern of change in invertebrate communities associated with thermal discharges to the sea which can be solely related to temperature. Some of the earliest studies in enclosed temperate saline waters in the UK showed that no species was consistently absent from areas affected by thermal plumes and the studies at Bradwell power station on the east coast showed no evidence of a decline in species richness over some 20 years though changes in methodology could have obscured changes in the fauna. No changes in bottom fauna were recorded at other sites affected by thermal plumes in both Europe and the US. The polychaete Heteromastus filiformis was found to be common to many of the thermal plume zones in several countries. In these temperate waters temperatures rarely exceeded 33–351C. In contrast, in tropical coastal waters data suggest that species of invertebrates are excluded by thermal plumes. For example in Florida, surveys showed that some 60 ha of the area affected by the Turkey Point thermal plume showed reduced diversity and abundance of benthos in summer, but there was marked recovery in winter. The 60 ha represented 0.0023% of the total bay area. A rise of 4–51C resulted in a dramatic reduction in the benthic community. Similarly, at Tampa Bay, 35 of the 104 indigenous invertebrate species were excluded from the thermal plume area. Removal of the vegetation was considered to be the primary cause of the loss of benthic



invertebrates. In an extreme tropical case few species of macro-invertebrates survived in a thermal effluent where temperatures reached 401C, 10 species occurred at the 371C isotherm and the number at the control site was 87. Scour may have caused the absence of species from some areas nearer the outfall (Figure 5). The effluent was chlorinated but no data on chlorine concentrations were published. Corals suffered severe mortalities at the Kahe power station in Hawaii but the bleaching of the colonies suggested that again chlorine was the primary cause of deaths, despite temperatures of up to 351C. It has been suggested that temperature increases of as little as 1– 21C could cause damage to tropical ecosystems but detailed scrutiny of the data indicate that it would be difficult to come to that conclusion from field data, especially where chlorination was used for antifouling. The changes in the invertebrate faunas of rocky shores in thermal effluents have been less well studied. Minimal changes were found on breakwaters in the paths of thermal plumes at two Californian sites. Any measurable effects were within 200–300 m of the outfalls. In contrast in southern France a chlorinated thermal discharge reduced the numbers of

100 C

90

C

80 2 3

Number of species

70

2 4

3 4

60 50

5

40

1

1

6

30

7

20

5

6

10 0

7 24

26

28

30

32

34

36

38

40

Temperature (°C) (from field measurements) Figure 5 Numbers of invertebrate species recorded at various temperatures, taken from two separate surveys ( , October; , winter) at Guyanilla Bay. C, control sampling; 1–7, effluent sampling sites. (Reproduced with permission from Langford, 1990.)

15

species on rocks near the outfall though 11 species of Hydroida were found in the path of the effluent. In most of the studies, chlorine would appear to be the primary cause of reductions in species and abundance though where temperatures exceeded 371C both factors were significant. There is some evidence that species showed advanced reproduction and growth in the thermal plumes areas of some power stations where neither temperature nor chlorine were sufficient to cause mortality. Also behavioral effects were demonstrated for invertebrates at a Texas coastal power station. Here, blue crabs (Callinectes sapidus) and shrimps (Penaeus aztecus and P. setiferus) avoided the highest temperatures (exceeding 381C) in the discharge canal but recolonized as temperatures fell below 351C. At another site in tropical waters, crabs (Pachygrapsus transversus) avoided the highest temperatures (and possibly chlorine) by climbing out of the water on to mangrove roots. Fish

There are few records of marine fish mortalities caused by temperature in thermal discharges except where fish are trapped in effluent canals. For example mortalities of Gulf menhaden (Brevoortia petronus), sea catfish (Arius felis) occurred in the canal of a Texas power station when temperatures reached 391C. Also a rapid rise of 151C killed menhaden (Alosa sp.) in the effluent canal of the Northport power station in the US. Avoidance behavior prevents mortalities where fish can move freely. The apparent attraction of many fish species to thermal discharges, widely reported, was originally associated with behavioral thermoregulation and the selection of preferred temperatures. Perhaps the best recorded example is the European seabass (Dicentrarchus labrax) found associated with cooling-water outfalls in Europe. Temperature selection is, however, not now believed to be the cause of the aggregations. Careful analysis and observations indicate clearly that the main cause of aggregation is the large amounts of food organisms discharged either dead or alive in the discharge. Millions of shrimps and small fish can be passed through into effluents and are readily consumed by the aggregated fish. Further where fish have gathered, usually in cooler weather, they remain active in the warmer water and are readily caught by anglers unlike the individuals outside the plume. This also gives the impression that there are more fish in the warmer water. Active tracking of fish has shown mainly short-term association with outfalls though some species have been


16

THERMAL DISCHARGES AND POLLUTION 360 320 (C)

Fork length (mm)

280

(B)

240 200 160

Kingsnorth × and SD (measured) (heated canal) Irish Sea (median) Kingsnorth (back-calculated) Fawley (measured) Severn (measured) Morocco (measured)

120 80 40 0

0

1

2

3

4

Age, at December (observed) Figure 6 Growth of bass (Dicentrarchus labrax) in a thermal discharge canal in comparison to other locations. Note that back calculated lengths are smaller because they probably relate to cold water growth. (Reproduced with permission from Langford, 1990.)

shown as entering water at temperatures above their lethal maximum for very short periods to collect food. There is clear evidence, however, that fish avoid adversely high temperatures for most of the time and will return to a discharge area once the temperatures cool. Avoidance behavior is also apparent at high chlorine concentrations. Where water temperatures and chemical conditions allow consistent residence, fish in thermal discharge canals show increased growth (Figure 6). At the Kingsnorth power station in the UK seabass (D. labrax) grew at twice the rate as in cold water, particularly in the first year. Fish showed varying residence times and sequential colonization of the canal at various ages. Winter growth occurred and the scales of older fish with long-term association with the discharge showed no evidence of annual winter growth checks. The fish were able to move into and out of the canal freely.

Occurrence of Exotic Species Exotic or introduced marine invertebrate species have been recorded from thermal discharges in various parts of the world. Some of the earliest were from the enclosed docks heated by power station effluents near Swansea and Shoreham in the UK. The exotic barnacle Balanus amphitrite var. denticulata

and the woodborer Limnoria tripunctata replaced the indigenous species in the heated areas but declined in abundance when the effluent ceased. The polyzoan Bugula neritina originally a favored immigrant species in the heated water disappeared completely as the waters cooled. In New Jersey (USA) subtropical species of Teredo bred in a heated effluent and both the ascidian Styela clavata and the copepod Acartia tonsa have both been regarded as immigrant species favored by heated effluents. However many immigrant species have survived in temperate waters without being associated with heated waters. In UK waters, only the crab Brachynotus sexdentatus and the barnacle B. amphitrite may be regarded as the only species truly associated with thermal discharges despite records of various other species. The decline of the American hard shell clam fishery (Mercenaria mercenaria) introduced to Southampton Water in the UK was reportedly caused by the closure of the Marchwood power station combined with overfishing. Recruitment of young clams and their early growth were maximized in the heated water and reproduction was advanced but all declined as the thermal discharge ceased.

Aquaculture in Marine Thermal Discharges The use of marine thermal discharges from power stations for aquaculture has not been highly successful in most parts of the world. Although it is clear that some species will grow faster in warmer water, the presence and unpredictability of chlorination has been a major obstacle. It is not generally economically viable to allow a large power station to become fouled such that efficiency declines merely to allow fish to grow. In Japan some farming ventures are regarded as profitable at power stations but in Europe and the USA such schemes are rarely profitable. At the Hunterston power station in Scotland plaice (Pleuronectes platessa) and halibut (Hippoglossus hippoglossus) grew almost twice as fast in warm water as in natural ambient but the costs of pump maintenance and capital equipment caused the system to be uneconomic irrespective of chlorination problems. Optimization of temperature is also a problem especially where temperatures fluctuate diurnally or where they exceed optimal growth temperatures. The general conclusion is that commercial uses of heated effluents in marine systems are not yet proven and are unlikely to become large-scale global ventures.



Thermal Discharges and Future Developments The closure of many older, less-efficient power stations, has led to an increase in the efficiency of the use of water and a decline in the discharge of heat to the sea per unit of electricity generated. However, the increasing industrialization of the developing countries, China, Malaysia, India and the African countries is leading to the construction of new, large power stations in areas not previously developed. The widespread use of CCGT stations can reduce the localized problems of heat loss and water use further as well as reducing emissions of carbon dioxide and sulphur dioxide to the air but the overall increase in power demand and generation will lead to an increase in the total aerial and aquatic emissions in some regions. In some tropical countries the delicate coastal ecosystems will be vulnerable not only to heat and higher temperature but much more importantly to the biocides used for antifouling. There is as yet no practical alternative that is as economic as chlorine though different methods have been tried with varying success in some parts of the world. There is little doubt that the same problems will be recognized in the areas of new development but as in the past after they have occurred.

17

that are more tolerant or less exposed. Constraints should therefore be tailored to each specific site and ecosystem. Irrespective of temperature it is also very clear that chlorination or other biocidal treatment has been responsible for many of the adverse ecological effects originally associated with temperature. The solution to fouling control and the reduction of chlorination of other antifouling chemicals is therefore probably more important than reducing heat loss and discharge temperatures particularly where vulnerable marine ecosystems are at risk.

See also Deep-Sea Ridges, Microbiology. Demersal Species Fisheries. Dispersion from Hydrothermal Vents. Fish Ecophysiology. Geophysical Heat Flow. Heat and Momentum Fluxes at the Sea Surface. Hydrothermal Vent Biota. Hydrothermal Vent Deposits. Hydrothermal Vent Ecology. Hydrothermal Vent Fluids, Chemistry of. Mesopelagic Fishes. Ocean Thermal Energy Conversion (OTEC). Pelagic Fishes. Satellite Remote Sensing of Sea Surface Temperatures. Upper Ocean Heat and Freshwater Budgets. Upper Ocean Mixing Processes. Wind- and BuoyancyForced Upper Ocean.

Conclusions It is clear that the problems of the discharge of heated effluents are essentially local and depend on many factors. Although temperatures of over 371C are lethal to many species which cannot avoid exposure, there are species which can tolerate such temperatures for short periods. Indeed it can be concluded for open coastal waters that discharge temperatures may exceed the lethal limits of mobile species at least for short periods. This, of course, would not apply if vulnerable sessile species were involved, though again some provisos may be acceptable. For example, an effluent which stratified at the surface in deep water would be unlikely to affect the benthos. On the other hand an effluent which impinges on the shore may need strict controls to protect the benthic community. From all the data it is clear that blanket temperature criteria intended to cover all situations would not protect the most vulnerable ecosystems and may be too harsh for those

Further Reading Barnett PRO and Hardy BLS (1984) Thermal deformations. In: Kinne O (ed.) Marine Ecology, vol. V. Ocean Management, part 4, Pollution and Protection of the Seas, Pesticides, Domestic Wastes and Thermal Deformations, pp. 1769–1926. New York: Wiley. Jenner HA, Whitehouse JW, Taylor CJL and Khalanski M (1998) Cooling Water Management in European Power Stations: Biology and Control of Fouling. Hydroecologie Appliquee. Electricite´ de France. Kinne O (1970) Marine Ecology, vol. 1, Environmental Factors, part 1. New York: Wiley-Interscience. Langford TE (1983) Electricity Generation and the Ecology of Natural Waters. Liverpool: Liverpool University Press. Langford TE (1990) Ecological Effects of Thermal Discharges. London: Elsevier Applied Science. Newell RC (1970) The Biology of Intertidal Animals. London: Logos Press.


THREE-DIMENSIONAL (3D) TURBULENCE

Introduction

Figure 2 illustrates the main physical mechanisms that drive turbulence at the smallest scales. The description is presented in terms of strain and vorticity, quantities that represent the tendency of the flow at any point to deform and to rotate fluid parcels, respectively. A major and recent insight is that vorticity and strain are not distributed randomly in turbulent flow, but rather are concentrated into coherent regions, each of which is dominated by one type of motion or the other. The first mechanism we consider is vortex rollup due toshear instability. This process

n

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The Mechanics of Turbulence

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This article describes fluid turbulence withapplication to the Earth’s oceans. We begin with the simple, classical picture of stationary, homogeneous, isotropic turbulence. We then discuss departures from this idealized state that occur in small-scale geophysical flows. The discussion closes with a tour of some of the many physical regimes in which ocean turbulence has been observed. Turbulent flow has been a source of fascination for centuries. The term ‘turbulence’ appears to have been used first in reference to fluid flows by da Vinci, who studied the phenomenon extensively. Today, turbulence is frequently characterized as the last great unsolved problem of classical physics. It plays a central role in both engineering and geophysical fluid flows. Its study led to the discovery of the first strange attractor by Lorenz in 1963, and thus to the modern science of chaotic dynamics. In the past few decades, tremendous insight into the physics of turbulence has been gained through theoretical and laboratory study, geophysical observations, improved experimental techniques, and computer simulations. Turbulence results from the nonlinear nature of advection, which enables interaction between motions on different spatial scales. Consequently, an initial disturbance with a given characteristic length scale tends to spread to progressively larger and smaller scales. This expansion of the spectral range is limited at large scales by boundaries and/or body forces, and at small scales by viscosity. If the range of scales becomes sufficiently large, the flow takes a highly complex form whose details defy prediction. The roles played by turbulence in the atmosphere and oceans can be classified into two categories: momentum transport and scalar mixing. In transporting momentum, turbulent motions behave in a manner roughly analogous to molecular viscosity, reducing differences in velocity between different regions of a flow. For example, winds transfer momentum to the Earth via strong turbulence in the planetary boundary layer (a kilometer-thick layer adjacent to the ground) and are thus decelerated.

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This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2947–2955, & 2001, Elsevier Ltd.

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Copyright & 2001 Elsevier Ltd.

Scalar mixing refers to the homogenization of fluid properties such as temperature by random molecular motions. Molecular mixing rates are proportional to spatial gradients, which are greatly amplified due to the stretching and kneading (i.e. stirring) of fluid parcels by turbulence. This process is illustrated in Figure 1, which shows the evolution of an initially circular region of dyed fluid in a numerical simulation. Under the action of molecular mixing (or diffusion) alone, an annular region of intermediate shade gradually expands as the dyed fluid mixes with the surrounding fluid. If the flow is turbulent, the result is dramatically different. The circle is distended into a highly complex shape, and the region of mixed fluid expands rapidly.

Di ffu

W. D. Smyth and J. N. Moum Oregon State University, Corvallis, OR, USA

Figure 1 A comparison of mixing enhanced by turbulence with mixing due to molecular processes alone, as revealed by a numerical solution of the equations of motion. The initial state includes a circular region of dyed fluid in a white background. Two possible evolutions are shown: one in which the fluid is motionless (save for random molecular motions), and one in which the fluid is in a state of fully developed, two-dimensional turbulence. The mixed region (yellow–blue) expands much more rapidly in the turbulent case.



Figure 2 Schematic illustration of line vortices and strained regions in turbulent flow. Fluid parcels in the vortex interiors rotate with only weak deformation. In contrast, fluid parcels moving between the vortices are rapidly elongated in the direction of the purple arrows and compressed in the direction of the green arrows.

results in a vorticity concentration of dimension close to unity, i.e. a line vortex. Line vortices are reinforced by the process of vortex stretching. When a vortex is stretched by the surrounding flow, its rotation rate increases to conserve angular momentum. Opposing these processes is molecular viscosity, which both dissipates vorticity and fluxes it away from strongly rotational regions. Turbulence may thus be visualized as a loosely tangled ‘spaghetti’ of line vortices, which continuously advect each other in complex ways (Figure 3). At any given time, some vortices are being created via rollup, some are growing due to vortex stretching, and some are decaying due to viscosity. Many, however, are in a state of approximate equilibrium among these processes, so that they appear as long-lived, coherent features of the flow. Mixing is not accomplished within the vortices themselves; in fact, these regions are relatively stable, like the eye of a hurricane. Instead, mixing occurs mainly in regions of intense strain that exist between any two nearby vortices that rotate in the same sense (Figure 2). It is in these regions that fluid parcels are deformed to produce amplified gradients and consequent rapid mixing.

Stationary, Homogeneous, Isotropic Turbulence Although the essential structures of turbulence are not complex (Figure 2), they combine in a bewildering range of sizes and orientations that defies analysis (Figure 3). Because of this, turbulence is most usefully understood in statistical terms. Although the statistical approach precludes detailed

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Figure 3 Computer simulation of turbulence as it is believed to occur in the ocean thermocline. The colored meshes indicate surfaces of constant vorticity.

prediction of flow evolution, it does give access to the rates of mixing and property transport, which are of primary importance in most applications. Statistical analyses focus on the various moments of the flowfield, defined with respect to some averaging operation. The average may betaken over space and/or time, or it may be an ensemble average taken overmany flows begun with similar initial conditions. Analyses are often simplified using three standard assumptions. The flow statistics are assumed to be

• • •

stationary (invariant with respect to translations intime), homogeneous (invariant with respect to translations inspace), and/or isotropic (invariant with respect to rotations).

Much of our present understanding pertains to this highly idealized case. Our description will focus on the power spectra that describe spatial variability of kinetic energy and scalar variance. The spectra provide insight into the physical processes that govern motion and mixing at different spatial scales. Velocity Fields

Big whorls have little whorls That feed on their velocity And little whorls have lesser whorls And so on to viscosity L.F. Richardson (1922) Suppose that turbulence is generated by a steady, homogeneous, isotropic stirring force whose spatial


20


variability is described by the Fourier wavenumber kF . Suppose further that the turbulence is allowed to evolve until equilibrium is reached between forcing and viscous dissipation, i.e., the turbulence is statistically stationary. Figure 4 shows typical wavenumber spectra of kinetic energy, EðkÞ, and kinetic energy dissipation, DðkÞ, for such a flow. EðkÞdk is the kinetic energy contained in motions whose wavenumber magnitudes lie in an interval of width dk surrounding k. DðkÞdk ¼ nk2 EðkÞdk is the rate at which that kinetic energy is dissipated by molecular viscosity (n) in that wavenumber band. R NThe net rate of energy dissipation is given by e ¼ 0 dk, and is equal (in the equilibrium state) to the rate at which energy is supplied by the stirring force. Nonlinear interactions induce a spectral flux, or cascade, of energy. The energy cascade is directed primarily (though not entirely) toward smaller scales, i.e., large-scale motions interact to create smaller-scale motions. The resulting small eddies involve sharp velocity gradients, and are therefore susceptible to viscous dissipation. Thus, although kinetic energy resides mostly in large-scale motions, it is dissipated primarily by small-scale motions. (Note that the logarithmic axes used in Figure 4 tend to de-emphasize the peaks in the energy and dissipation rate spectra.) Turbulence can be envisioned as a ‘pipeline’ conducting kinetic energy through wavenumber space: in at the large scales, down the spectrum, and out again at the small scales, all at a rate e. The cascade concept was first suggested early Energy

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E (k)

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Dissipation

Energy, dissipation rate

D (k)

kF

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Figure 4 Theoretical wavenumber spectra of kinetic energy and kinetic energy dissipation for stationary, homogeneous, isotropic turbulence forced at wavenumber kF . Approximate locations of the energy containing, inertial, and dissipation subranges are indicated, along with the Kolmogorov wavenumber kk . Axes are logarithmic. Numerical values depend on Re and are omitted here for clarity.

in the twentieth century by L.F. Richardson, who immortalized his idea in the verse quoted at the beginning of this section. The energy spectrum is often divided conceptually into three sections. The energy-containing subrange encompasses the largest scales of motion, whereas the dissipation subrange includes the smallest scales. If the range of scales is large enough, there may exist an intermediate range in which the form of the spectrum is independent of both large-scale forcing and small-scale viscous effects. This intermediate range is called the inertial subrange. The existence of the inertial subrange depends on the value of the Reynolds number: Re ¼ ul=n, where u and l are scales of velocity and length characterizing the energy-containing range. The spectral distance between the energy-containing subrange and the dissipation 3=4 subrange, kF =kK , is proportional to Re . A true inertial subrange exists only in the limit of large Re . In the 1940s, the Russian statistician A.N. Kolmogorov hypothesized that, in the limit Re -N, the distribution of eddy sizes in the inertial and dissipation ranges should depend on only two parameters (besides wavenumber): the dissipation rate e and the viscosity n, i.e., E ¼ Eðk; e; nÞ. Dimensional reasoning then implies that E ¼ e1=4 n5=4 f ðk=kK Þ, where kK ¼ ðe=n3 Þ1=4 is the Kolmogorov wavenumber and f is some universal function. Thus, with the assumptions of stationarity, homogeneity, isotropy, and infinite Reynolds number, all types of turbulence, from flow over a wing to convection in the interior of the sun, appear as manifestations of a single process whose form depends only on the viscosity of the fluid and the rate at which energy is transferred through the ‘pipeline’. This tremendous simplification is generally regarded as the beginning of the modern era of turbulence theory. Kolmogorov went on to suggest that the spectrum in the inertial range should be simpler still by virtue of being independent of viscosity. In that case E ¼ Eðk; eÞ, and the function can be predicted from dimensional reasoning alone up to the universal constant CK , namely, E ¼ CK e2=3 k5=3 . This powerlaw spectral form indicates that motions in the inertial subrange are self-similar, i.e., their geometry is invariant under coordinate dilations. Early efforts to identify the inertial subrange in laboratory flows were inconclusive because the Reynolds number could not be made large enough. (In a typical, laboratory-scale water channel, uB0:1ms1 , lB0:1m, and nB106 m2 s1 , giving Re B104 . In a typical wind tunnel, uB1ms1 , B1m, and nB106 m2 s1 , so that Re B105 .) The inertial subrange spectrum was first verified in 1962 using measurements in a strongly turbulent tidal channel



Passive Scalars and Mixing

Now let us suppose that the fluid possesses some scalar property y, such as temperature or the concentration of some chemical species, and that the scalar is dynamically passive, i.e., its presence does not affect the flow. (In the case of temperature, this is true only for sufficiently small-scale fluctuations; see Buoyancy Effects later in this article for details.) Suppose also that there is a source of large-scale variations in y, e.g., an ambient temperature gradient in the ocean. Isosurfaces of y will be folded and kneaded by the turbulence so that their surface area tends to increase. As a result, typical gradients of y will also increase, and will become susceptible to erosion by molecular diffusion. Scalar variance is destroyed at a rate w, which is equal (in equilibrium) to the rate at which variance is produced by the large eddies. Thus, the turbulent mixing of the scalar proceeds in a manner similar to the energy cascade discussed above. However, there is an important difference in the two phenomena. Unlike energy, scalar variance is driven to small scales by a combination of two processes. First, scalar gradients are compressed by the strain fields between the turbulent eddies. Second, the eddies themselves are continually redistributed toward smaller scales. (The latter process is just the energy cascade described in the previous section.) Figure 5 shows the equilibrium scalar variance spectrum for the case of heat mixing in water. Most of the variance is contained in the large scales, which are separated from the small scales by an inertialconvective subrange (so-called because temperature variance is convected by motions in the inertial subrange of the energy spectrum). Here, the spectrum depends only on e and w; its form is Ey ¼ bwe1=3 k5=3 , where b is a universal constant. The shape of the spectrum at small scales is very different from that of the energy spectrum, owing to the fact that, in sea water, the molecular diffusivity, k, of heat is smaller than the kinematic viscosity. The ratio of viscosity to thermal diffusivity is termed the Prandtl number (i.e. Pr ¼ n=k) and has a value near 7 for sea water. In the viscous-convective subrange, the downscale cascade of temperature variance is slowed because the eddies driving the cascade are weakened by viscosity. In other words, the first of the two

Variance-

containing Scalar variance, dissipation rate

near Vancouver Island, where typical turbulent velocity scales uB1ms1 and length scales lB100 m combine with the kinematic viscosity of seawater nB106 m2 s1 to produce a Reynolds number Re B108 . From this experiment and others like it, the value of CK has been determined to be near 1.6.

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Viscousdiffusive

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Figure 5 Theoretical wavenumber spectra of scalar variance and dissipation for stationary, homogeneous, isotropic turbulence forced at wavenumber kF . Approximate locations of the variancecontaining, inertial-convective, viscous-convective, and viscousdiffusive subranges are indicated, along with the Kolmogorov wavenumber kK and the Batchelor wavenumber kB . Axes are logarithmic. Numerical values depend on Re and are omitted here for clarity.

processes listed above as driving the scalar variance cascade is no longer active. There is no corresponding weakening of temperature gradients, because molecular diffusivity is not active on these scales. As a result, there is a tendency for variance to ‘accumulate’ in this region of the spectrum and the spectral slope is reduced from 5=3 to 1. However, the variance in this range is ultimately driven into the viscous-diffusive subrange, where it is finally dissipated by molecular diffusion. A measure of the wavenumber at which scalar variance is dissipated is the Batchelor wavenumber, kB ¼ ðe=nk2 Þ1=4 . When Pr > 1, as for sea water, the Batchelor wavenumber is larger than the Kolmogorov wavenumber, i.e., temperature fluctuations can exist at smaller scales than velocity fluctuations. In summary, the energy and temperature spectra exhibit many similarities. Energy (temperature variance) is input at large scales, cascaded down the spectrum by inertial (convective) processes, and finally dissipated by molecular viscosity (diffusion). The main difference between the two spectra is the viscous-convective range of the temperature spectrum, in which molecular smoothing acts on the velocity field but not on the temperature field. This difference is even more pronounced if the scalar field represents salinity rather than temperature, for salinity is diffused even more weakly than heat. The ratio of the molecular diffusivities of heat and salt is of order 102, so that the smallest scales of salinity fluctuation in sea water are ten times smaller than those of temperature fluctuations.


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Turbulence in Geophysical Flows The assumptions of homogeneity, stationarityand isotropy as employed by Kolmogorov have permitted tremendous advances inour understanding of turbulence. In addition, approximations based on theseassumptions are used routinely in all areas of turbulence research. However, wemust ultimately confront the fact that physical flows rarely conform to our simplifying assumptions. In geophysical turbulence, symmetries are upset by acomplex interplay of effects. Here, we focus on three important classes of phenomena that modify small-scale turbulence in the ocean: shear, stratification, and boundary proximity. Shear Effects

Geophysical turbulence often occurs in the presence of a current which varies on scales much larger than the energy-containing scales of the turbulence, and evolves much more slowly than the turbulence. Examples include atmospheric jet streams and largescale ocean currents such as the Gulf Stream and the Equatorial Undercurrent. In such cases, it makes sense to think of the background current as an entity separate from the turbulent component of the flow. Shear upsets homogeneity and isotropy by deforming turbulent eddies. By virtue of the resulting anisotropy, turbulent eddies exchange energy with the background shear through the mechanism of Reynolds stresses. Reynolds stresses represent correlations between velocity components parallel to and perpendicular to the background flow, correlations that would vanish if the turbulence were isotropic. Physically, they represent transport of momentum by the turbulence. If the transport is directed counter to the shear, kinetic energy is transferred from the background flow to the turbulence. This energy transfer is one of the most common generation mechanisms for geophysical turbulence. In sheared turbulence, the background shear acts primarily on the largest eddies. Motions on p scales ffiffiffiffiffiffiffiffiffi much smaller than the Corrsin scale, LC ¼ e=S3 (where S ¼ dU=dz, the vertical gradient of the ambient horizontal current) are largely unaffected. Buoyancy Effects

Most geophysical flows are affected to some degree by buoyancy forces, which arise due to spatial variations in density. Buoyancy breaks the symmetry of the flow by favoring the direction in which the gravitational force acts. Buoyancy effects can either force or damp turbulence. Forcing occurs in the case of unstable density stratification, i.e., when heavy

fluid overlies light fluid. This happens in the atmosphere on warm days, when the air is heated from below. The resulting turbulence is often made visible by cumulus clouds. In the ocean, surface cooling (at night) has a similar effect. Unstable stratification in the ocean can also result from evaporation, which increases surface salinity and hence surface density. In each of these cases, unstable stratification results in convective turbulence, which can be extremely vigorous. Convective turbulence usually restores the fluid to a stable state soon after the destabilizing flux ceases (e.g., when the sun rises over the ocean). Buoyancy effects tend to damp turbulence in the case of stable stratification, i.e., when light fluid overlies heavier fluid. In stable stratification, a fluid parcel displaced from equilibrium oscillates pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vertically with frequency N ¼ gr1 dr=dz, the buoyancy or Brunt–Vaisala frequency (g represents acceleration due to gravity and rðzÞ is the ambient mass density). A result of stable stratification that can dramatically alter the physics of turbulence is the presence of internal gravity waves (IGW). These are similar to the more familiar interfacial waves that occur at the surfaces of oceans and lakes, but continuous density variation adds the possibility of vertical propagation. Visible manifestations of IGW include banded clouds in the atmosphere and slicks on the ocean surface. IGW carry momentum, but no scalar flux and no vorticity. In strongly stable stratification, motions may be visualized approximately as two-dimensional turbulence (Figure 1) flowing on nearly horizontal surfaces that undulate with the passage of IGW. The quasitwo-dimensional mode of motion carries all of the vorticity of the flow (since IGW carry none), and is therefore called the vortical mode. In moderately stable stratification, three-dimensional turbulence is possible, but its structure is modified by the buoyancy force, particularly at large scales. Besides producing anisotropy, the suppression of vertical motion damps the transfer of energy from any background shear, thus reducing the intensity of turbulence. On scales smaller than the Ozmipffiffiffiffiffiffiffiffiffiffimuch ffi dov scale, L0 ¼ e=N 3 , buoyancy has only a minor effect. (In Passive scalars and mixing above, we used temperature as an example of a dynamically passive quantity. This approximation is valid only on scales smaller than the Ozmidov scale.) The relative importance of stratification and shear depends on the magnitudes of S and N. If SbN, shear dominates and turbulence is amplified. On the other hand, if S5N, the buoyancy forces dominate and turbulence is suppressed. The relationship between IGW and turbulence in stratified flow is exceedingly complex. At scales in



excess of a few meters (Figure 6), ocean current fluctuations behave like IGW, displaying the characteristic spectral slope k1 . At scales smaller than the Ozmidov scale (typically a few tens of centimeters), fluctuations differ little from the classical picture of homogeneous, isotropic turbulence. The intermediate regime is a murky mix of nonlinear IGW and anisotropic turbulence that is not well understood at present. The breaking of IGW is thought to be the major source of turbulence in the ocean interior. Breaking occurs when a superposition of IGW generates locally strong shear and/or weak stratification. IGW propagating obliquely in a background shear may break on encountering a critical level, a depth at which the background flow speed equals the horizontal component of the wave’s phase velocity. (Many dramatic phenomena occur where wave speed matches flow speed; other examples include the hydraulic jump and the sonic boom.) Just as waves may generate turbulence, turbulent motions in stratified flow may radiate energy in the form of waves. In stably stratified turbulence, the distinction between stirring and mixing of scalar properties becomes crucial. Stirring refers to the advection and deformation of fluid parcels by turbulent motion, whereas mixing involves actual changes in the scalar properties of fluid parcels. Mixing can only be accomplished by molecular diffusion, though it is accelerated greatly in turbulent flow due to stirring (cf. Figure 1 and the accompanying discussion). In stable

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stratification, changes in the density field due to stirring are reversible, i.e., they can be undone by gravity. In contrast, mixing is irreversible, and thus leads to a permanent change in the properties of the fluid. For example, consider a blob of water that has been warmed at the ocean surface, then carried downward by turbulent motions. If the blob is mixed with the surrounding water, its heat will remain in the ocean interior, whereas if the blob is only stirred, it will eventually bob back up to the surface and return its heat to the atmosphere. Boundary Effects

It is becoming increasingly clear that most turbulent mixing in the ocean takes place near boundaries, either the solid boundary at the ocean bottom, or the moving boundary at the surface. All boundaries tend to suppress motions perpendicular to themselves, thus upsetting both the homogeneity and the isotropy of the turbulence. Solid boundaries also suppress motion in the tangential directions. Therefore, since the velocity must change from zero at the boundary to some nonzero value in the interior, a shear is set up, leading to the formation of a turbulent boundary layer. Turbulent boundary layers are analogous to viscous boundary layers, and are sites of intense, shear-driven mixing (Figure 7). In turbulent boundary layers, the characteristic size of the largest eddies is proportional to the distance from the boundary. Near the ocean surface, the flexible nature of the boundaries leads to a multitude of interesting phenomena, notably surface gravity waves and Langmuir cells. These phenomena contribute significantly to upper-ocean mixing and thus to air–sea fluxes of momentum, heat and various chemical species. Boundaries also include obstacles to the flow, such as islands and seamounts, which create turbulence. If flow over an obstacle is stably stratified, buoyancyaccelerated bottom flow and a downstream hydraulic jump may drive turbulence (Figure 7). Ocean turbulence is often influenced by combinations of shear, stratification, and boundary effects. In the example shown in Figure 7, all three effects combine to create an intensely turbulent flow that diverges dramatically from the classical picture of stationary, homogeneous, isotropic turbulence.

Length Scales of Ocean Turbulence

Vertical wavenumber

Figure 6 Energy spectrum (cf. Figure 4) extended to larger scales to include internal gravity waves (IGW) plus anisotropic stratified turbulence. Labels represent approximate length scales from ocean observations.

Examples of turbulent flow regimes that havebeen observed in the ocean can be considered in terms of typical values of e and N that pertain to each (Figure 8). This provides the information to estimate


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both largest and smallest scales present in the flow. The largest scale is approximated by the Ozmidov scale, which varies from a few centimeters in the ocean’s thermocline to several hundred meters in weakly stratified and/or highly energetic flows. The smallest scale, the Kolmogorov scale LK ¼ k1 K , is typically 1 cm or less. Turbulence in the upper ocean mixed layer may be driven by wind and/or by convection due to surface cooling. In the convectively mixed layer, N is effectively zero within the turbulent region, and the maximum length scale is determined by the depth of the mixed layer. In both cases the free surface limits length scale growth. Turbulence in the upper equatorial thermocline is enhanced by the presence of shear associated with the strong equatorial zonal current system. Stratification tends to be considerably stronger in the upper thermocline than in the main thermocline. Despite weak stratification, turbulence in the main thermocline tends to be relatively weak due to isolation from strong forcing. Turbulence in this region is generated primarily by IGW interactions. Tidal channels are sites of extremely intense turbulence, forced by interactions between strong tidal currents and three-dimensional topography. Length scales are limited by the geometry of the channel. Turbulent length scales in the bottom boundary layer are limited below by the solid boundary and above by stratification. Intense turbulence is also found in hydraulically controlled flows, such as have been

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Figure 7 Flow over Stonewall bank, on the continental shelf off the Oregon coast. Colors show the kinetic energy dissipation rate, with red indicating strong turbulence. White contours are isopycnals, showing the effect of density variations in driving the downslope flow. Three distinct turbulence regimes are visible: (1) turbulence driven by shear at the top of the rapidly moving lower layer, (2) a turbulent bottom boundary layer and (3) a hydraulic jump.

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Wind-mixed layers Convectively Upper equatorial mixed layers thermocline Bottom boundary layer Main thermocline

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Figure 8 Regimes of ocean turbulence located with respect to stratification and energy dissipation. Dotted lines indicate Ozmidov and Kolmogorov length scales.

found in the Strait of Gibraltar, and also over topography on the continental shelf (cf. Figure 7). In these flows the stratification represents a potential energy supply that drives strongly sheared downslope currents, the kinetic energy of which is in turn converted into turbulence and mixing. All of these turbulence regimes are subjects of ongoing observational and theoretical research, aimed at generalizing Kolmogorov’s view of turbulence to encompass the complexity of real geophysical flows.



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See also

Further Reading

Atlantic Ocean Equatorial Currents. Brazil and Falklands (Malvinas) Currents. Breaking Waves and Near-Surface Turbulence. Heat and Momentum Fluxes at the Sea Surface. Heat Transport and Climate. Indian Ocean Equatorial Currents. Internal Waves. Island Wakes. Langmuir Circulation and Instability. Mesoscale Eddies. Open Ocean Convection. Turbulence in the Benthic Boundary Layer. Upper Ocean Mixing Processes. Vortical Modes.

Frisch U (1995) Turbulence. Cambridge: Cambridge University Press. Hunt JCR, Phillips OM, and Williams D (1991) Turbulence and Stochastic Processes; Kolmogoroff ’s Ideas 50 Years On. London: The Royal Society. Kundu PK (1990) Fluid Mechanics. London: Academic Press.


TIDAL ENERGY A. M. Gorlov, Northeastern University, Boston, Massachusetts, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2955–2960, & 2001, Elsevier Ltd.

Introduction Gravitational forces between the moon, the sun and the earth cause the rhythmic rising and lowering of ocean waters around the world that results in Tide Waves. The moon exerts more than twice as great a force on the tides as the sun due to its much closer position to the earth. As a result, the tide closely follows the moon during its rotation around the earth, creating diurnal tide and ebb cycles at any particular ocean surface. The amplitude or height of the tide wave is very small in the open ocean where it measures several centimeters in the center of the wave distributed over hundreds of kilometers. However, the tide can increase dramatically when it reaches continental shelves, bringing huge masses of water into narrow bays and river estuaries along a coastline. For instance, the tides in the Bay of Fundy in Canada are the greatest in the world, with amplitude between 16 and 17 meters near shore. High tides close to these figures can be observed at many other sites worldwide, such as the Bristol Channel in England, the Kimberly coast of Australia, and the Okhotsk Sea of Russia. Table 1 contains ranges of amplitude for some locations with large tides. On most coasts tidal fluctuation consists of two floods and two ebbs, with a semidiurnal period of about 12 hours and 25 minutes. However, there are some coasts where tides are twice as long (diurnal tides) or are mixed, with a diurnal inequality, but are still diurnal or semidiurnal in period. The magnitude of tides changes during each lunar month. The

highest tides, called spring tides, occur when the moon, earth and sun are positioned close to a straight line (moon syzygy). The lowest tides, called neap tides, occur when the earth, moon and sun are at right angles to each other (moon quadrature). Isaac Newton formulated the phenomenon first as follows: ‘The ocean must flow twice and ebb twice, each day, and the highest water occurs at the third hour after the approach of the luminaries to the meridian of the place’. The first tide tables with accurate prediction of tidal amplitudes were published by the British Admiralty in 1833. However, information about tide fluctuations was available long before that time from a fourteenth century British atlas, for example. Rising and receding tides along a shoreline area can be explained in the following way. A low height tide wave of hundreds of kilometers in diameter runs on the ocean surface under the moon, following its rotation around the earth, until the wave hits a continental shore. The water mass moved by the moon’s gravitational pull fills narrow bays and river estuaries where it has no way to escape and spread over the ocean. This leads to interference of waves and accumulation of water inside these bays and estuaries, resulting in dramatic rises of the water level (tide cycle). The tide starts receding as the moon continues its travel further over the land, away from the ocean, reducing its gravitational influence on the ocean waters (ebb cycle). The above explanation is rather schematic since only the moon’s gravitation has been taken into account as the major factor influencing tide fluctuations. Other factors, which affect the tide range are the sun’s pull, the centrifugal force resulting from the earth’s rotation and, in some cases, local resonance of the gulfs, bays or estuaries.

Energy of Tides Table 1

Highest tides (tide ranges) of the global ocean

Country

Site

Tide range (m)

Canada England France France Argentina Russia Russia

Bay of Fundy Severn Estuary Port of Ganville La Rance Puerto Rio Gallegos Bay of Mezen (White Sea) Penzhinskaya Guba (Sea of Okhotsk)

16.2 14.5 14.7 13.5 13.3 10.0 13.4

26

The energy of the tide wave contains two components, namely, potential and kinetic. The potential energy is the work done in lifting the mass of water above the ocean surface. This energy can be calculated as: ð E ¼ grA zdz ¼ 0:5grAh2 ; where E is the energy, g is acceleration of gravity, r is the seawater density, which equals its mass per unit


TIDAL ENERGY

volume, A is the sea area under consideration, z is a vertical coordinate of the ocean surface and h is the tide amplitude. Taking an average (gr) ¼ 10.15 kN m3 for seawater, one can obtain for a tide cycle per square meter of ocean surface: E ¼ 1:4h2 ; watt-hour or E ¼ 5:04h2 ; kilojoule The kinetic energy T of the water mass m is its capacity to do work by virtue of its velocity V. It is defined by T ¼ 0.5 m V2. The total tide energy equals the sum of its potential and kinetic energy components. Knowledge of the potential energy of the tide is important for designing conventional tidal power plants using water dams for creating artificial upstream water heads. Such power plants exploit the potential energy of vertical rise and fall of the water. In contrast, the kinetic energy of the tide has to be known in order to design floating or other types of tidal power plants which harness energy from tidal currents or horizontal water flows induced by tides. They do not involve installation of water dams.

Extracting Tidal Energy: Traditional Approach People used the phenomenon of tides and tidal currents long before the Christian era. The earliest navigators, for example, needed to know periodical tide fluctuations as well as where and when they could use or would be confronted with a strong tidal current. There are remnants of small tidal hydromechanical installations built in the Middle Ages around the world for water pumping, watermills and other applications. Some of these devices were exploited until recent times. For example, large tidal waterwheels were used for pumping sewage in Hamburg, Germany up to the nineteenth century. The city of London used huge tidal wheels, installed under London Bridge in 1580, for 250 years to supply fresh water to the city. However, the serious study and design of industrial-size tidal power plants for exploiting tidal energy only began in the twentieth century with the rapid growth of the electric industry. Electrification of all aspects of modern civilization has led to the development of various converters for transferring natural potential energy sources into electric power. Along with fossil fuel power systems and nuclear reactors, which create huge new environmental pollution problems, clean renewable energy sources have attracted scientists

27

and engineers to exploit these resources for the production of electric power. Tidal energy, in particular, is one of the best available renewable energy sources. In contrast to other clean sources, such as wind, solar, geothermal etc., tidal energy can be predicted for centuries ahead from the point of view of time and magnitude. However, this energy source, like wind and solar energy is distributed over large areas, which presents a difficult problem for collecting it. Besides that, complex conventional tidal power installations, which include massive dams in the open ocean, can hardly compete economically with fossil fuel (thermal) power plants, which use cheap oil or coal, presently available in abundance. These thermal power plants are currently the principal component of world electric energy production. Nevertheless, the reserves of oil and coal are limited and rapidly dwindling. Besides, oil and coal cause enormous atmospheric pollution both from emission of green house gases and from their impurities such as sulfur in the fuel. Nuclear power plants produce accumulating nuclear wastes that degrade very slowly, creating hazardous problems for future generations. Tidal energy is clean and not depleting. These features make it an important energy source for global power production in the near future. To achieve this goal, the tidal energy industry has to develop a new generation of efficient, low cost and environmentally friendly apparatus for power extraction from free or ultra-low head water flow. Four large-scale tidal power plants currently exist. All of them were constructed after World War II. They are the La Rance Plant (France, 1967), the Kislaya Guba Plant (Russia, 1968), the Annapolis Plant (Canada, 1984), and the Jiangxia Plant (China, 1985). The main characteristics of these tidal power plants are given in Table 2. The La Rance plant is shown in Figure 1. All existing tidal power plants use the same design that is accepted for construction of conventional river hydropower stations. The three principal structural and mechanical elements of this designare: a water dam across the flow, which creates an artificial water basin and builds up a water head for operation of hydraulic turbines; a number of turbines coupled with electric generators installed at the lowest point of the dam; and hydraulic gates in the dam to control the water flow in and out of the water basin behind the dam. Sluice locks are also used for navigation when necessary. The turbines convert the potential energy of the water mass accumulated on either side of the dam into electric energy during the tide. The tidal power plant can be designed for operation either by double or single action. Double action means that the turbines work in both water


28

TIDAL ENERGY Table 2

Extant large tidal power plants

Country

Site

Installed power (MW)

Basin area (km2)

Mean tide (m)

France Russia Canada China

La Rance Kislaya Guba Annapolis Jiangxia

240 0.4 18 3.9

22 1.1 15 1.4

8.55 2.3 6.4 5.08

flows, i.e. during the tide when the water flows through the turbines, filling the basin, and then, during the ebb, when the water flows back into the ocean draining the basin. In single-action systems, the turbines work only during the ebb cycle. In this case, the water gates are kept open during the tide, allowing the water to fill the basin. Then the gates close, developing the water head, and turbines start

operating in the water flow from the basin back into the ocean during the ebb. Advantages of the double-action method are that it closely models the natural phenomenon of the tide, has least effect on the environment and, in some cases, has higher power efficiency. However, this method requires more complicated and expensive reversible turbines and electrical equipment. The

Figure 1 Aerial view of the La Rance Tidal Power Plant (Source: Electricite´de France).


TIDAL ENERGY

single action method is simpler, and requires less expensive turbines. The negative aspects of the single action method are its greater potential for harm to the environment by developing a higher water head and causing accumulation of sediments in the basin. Nevertheless, both methods have been used in practice. For example, the La Rance and the Kislaya Guba tidal power plants operate under the double-action scheme, whereas the Annapolis plant uses a single-action method. One of the principal parameters of a conventional hydropower plant is its power output P (energy per unit time) as a function of the water flow rate Q (volume per time) through the turbines and the water head h (difference between upstream and downstream water levels). Instantaneous power P can be defined by the expression: P ¼ 9.81 Qh, kW, where Q is in m3s1, h is in meters and 9.81 is the product (rg) for fresh water, which has mass density r ¼ 1000 kg m3 and g ¼ 9.81 m s2. The (rg) component has to be corrected for applications in salt water due to its different density (see above). The average annual power production of a conventional tidal power plant with dams can be calculated by taking into account some other geophysical and hydraulic factors, such as the effective basin area, tidal fluctuations, etc. Tables 2 and 3 contain some characteristics of existing tidal power plants as well as prospects for further development of traditional power systems in various countries using dams and artificial water basins described above.

Extracting Tidal Energy: Non-traditional Approach As mentioned earlier, all existing tidal power plants have been built using the conventional design

Table 3

29

developed for river power stations with water dams as their principal component. This traditional river scheme has a poor ecological reputation because the dams block fish migration, destroying their population, and damage the environment by flooding and swamping adjacent lands. Flooding is not an issue for tidal power stations because the water level in the basin cannot be higher than the natural tide. However, blocking migration of fish and other ocean inhabitants by dams may represent a serious environmental problem. In addition, even the highest average global tides, such as in the Bay of Fundy, are small compared with the water heads used in conventional river power plants where they are measured in tens or even hundreds of meters. The relatively low water head in tidal power plants creates a difficult technical problem for designers. The fact is that the very efficient, mostly propeller-type hydraulic turbines developed for high river dams are inefficient, complicated and very expensive for lowhead tidal power application. These environmental and economic factors have forced scientists and engineers to look for a new approach to exploitation of tidal energy that does not require massive ocean dams and the creation of high water heads. The key component of such an approach is using new unconventional turbines, which can efficiently extract the kinetic energy from a free unconstrained tidal current without any dams. One such turbine, the Helical Turbine, is shown in Figure 2. This cross-flow turbine was developed in 1994. The turbine consists of one or more long helical blades that run along a cylindrical surface like a screwthread, having a so-called airfoil or ‘airplane wing’ profile. The blades provide a reaction thrust that can rotate the turbine faster than the water flow itself. The turbine shaft (axis of rotation) must be perpendicular to the water current, and the turbine can be positioned either horizontally or

Some potential sites for tidal power installations (traditional approach)

Country

Site

Potential power (MW)

Basin area (km2)

USA USA Russia Russia UK UK Argentina Korea Australia Australia

Passamaquoddy Cook Inlet Mezen Tugur Severn Mersey San Jose Carolim Bay Secure Walcott

400 Up to 18 000 15 000 6790 6000 700 7000 480 570 1750

300 3100 2640 1080 490 60 780 90 130 260


Mean tide (m) 5.5 4.35 5.66 5.38 8.3 8.4 6.0 4.7 8.4 8.4

30

TIDAL ENERGY

a cross-flow area A is Pw ¼ 0.5rAV3. The turbine efficiency Z, also called power coefficient, is the ratio of the turbine power output Pt to the power of either the water head for traditional design or unconstrained water current Pw , i.e. Z ¼ Pt /Pw. The maximum power of the Uldolmok tidal project shown in Figure 3 is about 90 MW calculated using the above approach for V ¼ 12 knots, A ¼ 2100 m2 and Z ¼ 0.35. Along with the floating power farm projects with helical turbines described, there are proposals to use large-diameter propellers installed on the ocean floor to harness kinetic energy of tides as well as other ocean currents. These propellers are, in general, similar to the well known turbines used for wind farms.

Helical Turbine

Waterproof chamber for generator and data collectors

Ports

Figure 2 Double-helix turbine with electric generator for underwater installation.

vertically. Due to its axial symmetry, the turbine always develops unidirectional rotation, even in reversible tidal currents. This is a very important advantage, which simplifies design and allows exploitation of the double-action tidal power plants. A pictorial view of a floating tidal power plant with a number of vertically aligned triple-helix turbines is shown in Figure 3. This project has been proposed for the Uldolmok Strait in Korea, where a very strong reversible tidal current with flows up to12 knots (about 6 m s1) changes direction four times a day. The following expression can be used for calculating the combined turbine power of a floating tidal plant (power extracted by all turbines from a free, unconstrained tidal current): Pt ¼ 0.5ZrAV3, where Pt is the turbine power in kilowatts, Z is the turbine efficiency (Z ¼ 0.35 in most tests of the triple-helix turbine in free flow), r is the mass water density, A is the total effective frontal area of the turbines in m2 (cross-section of the flow where the turbines are installed) and V is the tidal current velocity in m s1. Note, that the power of a free water current through

Utilizing Electric Energy from Tidal Power Plants A serious issue that must be addressed is how and where to use the electric power generated by extracting energy from the tides. Tides are cyclical by their nature, and the corresponding power output of a tidal power plant does not always coincide with the peak of human activity. In countries with a welldeveloped power industry, tidal power plants can be a part of the general power distribution system. However, power from a tidal plant would then have to be transmitted a long distance because locations of high tides are usually far away from industrial and urban centers. An attractive future option is to utilize the tidal power in situ for year-round production of hydrogen fuel by electrolysis of the water. The hydrogen, liquefied or stored by another method, can be transported anywhere to be used either as a fuel instead of oil or gasoline or in various fuel cell energy systems. Fuel cells convert hydrogen energy directly into electricity without combustion or moving parts, which is then used, for instance, in electric cars. Many scientists and engineers consider such a development as a future new industrial revolution. However, in order to realize this idea worldwide, clean hydrogen fuel would need to be also available everywhere. At present most hydrogen is produced from natural gases and fossil fuels, which emit greenhouse gases into the atmosphere and harm the global ecosystem. From this point of view, production of hydrogen by water electrolysis using tidal energy is one of the best ways to develop clean hydrogen fuel by a clean method. Thus, tidal energy can be used in the future to help develop a new era of clean industries, for example, to clean up the automotive industry, as well as other energy-consuming areas of human activity.


TIDAL ENERGY

31

Electric generators sit above the water

Figure 3 Artist rendition of the floating tidal power plant with vertical triple-helix turbines for Uldolmok Strait (Korean Peninsula).

Conclusion Tides play a very important role in the formation of global climate as well as the ecosystems for ocean habitants. At the same time, tides are a substantial potential source of clean renewable energy for future human generations. Depleting oil reserves, the emission of greenhouse gases by burning coal, oil and other fossil fuels, as well as the accumulation of nuclear waste from nuclear reactors will inevitably force people to replace most of our traditional energy sources with renewable energy in the future. Tidal energy is one of the best candidates for this approaching revolution. Development of new, efficient, low-cost and environmentally friendly hydraulic energy converters suited to free-flow waters, such as triple-helix turbines, can make tidal energy available worldwide. This type of machine, moreover, can be used not only for multi-megawatt tidalpower farms but also for mini-power stations with turbines generating a few kilowatts. Such power stations can

provide clean energy to small communities or even individual households located near continental shorelines, straits or on remote islands with strong tidal currents.

See also Flows in Straits and Channels. Tides.

Further Reading Bernshtein LB (ed.) (1996) Tidal Power Plants. Seoul: Korea Ocean Research and Development Institute (KORDI). Gorlov AM (1998) Turbines with a twist. In: Kitzinger U and Frankel EG (eds.) Macro-Engineering and the Earth: World Projects for the Year 2000 and Beyond, pp. 1--36. Chichester: Horwood Publishing. Charlier RH (1982) Tidal Energy. New York: Van Nostrand Reinhold.


TIDES D. T. Pugh, University of Southampton, Southampton, UK Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2961–2968, & 2001, Elsevier Ltd.

Introduction Even the most casual coastal visitor is familiar with marine tides. Slightly more critical observers have noted from early history, relationships between the movements of the moon and sun, and with the phases of the moon. Several plausible and implausible explanations for the links were advanced by ancient civilizations. Apart from basic curiosity, interest in tides was also driven by the seafarer’s need for safe and effective navigation, and by the practical interest of all those who worked along the shore. Our understanding of the physical processes which relate the astronomy with the complicated patterns observed in the regular tidal water movements is now well advanced, and accurate tidal predictions are routine. Numerical models of the ocean responses to gravitational tidal forces allow computations of levels both on- and offshore, and satellite altimetry leads to detailed maps of ocean tides that confirm these. The budgets and flux of tidal energy from the earth–moon dynamics through to final dissipation in a wide range of detailed marine processes has been an active area of research in recent years. For the future, there are difficult challenges in understanding the importance of these processes for many complicated coastal and open ocean phenomena.

The two main tidal features of any sea-level record are the range (measured as the height between successive high and low levels) and the period (the time between one high (or low) level and the next high (or low) level).Spring tides are semidiurnal tides of increased range, which occur approximately twice a month near the time when the moon is either new or full. Neap tides are the semidiurnal tides of small range which occur between spring tides near the time of the first and last lunar quarter. The tidal responses of the ocean to the forcing of the moon and the sun are very complicated and tides vary greatly from one site to another. Tidal currents, often called tidal streams, have similar variations from place to place. Semidiurnal, mixed, and diurnal currents occur; they usually have the same characteristics as the local tidal changes in sea level, but this is not always so. For example, the currents in the Singapore Strait are often diurnal in character, but the elevations are semidiurnal. It is important to make a distinction between the popular use of the word ‘tide’ to signify any change of sea level, and the more specific use of the word to mean only regular, periodic variations. We define tides as periodic movements which are directly related in amplitude and phase to some periodic geophysical force. The dominant geophysical forcing function is the variation of the gravitational field on the surface of the earth, caused by the regular movements of the Moon–Earth and Earth–Sun systems. Movements due to these gravitational forces are termed gravitational tides. This is to distinguish them from the smaller movements due to regular meteorological forces which are called eithermeteorological or more usually radiational tides.

Gravitational Potential Tidal Patterns Modern tidal theory began when Newton(1642– 1727) applied his formulation of the Law of Gravitational Attraction: that two bodies attract each other with a force which is proportional to the product of their masses and inversely proportional to the square of the distance between them. He was able to show why there are two tides for each lunar transit. He also showed why the half-monthly springto-neap cycle occurred, why once-daily tides are a maximum when the Moon is furthest from the plane of the equator, and why equinoctial tides are larger than those at the solstices.

32

The essential elements of a physical understanding of tide dynamics are contained in Newton’s Laws of Motion and in the principle of Conservation of Mass. For tidal analysis the basics are Newton’s Laws of Motion and the Law of Gravitational Attraction. The Law of Gravitational Attraction states that for two particles of masses m1 and m2, separated by a distance r the mutual attraction is: F¼G

m1 m2 r2

G is the universal gravitational constant.


½1

TIDES

33

Use is made of the concept of the gravitational potential of a body; gravitational potential is the work which must be done against the force of attraction to remove a particle of unit mass to an infinite distance from the body. The potential at P on the Earth’s surface (Figure 1) due to the moon is:

The terms in Pn(cos f) are the Legendre Polynomials:

Gm Op ¼ MP

P3 ¼ 12 5cos3 f 3cos f

½2

This definition of gravitational potential, involving a negative sign, is the one normally adopted in physics, but there is an alternative convention often used in geodesy, which treats the potential in the above equation as positive. The advantage of the geodetic convention is that an increase in potential on the surface of the earth will result in an increase of the level of the free water surface. Potential has units of L2T2. The advantage of working with gravitational potential is that it is a scalar property, which allows simpler mathematical manipulation; in particular, the vector, gravitational force on a particle of unit mass is given by grad (Op). Applying the cosine law to DOPM in Figure 1 MP2 ¼ a2 þ r2 2ar cos f

½3

P1 ¼ cos f

½7

P2 ¼ 12 3cos2 f 1

½8

The tidal forces represented by the terms in this potential are calculated from their spatial gradients grad (Pn). The first term in the equation is constant (except for variations in r) and so produces no force. The second term produces a uniform force parallel to OM because differentiating with respect to (a cos f) yields a gradient of potential which provides the force necessary to produce the acceleration in the earth’s orbit towards the center of mass of the Moon–Earth system. The third term is the major tide-producing term. For most purposes the fourth term may be neglected, as may all higher terms. The effective tide-generating potential is therefore written as: OP ¼ 12 Gm

Hence we have:

½10

qOp ¼ 2gDl cos2 f 13 qa

½11

horizontally in the direction of increasing f.

1=2 Gm a a2 1 2 cosf þ 2 Op ¼ r r r

½5 qOp ¼ gDl sin2f adf

½12

3 ml a 3 Dl ¼ 2 me Rl

½13

which may be expanded: For the Moon:

Gm a a2 OP ¼ 1 þ P1 ðcosfÞ þ 2 P2 ðcosfÞ r r r 2 a þ 2 P3 ðcosfÞ þ y r

½6

P

O

a2 3cos2 f 1 3 r

½4 vertically upwards :

M

½9

The force on the unit mass at P may be resolved into two components as functions of f:

2 1=2

a a ‘MP ¼ r 1 2 cosf þ 2 r r

a

r Moon

Earth

Figure 1 The general position of the point P on the Earth’s surface, defined by the angle f.

m1 is the lunar mass and me is the Earth mass. R1, the lunar distance, replaces r. The resulting forces are shown in Figure 2. To generalize in three dimensions, the lunar angle f must be expressed in suitable astronomical variables. These are chosen to be declination of the Moon north or south of the equator, the north–south latitude of P, fp, and the hour angle of the moon, which is the difference in longitude between the meridian of P and the meridian of the sublunar point V on the Earth’s surface.


34

TIDES

mass, distance, and declination substituted for lunar parameters. The ratio of the two tidal amplitudes is: ms R l 3 m1 Rs

(A)

(B)

Figure 2 The tide-producing forces at the Earth’s surface, due to the Moon. (A) The vertical forces, showing an outward pull at the equator and a smaller downward pull at the poles. (B) The horizontal forces, which are directed away from the poles towards the equator, with a maximum value at 451 latitude.

The Equilibrium Tide An equilibrium tide can be computed from eqn [10] by replacing cos2f by the full astronomical expression in terms of d1, fp and the hour angle C1. The equilibrium tide is defined as the elevation of the sea surface that would be in equilibrium with the tidal forces if the Earth were covered with water and the response is instantaneous. It serves as an important reference system for tidal analysis. It has three coefficients which characterize the three main species of tides: (1) the long period species; (2) the diurnal species at a frequency of one cycle per day (cos C); and (3) the semidiurnal species at two cycles per day(cos 2C). The equilibrium tide due to the sun is expressed in a form analogous to the lunar tide, but with solar

For the semidiurnal lunar tide at the equator when the lunar declination is zero, the equilibrium tidal amplitude is 0.27 m. For the sun it is 0.13 m. The solar amplitudes are smaller by a factor of 0.46 thanthose of the lunar tide, but the essential details are the same. The maximum diurnal tidal ranges occur when the lunar declination is greatest. The ranges become very small when the declination is zero. This is because the effect of declination is to produce an asymmetry between the two high- and the two low-water levels observed as a point P rotates on the earth within the two tidal bulges. The fortnightly spring/neap modulation of semidiurnal tidal amplitudes is due to the various combinations of the separate lunar and solar semidiurnal tides. At times of spring tides the lunar and solar forces combine together, but at neap tides the lunar and solar forces are out of phase and tend to cancel. In practice, the observed spring tides lag the maximum of the tidal forces, usually by one or two days due to the inertia of the oceans and energy losses. This delay is traditionally called the age of the tide. The observed ocean tides are normally much larger than the equilibrium tide because of the dynamic response of the ocean to the tidal forces. But the observed tides do have their energy at the samefrequencies (or periods) as the equilibrium tide. This forms the basis of tidal analysis.

Tidal Analysis Tidal analysis of data collected by observations of sea levels and currents has two purposes. First, a goodanalysis provides the basis for predicting tides at future times, a valuable aid for shipping and other operations. Secondly, the results of analyses can be mapped and interpreted scientifically in terms of the hydrodynamics of the seas and their responses to tidal forcing. In tidal analysis the aim is to produce significant time-stable tidal parameters which describe the tidal regime at the place of observation. These parameters are often termed tidal constants on the assumption that the responses of the oceans and seas to tidal forces do not change with time. A good tidal analysis seeks to represent the data by afew significant stable numbers which mean something physically. In general, the longer the period of data included in the analysis, the greater the number of


TIDES

constants which can be independently determined. If possible, an analysis should give some idea of the confidence which should be attributed to each tidal constant determined. The close relationship between the movement of the moon and sun, and the observed tides, make the lunar and solar coordinates a natural starting point for any analysis scheme. Three basic methods of tidal analysis have been developed. The first, which is now generally of only historical interest, the non-harmonic method, relates high and low water times and heights directly to the phases of the moon and other astronomical parameters. The second method which is generally used for predictions and for scientific work, harmonic analysis, treats the observed tides as the sum of a finite number of harmonic constituents with angular speeds determined from the astronomical arguments. The third method developsthe concept, widely used in electronic engineering, of a frequency-dependent response of a system to a driving mechanism. For tides, the driving mechanism is the equilibrium potential. The latter twomethods are special applications of the general formalisms of time series analysis. Analyses of changing sea levels (scalar quantities) are obviously easier than those of currents (vectors), which can be analysed by resolving into two components. Harmonic Analysis

The basis of harmonic analysis is the assumption that the tidal variations can be represented by a finite number N of harmonic terms of the form: Hn cosðsn t gn Þ where Hn is the amplitude, gn is the phase lag on the equilibrium tide at Greenwich and sn is the angular speed. The angular speeds sn are determined by an expansion of the equilibrium tide into harmonic terms. The speeds of these terms are found to have the general form: on ¼ ia o1 þ ib o2 þ ic o3 þ ðo4 ; o5 ; o6 termsÞ

½14

where the values of o1 to o6 are the angular speeds related to astronomical parameters and the coefficients, ia to ic are small integers (normally 0, 1 or 2) (Table 1). The phase lags gn are defined relative to the phase of the corresponding term in the harmonic expansion of the equilibrium tide. Full harmonic analysis of the equilibrium tide shows the grouping of tidal terms into species (1;

35

Table 1 The basic astronomical periods which modulate the tidal forces

Mean solar day (msd) Mean lunar day Sidereal month Tropical year Moon’s perigee Regression of Moon’s nodes Perihelion

Period

Symbol

1.0000 msd 1.0351 msd 27.3217 msd 365.24222 msd 8.85 years 18.61 years 20 942 years

o0 o1 o2 o3 o4 o5 o6

diurnal, semidiurnal y), groups (o2; monthly) and constituents (o3; annual). Response Analysis

The basic ideas involved in response analysis are common to many activities. A system, sometimes called a ‘black box’, is subjected to an external stimulus or input. The output from a system depends on the input and the system response to that input. The response of the system may be evaluated by comparing the input and output functions at various forcing frequencies. These ideas are common in many different contexts, including mechanical engineering, financial modeling and electronics. In tidal analysis the input is the equilibrium tidal potential. The tidal variations measured at a particular site may be considered as the output from the system. The system is the ocean, and we seek to describe its response to gravitational forces. This ‘response’ treatment has the conceptual advantage of clearly separating the astronomy (the input) from the oceanography (the black box). The basic response analysis assumes a linear system, but weak nonlinear interactions can be allowed for with extra terms.

Tidal Dynamics The equilibrium tide consists of two symmetrical tidal bulges directly opposite the moon or sun. Semidiurnal tidal ranges would reach their maximum value of about 0.5 m at equatorial latitudes. The individual high water bulges would track around the earth, moving from east to west in steady progression. These theoretical characteristics are clearly not those of the observed tides. The observed tides in the main oceans have much larger mean ranges, of about 1 m, but there are considerable variations. Times of tidal high water vary in a geographical pattern which bears norelationship to the simple ideas of a double bulge. The tides spread from the oceans onto the surrounding


36

TIDES

continental shelves, where even larger ranges areobserved. In some shelf seas the spring tidal range may exceed 10 m: the Bay of Fundy, the Bristol Channel and the Argentine Shelf are well-known examples. Laplace (1749–1827) advanced the basic mathematical solutions for tidal waves on a rotating earth. More generally, the reasons for these complicated ocean responses to tidal forcing may be summarized as follows. 1. Movements of water on the surface of the earth must obey the physical laws represented by the hydrodynamic equations of continuity and momentum balance; this means that they must propagate as long waves. Any propagation of a wave, east to west around the earth, is impeded by the north–south continental boundaries. 2. Long waves travel at a speed that is related to the water depth; oceans are too shallow for this to match the tracking of the moon. 3. The various ocean basins have their individual natural modes of oscillation which influence their response to the tide-generating forces. There are many resonant frequencies. However, the whole global ocean system seems to be near to resonance at semidiurnal tidal frequencies, as the observed semidiurnal tides are generally much bigger than the diurnal tides. 4. Water movements are affected by the rotation of the earth. The tendency for water movement to maintain a uniform direction in absolute spacemeans that it performs a curved path in the rotating frame of reference within which our observations are made. 5. The solid earth responds elastically to the imposed gravitational tidal forces, and to the ocean tidal loading. The redistribution of water mass during the tidal cycle affects the gravitational field. Long-Wave Characteristic, No Rotation

Provided that wave amplitudes are small compared with the depth, and that the depth is small compared with the wavelength, then the speed for the wave propagation is: c ¼ ðgDÞ1=2

Standing Waves and Resonance

Two progressive waves traveling in opposite directions result in a wave motion, called a standing wave. This can happen where a wave is perfectly reflected at a barrier. Systems which are forced by oscillations close to their natural period have large amplitude responses. The responses of oceans and many seas are close to semidiurnal resonance. In nature, the forced resonant oscillations cannot grow indefinitely because friction limits the response. Because of energy losses, tidal waves are not perfectly reflected at the head of a basin, which means that the reflected wave is smaller than the ingoing wave. It is easy to show that this is equivalent to a progressive wave superimposed on a standing wave with the progressive wave carrying energy to the head of the basin. Standing waves cannot transmit energy because they consist of two progressive waves of equal amplitude traveling in opposite directions. Long Waves on a Rotating Earth

A long progressive wave traveling in a channel on a rotating Earth behaves differently from a wave traveling along a nonrotating channel. The geostrophic forces that affect the motion in a rotating system, cause a deflection of the currents towards the right of the direction of motion in the Northern Hemisphere. The build-up of water on the right of the channel gives rise to a pressure gradient across the channel, which in turn develops until at equilibrium it balances the geostrophic force. The resulting Kelvin wave is described mathematically:

½15

where g is gravitational acceleration, and D is the water depth. The currents u are related to the instantaneous level z by: u ¼ zðg=DÞ1=2

on the value of g and the water depth; any disturbance which consists of a number of separate harmonic constituents will not change its shape as it propagates – this is nondispersive propagation. Waves at tidal periods are long waves, even in the deep ocean, and so their propagation is nondispersive. In the real ocean, tides cannot propagate endlessly as progressive waves. They undergo reflection at sudden changes of depth and at the coastal boundaries.

½16

Long waves have the special property that the speed c is independent of the frequency, and depends only

zðyÞ ¼ Ho exp

g 1=2 fy zðyÞ ½17 ; u ð yÞ ¼ c D

where z(y) is the amplitude at a distance y from the right-hand boundary (in the Northern Hemisphere) and Ho is the amplitude of the wave at the boundary. The effect of the rotation appears only in the factor exp ( fy/c), which gives a decay of wave amplitude away from the boundary with a length scale of


TIDES

c/f ¼ [(gD)1/2/f], which depends on the latitude and the water depth. This scale is called the Rossby radius of deformation. At a distance y ¼ c/f from the boundary the amplitude has fallen to 0.37 Ho. At 451N in water of 4000 m depth the Rossby radius is 1900 km, but in water 50 m deep this is reduced to 215 km. Kelvin waves are not the only solution to the hydrodynamic equations on a rotating Earth: a more general form, called Poincare´ waves, gives amplitudes which vary sinusoidally rather than exponentially in the direction transverse to the direction of wave propagation. The case of a standing-wave oscillation on a rotating Earth is of special interest in tidal studies. Away from the reflecting boundary, tidal waves can be represented by two Kelvin waves traveling in opposite directions. The wave rotates about a nodal point, which is called an amphidrome (Figure 3). The cotidal lines all radiate outwards from the amphidrome and the co-amplitude lines form a set of nearly concentric circles around the center at the amphidrome, at which the amplitude is zero. The amplitude is greatest around the boundaries of the basin.

Ocean Tides Dynamically there are two essentially different types of tidal regime; in the wide and relatively deep ocean

37

basins the observed tides are generated directly by the external gravitational forces; in the shelf seas the tides are driven by co-oscillation with the oceanic tides. The ocean response to the gravitational forcing may be described in terms of a forced linear oscillator, with weak energy dissipation. A global chart of the principal lunar semidiurnal tidal constituent M2 shows a complicated pattern of amphidromic systems. As a general rule these conform to the expected behavior for Kelvin wave propagation, with anticlockwise rotation in the Northern Hemisphere, and clockwise rotation in the Southern Hemisphere. For example, in the Atlantic Ocean the mostfully developed semidiurnal amphidrome is located near 501N, 391W. The tidal waves appear to travel around the position in a form which approximates to a Kelvin wave, from Portugal along the edge of the north-west European continental shelf towards Iceland, and thence west and south past Greenland to Newfoundland. There is a considerable leakage of energy to the surrounding continental shelves and to the Arctic Ocean, so the wave reflected in a southerly direction, is weaker than the wave traveling northwards along the European coast. The patterns of tidal waves on the continental shelf are scaled down as the wave speeds are reduced. In the very shallow water depths (typically less than 20 m) there are strong tidal currents and substantial

Y λ/

2

4

3

2

1

12

11

λ/

9

10

4 8

Open boundary

7

6 0.4

0.4 0.8

1.2

1.2 8

9

5

0.8

10

11

0

1

2

Reflecting (closed) boundary

Reflected Kelvin wave

1.6 3

4

X

Ingoing Kelvin wave Figure 3 Cotidal and co-amplitude lines for a Kelvin wave reflected without energy loss in a rectangular channel. The incoming wave travels fromleft to right. Continuous lines are cotidal lines at intervals of 1/12 of a full cycle. Broken lines are lines of equal amplitude. Progression of the wave crests in the Northern Hemisphere is anticlockwise for both the amphidromic systems shown. In practice the reflected wave is weaker and so the amphidromes are moved towards the upper wall in the diagram.


38

TIDES

energy losses due to bottom friction. Tidal waves are strongly influenced by linear Kelvin wave dynamics and by basin resonances. Energy is propagated to the shallow regions where it is dissipated.

Energy Fluxes and Budgets The energy lost through tidal friction gradually slows down the rate of rotation of the earth, increasing the length of the day by one second in 41 000 years. Angular momentum of the earth–moon system is conserved by the moon moving away from the earth at 3.7 mm per year. The total rate of tidal energy dissipation due to the M2 tide can be calculated rather exactly from the astronomic observations at 2.50 7 0.05 TW, of which 0.1 TW is dissipated in the solid Earth. The total lunar dissipation is 3.0 TW, and the total due to both sun and moon is 4.0 TW. For comparison the geothermal heat loss is 30 TW, and the 1995 total installed global electric capacity was 2.9 TW. Solar radiation input isfive orders of magnitude greater. Most of the 2.4 TW of M2 energy lost in the ocean is due to the work against bottom friction which opposes tidal currents. Because the friction increases approximately as the square of current speed, and the energy loses as the cube, tidal energy loses are concentrated in a few shelf areas of strong tidal currents. Notable among these are the north-west European Shelf, the Patagonian Shelf, the Yellow Sea, the Timor and Arafura Seas, Hudson Bay, Baffin Bay, and the Amazon Shelf. It now appears that up to 25% (1 TW) of the tidal energy may be dissipated by internal tidal waves in the deep ocean, where the dissipation processes contribute to vertical mixing and the breakdown of stratification. Again, energy losses may be concentrated in a few areas, for example where the rough topography of midocean ridges and islandarcs create favorable conditions. One of the main areas of tidal research is increasingly concentrated on gaining a better understanding of the many nonlinear ocean processes that are driven by this cascading tidal energy. Some examples, outlined below, are considered in more detail elsewhere in this Encyclopedia.

•

•

Generation of tidal fronts in shelf seas, where the buoyancy forces due to tidal mixing compete with the buoyancy fluxes due to surface heating; the ratio of the water depth divided by the cube of the tidal currentis a good indicator of the balance between the two factors, and fronts form along lines where this ratio reaches a critical value. River discharges to shallow seas near the mouth of rivers. The local input of freshwater

• •

•

•

•

•

buoyancy may be comparable to the buoyancy input fromsummer surface warming. These regions are called ROFIs (regions of freshwater influence). Spring–neap variations in the energy of tidal mixing strongly influence the circulation in these regions. The mixing and dispersion of pollutants often driven by the turbulence generated by tides. Maximum turbulent energy occurs some hours after the time of maximum tidal currents. Sediment processes of erosion and deposition are often controlled by varying tidal currents, particularly over a spring–neap cycle. The phase delay in suspended sediment concentration after maximum currents may be related to the phase lag in the turbulent energy, which has importantconsequences for sediment deposition and distribution. Residual circulation is partly driven by nonlinear responses to tidal currents in shallow water. Tidal flows also induce residual circulation around sandbanks because of the depth variations. In the Northern Hemisphere this circulation is observed to be in a clockwise sense. Near headlands and islands which impede tidal currents, residual eddies can cause marked asymmetry between the time and strength of the tidal ebb and flow currents. Tidal currents influence biological breeding patterns, migration, and recruitment. Some types of fish have adapted to changing tidal currents to assist in their migration: they lie dormant on the seabed when the currents are not favorable. Tidal mixing in shallow seas promotes productivity by returning nutrients to surface waters where light is available. Tidal fronts are known to be areas of high productivity. The most obvious example of tidal influence on biological processes is the zonation of species found at different levels along rocky shorelines. Evolution of sedimentary shores due to the dynamic equilibrium between waves, tides and other processes along sedimentary coasts which resultsin a wide range of features such as lagoons, sandbars, channels, and islands. These are very complicated processes which are still difficult to understandand model. Tidal amphidrome movements. The tidal amphidromes as shown in Figure 4 only fall along the center line of the channel if the incoming tidal Kelvin wave is perfectly reflected. In reality, the reflected wave is weaker, and the amphidromes are displaced towards the side of the sea along which the outgoing tidal wave travels. Proportionately more energy is removed at spring tides than at neap tides, so the amphidromes can


_ 270°

_ 240°

_ 210°

_ 180°

_ 150°

_ 120°

_ 90°

_ 60°

_ 30°

_ 0°

_ 300° _ 330° LEGOS _ Toulouse

_ 270°

_ 240°

_ 210°

_ 180°

_ 150°

_ 120°

_ 90°

_ 60°

_ 30°

_ 0°

10

20

30

60° 0.0

5

40

50

60

70

80

90

100

_ 90

°

_ 90°

_ 60

°

_ 60°

_ 30°

_ 30°

_0

_ 0°

°

30°

30°

60°

90°

_ 300°

_ 330°

39

90°

TIDES

150

Figure 4 Map of the principal lunar semidiurnal tide produced by computer. Ocean tides observed by satellite and in situ now agree very closely with computer modeled tides. The dark areas show regions of high tida lamplitude. Note the convergence of cophase lines at amphidromes. In the Northern Hemisphere the tides normally progress in an anticlockwise sense around the amphidrome; in the Southern Hemisphere the progression is usually clockwise.

move by several tens of kilometers during thespring–neap cycle. Nonlinear processes on the basic M2 tide generate a series of higher harmonics, M4, M6y, with corresponding terms such as MS4 for spring–neap interactions. A better understanding of the significance of the amplitudes and phases of these terms in the analyses of shallow-water tides and tidal phenomena will be an important tool in advancing our overall knowledge of the influence of tides on a wide range of ocean processes.

See also Beaches, Physical Processes Affecting. Coastal Trapped Waves. Dispersion in Shallow Seas. Estuarine Circulation. Fish Migration, Horizontal. Geomorphology. Internal Tidal Mixing. Internal Waves. Intertidal Fishes. Lagoons. River Inputs. Salt Marshes and Mud Flats. Satellite Altimetry. Sea Level Change. Shelf Sea and Shelf Slope Fronts. Tidal Energy. Upper Ocean Vertical Structure. Waves on Beaches.

Further Reading Cartwright DE (1999) Tides – a Scientific History. Cambridge: Cambridge University Press. Garrett C and Maas LRM (1993) Tides and their effects. Oceanus 36(1): 27--37. Parker BB (ed.) (1991) Tidal Hydrodynamics. New York: John Wiley. Prandle D (1997) Tidal characteristics of suspended sediment concentrations. Journal of Hydraulic Engineering 123: 341--350. Pugh DT (1987) Tides, Surges and Mean Sea Level. Chichester: John Wiley. Ray RD and Woodworth PL (eds.) (1997) Special issue on tidal science in honour of David E Cartwright. Progress in Oceanography 40. Simpson JH (1998) Tidal processes in shelf seas. In: Brink KH and Robinson AR (eds.) The Sea, Vol. 10. New York: John Wiley. Wilhelm H, Zurn W, and Wenzel HG (eds.) (1997) TidalPhenomena: Lecture Notes in Earth Sciences 66. Berlin: Springer-Verlag.


TOMOGRAPHY P. F. Worcester, University of California at San Diego, La Jolla, CA, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2969–2986, & 2001, Elsevier Ltd.

Introduction Ocean acoustic tomography is a method for acoustic remote sensing of the ocean interior that takes advantage of the facts that the propagation of sound through the ocean is sensitive to quantities of oceanographic interest, such as temperature and water velocity, and that the ocean is nearly transparent to low-frequency sound so that signals can be transmitted over long distances. The procedure is (i) to transmit acoustic signals through the ocean, (ii) to make precise measurements of the properties of the received signals, e.g., travel times, and (iii) to use inverse methods to infer the state of the ocean traversed by the sound field from the measured properties. The characteristics of the ocean between the sources and receivers are determined, rather than the characteristics of the ocean at the instruments as is the case for conventional thermometers and current meters. Ocean acoustic tomography has a number of attractive attributes. It makes possible the rapid and repeated measurement of ocean properties over large areas, taking advantage of the speed with which sound travels in water (B1500 m s1). It permits the monitoring of regions in which it is difficult to install instruments to make direct measurements, such as the Gulf Stream or the Strait of Gibraltar, using sources and receivers on the periphery of the region. Acoustic measurements are inherently spatially integrating, suppressing the small-scale variability that can contaminate point measurements and providing direct measurements of horizontal and vertical averages over large ranges. Finally, the amount of data grows as the product (S R) of the number of acoustic sources S and receivers R, rather than linearly as the sum of the number of instruments (S þ R) as is the case for point measurements. Ocean acoustic tomography was originally introduced by Munk and Wunsch in 1979 to address the difficult problem of observing the evolving ocean mesoscale. Mesoscale variability has spatial scales of order 100 km and timescales of order one month. The short timescales mean that ships move too slowly for ship-based measurements to be practical.

40

The short spatial scales mean that moored sensors must be too closely spaced to be practical. Munk and Wunsch proposed that the travel times of acoustic signals propagating between a relatively small number of sources and receivers could be used to map the evolving temperature field in the intervening ocean. Their work led directly to the first 3D ocean acoustic tomography experiment, conducted in 1981. In spite of the marginal acoustic sources that were available at the time, the experiment showed that it was possible to use acoustic methods to map the evolving mesoscale field in a 300 km by 300 km region (Figure 1). It was quickly realized, however, that the integral measures provided by acoustic methods are powerful tools for addressing certain types of problems, including the measurement of integral quantities such as heat content, mass transport, and circulation. Acoustic measurements of the integrated water velocity around a closed contour, for example, provide the circulation, which is directly related to the arealaverage vorticity in the interior by Stokes’ theorem. Vorticity is difficult to measure in other ways. The suppression of small-scale variability in the spatially integrating acoustic measurements also makes them well suited to measure large-scale phenomena, such as the barotropic and baroclinic tides. Finally, the integral measurements provided by the acoustic data can be used to test the skill of dynamic models and to provide strong model constraints. Acoustic scattering due to small-scale oceanic variability (e.g., internal waves) causes the properties of the received acoustic signals to fluctuate. Although these fluctuations limit the precision with which the signal characteristics can be measured and with which oceanic parameters such as temperature and water velocity can be inferred, it was soon realized that measurements of the statistics of the fluctuations can be used to infer the statistical properties of the small-scale oceanic variability, such as internal-wave energy level, as a function of space and time. Summarizing, tomographic methods can be used to map the evolving ocean, to provide integral measures of its properties, and to characterize the statistical behavior of small-scale oceanic variability.

Ocean Acoustics: The Forward Problem The ‘forward’ problem in ocean acoustics is to compute the properties of the received signal given the sound-speed C(x, y, z) and current v(x, y, z) fields


41

TOMOGRAPHY

R

300 km

S

R

S

R

300 km

88

-6

-10 -8 -4

-1

-4

0 -2

0

-5

103 -1

-1

-3

-3

-7

-1

-1 -3

-5

-5

-5

-1

112

-1

-3

-3

-2

109

-6

100

-6

-3

-2

-2

-2

-4

-5

-3

0

-6

-8

-12

-8

97

-7

106

-2

-3

-5

-8

-4

-5

94

91

-4

-7

-5

-6

-8 -10

-9

-4

-4

85 -6

-5

-5

-2

-4

82

-7

R

S

79

76 -2

-2

R

S

66 _ 85

CTD I

115

CTD II 120 _ 139

118

1

-2 -2

-6

-4

-6

-4

-2

-4

-6

-2

-6

-4

-2 -3

-4

-4

-7

1

-5

-5

-4

-4

-1

-3

Figure 1 The 1981 tomography experiment. The first panel shows the geometry, with four source (S) and five receiver (R) moorings on the periphery of a 300 km by 300 km region in the north-west Atlantic Ocean. Subsequent panels show the sound-speed perturbations at 700 m depth derived from the acoustic data at 3-day intervals, with regions of high uncertainty shaded. The initial and final panels are derived from two ship-borne conductivity-temperature-depth (CTD) surveys, each of which required about 20 days to complete. The label on each panel is the year day in 1981. The contour interval is 1 m s1 (0.21C). Adapted from Cornuelle B, Wunsch C, Behringer D, et al. (1985) Tomographic maps of the ocean mesoscale. Part I: Pure acoustics. Journal of Physical Oceanography 15: 133–152.

between the source and receiver. Acoustic remote sensing of the ocean interior requires first a full understanding of the forward problem, i.e., of methods for finding solutions to the wave equation. A variety of approaches are available to do this, including geometric optics, normal mode, and parabolic equation methods. The appropriate method depends in part on the character of the sound-speed and current fields (e.g., range-independent or rangedependent) and in part on the choice of the observables in the received signal to use in the inverse problem. The approach most commonly used in ocean acoustic tomography has been to transmit broadband signals designed to measure the impulse response of the ocean channel and to interpret the peaks in the impulse response in terms of geometric rays. Ray travel times are robust observables in the presence of internal-wave-induced scattering because of Fermat’s principle, which states that ray travel times are not sensitive to first-order changes in the ray path. Other observables are possible, however. The peaks in the impulse response are in some cases more appropriately interpreted in terms of normalmode arrivals, for example, and the observables are then modal group delays. Another possibility is to perform full-field inversions that use the time series

of intensity and phase for the entire received signal as observables. Unfortunately, neither normal modes nor the intensities and phases of the received signal are robust in the presence of internal-wave-induced scattering, and so tend to be useful only at short ranges and/or low frequencies where internal-waveinduced scattering is less important. A number of other possible observables have been proposed as well. In what follows the use of ray travel times as observables will be emphasized, in part because they have been the observable most commonly used to date and in part because they are robust to internalwave-induced scattering. Ocean Sound Channel

The speed with which sound travels in the ocean increases with increasing temperature, salinity, and pressure. As a result, over much of the temperate world ocean there is a subsurface minimum in sound speed at depths of roughly 1000 m. Sound speed increases toward the surface above the minimum because of increasing temperature and toward the bottom below the minimum because of increasing pressure. Salinity does not play a major role because its effect on sound speed is normally less than that of either temperature or pressure. The depth of the


42

TOMOGRAPHY

sound-speed minimum is called the sound channel axis. The axis shoals towards high latitudes where the surface waters are colder, actually reaching the surface during winter at sufficiently high latitudes. The sound speed gradients above and below the sound channel axis refract acoustic rays toward the axis in accord with Snell’s law. Near-horizontal rays propagating outward from an omnidirectional source on the axis will therefore tend to be trapped, cycling first above and then below the axis (Figure 2). Such rays are referred to as refracted–refracted (RR) rays. Steeper rays will interact with the surface and/or seafloor. Rays can reflect from the sea surface with relatively low loss. Rays that are refracted at depth and reflected from the sea surface are referred to as refracted-surface-reflected (RSR) rays. Both RR and RSR rays can propagate to long distances and are

commonly used in ocean acoustic tomography. Rays that interact with the seafloor tend to be strongly scattered, however. Rays with multiple bottom interactions therefore tend not to propagate to long ranges. A receiver at a specified range from an omnidirectional acoustic source will detect a discrete set of ray arrivals (Figure 2), corresponding to the rays that are at the depth of the receiver at the appropriate range. These rays are called eigenrays and are designated 7p, where 7 indicates an upward/downward launch direction and p is the total number of ray turning points (including reflections). The ray geometry controls the vertical sampling properties of tomographic measurements. One can obtain significant vertical resolution even for the case of source and receiver both on the sound channel axis, because the eigenrays in general have a range of turning depths.

_1

0

C (km s ) 1.50 1.55

Depth (km)

1 2

W

+20

N

3 +11

4 5

0

(A)

100

Range (km)

200

300 +20

+11

Measured

203

(B)

_11 +10 _ 10 +12 _12 +13 _13 +11 +14 _14 +15 _15 +16 _16 +17 _17 +18 _18 +19 _19 +20

+11

+10 _ 10

_9

+9

_7 _7 +8 _8 +8 _8 +9

Predicted

τ (s)

204

Figure 2 (A) Sound-speed profile in the western North Atlantic and the corresponding ray paths for source and receiver near the depth of the sound channel axis and about 300 km apart. The geometry is that of a reciprocal acoustic transmission experiment conducted in 1983 with transceivers designated W(est) and N(orth). (B) Measured and predicted acoustic amplitudes as a function of time for the 1983 experiment. The arrivals are labeled with their ray identifier. The earliest arrivals are from steep ray paths that cycle through nearly the entire water column. The latest arrivals are from flat ray paths that remain near the sound-channel axis. The differences between the measured and predicted arrival times are the data used in tomographic inversions. Adapted from Howe BM, Worcester PF and Spindel RC (1987) Ocean acoustic tomography: mesoscale velocity. Journal of Geophysical Research 92: 3785–3805.


TOMOGRAPHY

43

DC=C and jvj=C gives

Ray Travel Time

To first order in jvj=C, the travel time ti of ray i is ti ¼

ð

ds CðrÞ þ vðrÞ r0

½1

Gi

where Gi is the ray path for ray i along which distance s is measured and r0 is the tangent to the ray at position r. The sign of v . r0 depends on the direction of propagation, and the travel times and ray paths in opposite directions differ because of the effects of currents. (Sound travels faster with a current than against a current.) The eigenrays Gi are obtained using a numerical eigenray code.

þ Dtþ i ¼ ti t i ð Þ ¼ ð ½DCðrÞ þ vðrÞ r0 ðÞ ds C2 ðr;Þ

½7

GiðÞ Dt i ¼ ti t i ð Þ ¼ ð ½DCðrÞ vðrÞ r0 ðÞ ds C2 ðr;Þ

½8

GiðÞ

where GiðÞ ; r0 ðÞ are the ray path and tangent vector for the reference state. The superscript plus (minus) refers to propagation in the þ ðÞ direction. The reference travel time is ð ds ½9 ti ðÞ ¼ Cðr;Þ GiðÞ

The Inverse Problem The ‘inverse’ problem is to compute the sound-speed CðrÞ and current vðrÞ fields given the measured travel times. In fact, a great deal is normally known about CðrÞ in the ocean from climatological or other data. The interesting problem is therefore to compute the perturbations from an assumed reference state, using the measured perturbations from the travel times computed for the reference state.

The sum of the travel time perturbations ð 1 ds Dsi ¼ Dtþ DCðrÞ þ Dt ¼ i i 2 C2 ðr;Þ

½10

GiðÞ

depends only on the sound-speed perturbation DCðrÞ. The difference ð 1 ds Ddi ¼ Dtþ vðrÞ r0 ðÞ ½11 þ Dt ¼ i i 2 C2 ðr;Þ

Data

Gi ð Þ

Travel times are in general a nonlinear function of the sound-speed and current fields, because the ray path Gi depends on CðrÞ and vðrÞ. Linearize by setting CðrÞ ¼ Cðr;Þ þ DCðrÞ

½2

vðrÞ ¼ vðr;Þ þ DvðrÞ

½3

where Cðr; Þ; vðr; Þ are the known reference states. The argument ðÞ denotes the dependence of the variables only on the reference state, independent of the measurements. Normally, jDCðrÞj5Cðr;Þ

½4

jDvðrÞj5vðr;Þ

½5

In general, however, jDvðrÞj > jvðr;Þj

½6

because the fluctuations in current at a fixed location in the ocean are typically large compared to the timemean current. Setting vðr; Þ 0; DvðrÞ vðrÞ, forming perturbation travel times, and linearizing to first order in

depends only on the water velocity v . r0 along the ray path. Forming sum and difference travel times separates the effects of DC and v. This separation is crucial for measuring v, because jvj is usually much smaller than DC. It is not crucial for measuring DC, however, and one-way, rather than sum, travel time perturbations are often used for this purpose. The data used in the inverse problem can therefore either be the one-way travel time perturbations, e.g., Dtþ i , or the sum and difference travel time perturbations, Dsi and Ddi . The use of one-way travel time measurements to estimate DC is sometimes given the special name of acoustic thermometry, reflecting the fact that sound-speed perturbations depend mostly on temperature. Reference States and Perturbation Models

The perturbation field DC and therefore the data depend on the choice of reference state, Cðr; Þ. Although there is some freedom in the choice, reference states that include the range and time dependence of the sound-speed field available from ocean climatologies, for example, usually yield reference ray paths that are acceptably close to the true ones. Accurate prior estimates of the ray paths help ensure that the sampling properties of the rays are included properly


44

TOMOGRAPHY

in the inverse procedure and that the nonlinearities associated with ray-path mismatches are minimized. The continuous perturbation fields DC and v are parametrized with a finite number of discrete parameters using a model. Because the tomographic inverse problem is always underdetermined, it is important to use perturbation models that are oceanographically meaningful and that provide an efficient representation of ocean variability. Separable models using a linear combination of basis functions describing the horizontal (x, y) and vertical (z) structures, DCðx; y; zÞ ¼

X

aklm Xk ðxÞYl ðyÞFm ðzÞ

½12

k;l;m

have frequently been used for simplicity, although more general models are of course possible. The coefficients aklm are the model parameters to be determined, and the Xk, Yl, Fm are the basis functions. Inverse Methods: Vertical Slice

The inverse problem is most simply described for the case of a single acoustic source–receiver pair. Neglecting currents and assuming that the sound-speed perturbation is a function of depth only, Dti ¼

ð

DCðzÞ ds þ dti ; C2 ðr;Þ

i ¼ 1; y; M

½13

Gi ðÞ

where there are M rays. (Note that although the sound-speed perturbation has been assumed to be independent of range, the reference state can be a more general function of position.) The quantity dti has been introduced to represent the noise contribution that is inevitably present. The noise term arises not only from observational errors but also from modeling errors associated with the representation of DC using a finite number of parameters and from nonlinearity errors associated with the use of the ray paths for the reference state rather than the true ray paths. (In the absence of currents the problem can be restated somewhat more simply in terms of sound slowness, S ¼ C1, if desired.) Substituting DCðzÞ ¼

X

am Fm ðzÞ

½14

m

gives Dti ¼

X

am

m

ð

Fm ðzÞ ds þ dti ; C2 ðr;Þ

¼

X m

Eim am þ dti ;

i ¼ 1; y; M

y ¼ Exþn

½17

where y ¼ ½Dti ; x ¼½am ;

E ¼fEim g; n ¼ ½dti

½18

The inverse problem consists (i) of finding a particular solution xˆ and (ii) of determining the uncertainty and resolution of the particular solution. Writing the estimate xˆ as a weighted linear sum of the observations, xˆ ¼ By ¼ BðEx þ nÞ

½19

For zero-mean noise, /nS ¼ 0, the expected value is /xˆ S ¼ BE/xS

½20

The matrix BE is called the resolution matrix. It gives the particular solution as a weighted average of the true solution x, with weights given by the row vectors of BE. If the resolution matrix is the identity matrix I, then the particular solution is the true solution. If the row vectors of BE are peaked on the diagonal with low values elsewhere, the particular solution is a smoothed version of the true solution. The solution uncertainty is described by the covariance matrix D T E ½21 P ¼ xˆ x xˆ x where superscript T denotes transpose. There is an immense literature on inverse methods, and a variety of approaches are available to construct the inverse operator B, including least squares, singular-value decomposition (SVD), and Gauss–Markov estimation. To provide an example of one approach that has been widely used, the Gauss– Markov estimate is discussed briefly here. (The Gauss–Markov estimate is sometimes known as the ‘stochastic inverse’ or as ‘objective mapping’.) The Gauss–Markov estimate is derived by minimizing the expected uncertainty between the true value xj and the estimate xˆj , i.e., by individually minimizing the diagonal elements of the uncertainty covariance matrix P. The result is the Gauss–Markov theorem, B ¼ Uxy U1 xy

½22

½15

Uxy xyT ; Uyy yyT

½23

½16

are the model–data and data–data covariance matrices, respectively. These covariances can be rewritten

where

Gi ð Þ

i ¼ 1; y; M

The elements Eim depend only on prior information. This equation can be written in compact matrix notation as


TOMOGRAPHY

using y ¼ Ex þ n. The model–data covariance matrix becomes

Uxy ¼ xxT E

T

¼ Uxy ET

½24

where it has been assumed that the model x and noise n are uncorrelated. The data–data covariance matrix becomes D E Uyy ¼ ðEx þ nÞðEx þ nÞT ¼ EFxx ET þ Unn

½25

Finally, the inverse estimate, xˆ ¼ By, can be written in the familiar form 1 xˆ ¼ By ¼ Uxx ET EFxx ET þ Unn y

½26

The Gauss–Markov estimator requires that the perturbation model discussed above include the a priori specification of the statistics of the model parameters, i.e., of the covariance matrix Uxx . The solution uncertainty P includes contributions due to data error and due to a lack of resolution. In most realistic cases the lack of resolution dominates the solution uncertainty estimate. A key, and unfamiliar, feature of the acoustic methods is that the solution uncertainty matrix is not diagonal, i.e., the uncertainties in the model parameters are correlated in a way that depends on the ray sampling properties. These correlated uncertainties often cancel in the computation of integral properties of the solution, such as the vertically averaged heat content. Once a solution and its uncertainty have been found, the solution must be evaluated for consistency with the various assumptions made in its construction before it can be accepted. The statistics of the residuals yˆ yÞ, where yˆ ¼ E xˆ , need to be examined for consistency with the assumed noise statistics Unn , for example. Further, ray trajectories should be recomputed for the field CðrÞ ¼ Cðr;Þ þ DCðrÞ

½27

and the resulting ray travel times compared with the original data to test for consistency. Significant differences imply that the reference state is inadequate or the model is inadequately formulated. When nonlinearities are important, iterative or other methods are needed to find a solution consistent with the original data. The linear inverse methods used in ocean acoustic tomography are well known and widely used in a variety of fields. The crucial problem in the application to tomography is the construction of the model used to describe oceanic variability, including the choice of parametrization and the specification of the (co)variances of the model parameters and noise.

45

Sampling Properties of Acoustic Rays

Vertical slice: range-independent The vertical sampling properties of acoustic rays are most easily understood for the range-independent case, in which sound speed is a function of depth z only. In that case, eqn [13] can be converted to an integral over depth z˜ þðÞ ð

Dti ¼

z˜ ðÞ

þ dti ;

dz qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi DCðzÞ ˜ 2 C2 ðz; Þ 1 Cðz; Þ=C i ¼ 1; y; M

½28

˜ where y is the ray using Snell’s law, CðzÞ=cosðyÞ ¼ C, ˜ is the sound angle relative to the horizontal and C speed at the ray turning points z˜7. The function 1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 C2 ðz; Þ 1 Cðz; Þ=C˜

½29

gives the weighting with which DCðzÞ contributes to Dti . There are (integrable) singularities in the weighting function at both the upper and lower ˜ The ray turning point depths, where ( z˜7; Þ ¼ C: travel times are therefore most sensitive to soundspeed perturbations at the ray turning points (Figure 3). The value of the weighting function is the same for depths z7 above and below the sound-channel axis at which Cðzþ ; Þ ¼ Cðz ; Þ

½30

There is a fundamental up–down ambiguity for acoustic measurements in mid-latitudes. It is in principle impossible to distinguish from the acoustic data alone whether the observed travel-time perturbations are due to sound-speed perturbations located above or below the sound channel axis. This ambiguity has to be resolved from a priori information or from other data. This up–down ambiguity is not present in polar regions when the temperature profile is close to adiabatic, so that sound speed is a minimum at the surface and increases monotonically with depth (pressure). Vertical slice: range-dependent A ray trapped in the sound channel, cycling between upper and lower turning points at regular intervals, samples the ocean periodically in space, so that its travel time is sensitive to some spatial frequencies but is unaffected by others. The key to understanding the horizontal sampling properties of acoustic travel times is to consider the wavenumber domain, rather than physical space, using a truncated Fourier series


46

TOMOGRAPHY

The travel-time perturbations are then

0

Dti ¼

0.1

XX k

( akm

m

ð

ds C2 ðr;Þ

Gi ð Þ

) 2pi exp ðkxÞ Fm ðzÞ þ dti L

~+ Upper turning depth z (km)

0.2

0.3

0.4

0.5

0.6

0

1

2

3 ∆ (ms)

4

5

Figure 3 Travel-time perturbations computed at about 1000 km range in the North-east Pacific for a sound-speed perturbation with an amplitude of 1 m s1 at 100 m depth, linearly decreasing to zero at 90 m and 110 m. The travel-time perturbations are zero for rays with upper turning depths below 110 m, because they do not sample the perturbed region. The perturbations are sharply peaked for rays with upper turning depths between 90 m and 110 m. Rays that have upper turning depths above 90 m have nonzero perturbations because they traverse the perturbed region, but the perturbations are relatively small because the ray weighting function falls off rapidly with distance from the turning point. Adapted from Cornuelle BD, Worcester PF, Hildebrand JA, et al. (1993) Ocean acoustic tomography at 1000-km range using wavefronts measured with a large-aperture vertical array. Journal of Geophysical Research 98: 16365–16377.

in x as the model for the sound-speed perturbations,

DCðx; zÞ ¼

XX k

m

where the integrals depend only on prior information. The problem has again reduced to the form y ¼ Ex þ n, with the solution vector x containing an ordered set of the complex Fourier coefficients akm. The sensitivity of the travel-time inverse to various wavenumbers can be quantified by plotting the diagonal of the resolution matrix BE defined in eqn [20]. For the specific case of two moorings separated by 600 km, with a source and five widely-separated receivers on each mooring, the resolution matrix shows the sensitivity of tomographic measurements to the features that match the ray periodicity (i.e., have the same wavelength as the ray double loops) (Figure 4). Further, because the ray paths are somewhat distorted sinusoids in midlatitudes, the resolution matrix displays sensitivity to harmonics of the basic ray double loop length. Finally, as expected, the measurements are sensitive to the mean. There are obvious spectral gaps for wavenumbers between the mean and first harmonics of the ray paths, and again between the first and second harmonics. The harmonics extend over bands of wavenumbers because the eigenrays connecting the source and receiver have a range of double-loop lengths. Horizontal slice The sampling issues present when the goal is to map the evolving ocean using integral data are most easily understood by considering the two-dimensional horizontal slice problem. In this case sound speed is assumed to be constant in the vertical, so that ray paths travel in straight lines in the horizontal plane containing the sources and receivers. Neglecting currents, Dti ¼

2pi akm exp ðkxÞ Fm ðzÞ; L

k ¼ 0; 71; y; 7N

½32

ð

DCðx; yÞ ds þ dti ; C2 ðx; y; Þ

Gi ð Þ

i ¼ 1; y; M ½31

The Fourier series is periodic over a domain of length L and is truncated at harmonic N. The domain is normally chosen sufficiently large (say twice the size of the source–receiver range) to avoid artifacts within the area of interest that might be caused by the periodicity.

½33

where there is one ray path per source–receiver pair and there are a total of M ray paths connecting the sources and receivers. As was the case for the range-dependent vertical slice problem, the key to understanding the horizontal sampling properties of acoustic travel times is to consider the wavenumber domain, rather than physical space, using a truncated Fourier series in x and y


TOMOGRAPHY 1.0

Resolution

0.8 0.6 0.4 0.2 0.0 0

10

20 30 40 k (cycles / 600 km)

50

60

Figure 4 Diagonal elements of the resolution matrix (‘transfer function’) for tomographic measurements over a 600-km path in a range-dependent ocean. The plot is for the lowest baroclinic mode. Adapted from Cornuelle BD and Howe BM (1987) High spatial resolution in vertical slice ocean acoustic tomography. Journal of Geophysical Research 92: 11680–11692.

as the model for the sound-speed perturbations, XX 2pi DCðx; yÞ ¼ akl exp ðkx þ lyÞ ; L k l k; l ¼ 0; 71; y; 7N

½34

where the integrals depend only on prior information. The problem has again reduced to the form y ¼ Ex þ n, with the solution vector x containing an ordered set of the complex Fourier coefficients akl. To explore the horizontal sampling properties of integral data, consider a simple scenario in which two ships start in the left and right bottom corners of a 1000-km square and steam northward in parallel, transmitting from west to east through an isotropic mesoscale field constructed to have a 1/e decay scale of 120 km (Figure 5). Inversion of the resulting travel-time data leads to an estimate that consists only of east–west contours, as all the ray paths measure only zonal averages. To interpret this result in wavenumber space, note that for

ka0

determined for the assumptions made in this simple scenario. Similarly, for north–south transmissions between two ships traveling from east to west, only the parameters ak0 are determined. Combining east– west and north–south transmissions determines both a0l and ak0, but nonetheless fails to give useful maps because the majority of the wavenumbers are still undetermined. Adding scans at 451 determines wavenumbers for which k ¼ l, giving improved, but still imperfect, maps. The conclusion is that generating accurate maps from integral data requires sampling geometries with ray paths at many different angles to provide adequate resolution in wavenumber space. This requirement must be independently satisfied in any region with dimensions comparable to the ocean correlation scale. These results are a direct consequence of the projection-slice theorem. Time-dependent Inverse Methods

The Fourier series is doubly periodic over the square domain of size L and is truncated at harmonic N. The travel-time perturbations are then ( ð XX ds akl Dti ¼ 2 ð x; y; Þ C k l GiðÞ ) 2pi exp ðkx þ lyÞ þ dti ½35 L

ðL dxDCðx; yÞ ¼ 0

47

½36

0

East–west transmissions therefore give information only on the parameters a0l, which are nearly perfectly

The discussion of inverse methods to this point has implicitly assumed that data from a single instant in time are used to estimate the state of the ocean at that instant. Observations from different times can be combined to generate improved estimates of the evolving ocean, however, using a time-dependent ocean model to connect the oceanic states at those times. One seeks to minimize the misfit between the estimate xˆ (t) and the true state x(t) over some finite time span, instead of at a single instant. The practice of combining data with time-evolving ocean circulation models, referred to as ‘assimilation’ or ‘state estimation’, simultaneously tests and constrains the models. A variety of approaches are available to solve this problem, including, for example, Kalman filtering and the use of adjoint methods. Although the problem of combining integral tomographic data with time-evolving models does not differ in any fundamental way from the problem of using other data types, tomographic data do differ from most other oceanographic data because their sampling and information content tend to be localized in spectral space rather than in physical space, as discussed above. It is therefore important to use methods that directly assimilate the tomographic measurements and preserve the integral information they contain. Approximate data assimilation methods optimized for measurements localized in physical space are generally inappropriate because they do not preserve the nonlocal tomographic information.

Selected Tomographic Results Tomographic methods have been used to study a wide range of ocean processes, at diverse locations. Measurements have been made at scales ranging


48

TOMOGRAPHY

Figure 5 The top center panel is the ‘true ocean,’ constructed assuming a horizontally homogeneous and isotropic wavenumber spectrum, to be mapped using tomographic data. (A) W-E transmissions between two northward-traveling ships (left panel). Inversion of the travel time perturbations produces east–west contours in DC (middle) with only a faint relation to the ‘true ocean.’ Expected predicted variances in wavenumber space (right) are 0% (no skill) except for (k,l) ¼ (0,1),(0,2),y,(0,7), which account for s2 ¼ 16% of the a priori DC variance. (B) S-N transmissions between two eastward traveling ships. (C) Combined W-E and S-N transmissions, accounting for 32% of the DC variance. (D) Combined W-E, S-N, SW-NE, and SE-NW transmissions, accounting for 67% of the variance and giving some resemblance to the true ocean. Distances are shown in magameters (Mm) and wavenumbers are shown in cycles per megameter (cpMm). Adapted from Cornuelle BD, Munk WH and Worcester PF (1989) Ocean acoustic tomography from ships. Journal of Geophysical Research 94: 6232–6250.


TOMOGRAPHY

from a few tens of kilometers (e.g., to measure the transport through the Strait of Gibraltar) to thousands of kilometers (e.g., to measure the heat content in the north-east Pacific Ocean). This review concludes by presenting results from a few selected experiments to provide some indication of the breadth of possible applications and to illustrate the strengths and weaknesses of tomographic measurements. Oceanic Convection

Oceanic convection to great depths occurs at only a few locations in the world (see Deep Convection). Nonetheless, it is believed to be the process by which the properties of the surface ocean and deep ocean are connected, with important consequences for the global thermohaline circulation and climate. The deep convective process is temporally intermittent and spatially compact, consisting of convective plumes with scales of about 1 km clustered in chimneys with scales of tens of kilometers. Observing the evolution of the deep convective process and quantifying the amount of deep water formed presents a difficult sampling problem. Tomographic measurements have been key components in programs to study deep convection in the Greenland Sea (1988–1989) and the Mediterranean

49

Sea (1991–1992), as well as in an ongoing program in the Labrador Sea (1996 to present). In all of these regions the tomographic data provide the spatial coverage and temporal resolution necessary for observing the convective process. In the Greenland Sea, for example, six acoustic transceivers were deployed from summer 1988 to summer 1989 in an array approximately 210 km in diameter (Figure 6), as part of the intensive field phase of the International Greenland Sea Project. The acoustic data were combined with moored thermistor data and hydrographic data to estimate the evolution of the three-dimensional temperature field T(x, y, z) in the Greenland Sea during winter. (During the convective period, the hydrographic data were found to be contaminated by small-scale variability and were not useful for determining the chimney and gyre-scale structure.) A convective chimney reaching depths of about 1500 m was observed to the south west of the gyre center during March 1989. The chimney had a spatial scale of about 50 km and a timescale of about 10 days (Figure 7). The location of the chimney seemed to be sensitively linked to the distribution of the relatively warm, salty Arctic Intermediate Water found at intermediate depths. Potential temperature profiles

Figure 6 (A) Geometry of the tomographic transceiver array deployed in the Greenland Sea during 1988–1989. Mooring 2 failed about one month after deployment. A deep convective chimney was observed near the center of the array during March 1989 (shaded region). (B) Time-depth evolution of potential temperature averaged over the chimney region. Contour interval is 0.21C. Typical rms uncertainty (1C) as a function of depth is shown to the right. Total heat flux (from the British Meteorological Office) and daily averaged ice cover (derived from satellite SSM/I measurements) are shown above. Adapated from Morawitz WML, Cornuelle BD and Worcester PF (1996) A case study in three-dimensional inverse methods: combining hydrographic, acoustic, and moored thermistor data in the Greenland Sea. Journal of Atmospheric and Oceanic Techniques 13: 659–679.


50

TOMOGRAPHY

Depth (km)

0

1

° 76

2

6°

°

5°

75

4°

Wes t lo

ngitu

3° de

2°

° 74

r th

No

de

itu

lat

Figure 7 Mixed layer depth in the central Greenland Sea on 19 March 1989, as defined by the minimum depth of the 1.21C isotherm. The ocean is colder than 1.21C above this depth as a result of surface cooling and is warmer below. The main chimney reaches a maximum depth of about 1500 m in an area about 50 km in diameter centered on 74.751N, 3.51W, south west of the gyre center. A secondary chimney with a maximum depth of about 1000 m is evident to the north east of the gyre center, separated from the primary chimney by a ridge of warmer water. Contours of mixed layer depth are shown below. Adapted from Morawitz WML, Sutton PJ, Worcester PF et al. (1996) Threedimensional observations of a deep convective chimney in the Greenland Sea during winter 1988/1989. Journal of Physical Oceanography 26: 2316–2343.

extracted from the three-dimensional inverse estimates were averaged over the chimney region to show the time-evolution of the chimney (Figure 6). A one-dimensional vertical heat balance adequately described changes in total heat content in the chimney region from autumn 1988 until the time of chimney break-up, when horizontal advection became important and warmer waters moved into the region. The average annual deep-water production rate in the Greenland Sea for 1988–1989 was estimated from the average temperature change over the region occupied by the tomographic array to be about 0.1 106m3 s1.

with baroclinic (internal) tidal displacements and of barotropic tidal currents, respectively. The availability of global sea-surface elevation data from satellite altimeter measurements has made possible the development of improved global tidal models. Tomographic measurements of tidal currents made in both the central North Pacific and western North Atlantic Oceans have shown that the harmonic constants for current derived from a recent global tidal model (TPXO.2) are accurate to a fraction of a millimeter per second in amplitude and a few degrees in phase in open ocean regions (Figure 8). Small, spatially coherent differences between the modeled and measured harmonic constants are found in the western North Atlantic near complicated topography that is unresolved in the model. These differences are almost certainly due to errors in the TPXO.2 currents. The integrating nature of the tomographic measurements suppresses short-scale internal waves and internal tides, providing tidal current measurements that are substantially more accurate than those derived from current-meter data. Tomographic measurements of sound-speed fluctuations at tidal frequencies from the same experiments revealed large-scale internal tides that are phase-locked to the barotropic tides. Prior to these measurements it had commonly been assumed that midocean internal tides are not phase-locked to the barotropic tides (except for locally forced internal tides) and have correlation length scales of order only 100–200 km. The measurements in the North Pacific were consistent with a large-scale, phaselocked internal tide that had been generated at the Hawaiian Ridge and then propagated to the tomographic array over 2000 km to the north (Figure 9). These observations were subsequently confirmed from satellite altimeter data. The measurements in the western North Atlantic revealed a diurnal internal wave resonantly trapped between the shelf just north of Puerto Rico and the turning latitude for the diurnal K1 internal tide, 1100 km distant at 30.01N (Figure 10). In both cases the peak-to-peak temperature variations associated with the maximum displacement of the first baroclinic modes were only about 0.041C. Once again, the acoustic observations of the baroclinic tide average in range and depth, suppressing internal-wave noise and providing enhanced estimates of the deterministic part of the internal-tide signal compared to measurements made at a point, such as by moored thermistors.

Barotropic and Baroclinic Tides

Sum and difference travel times from long-range reciprocal transmissions provide precise measurements of the sound-speed (temperature) changes associated

Heat Content

Acoustic methods have been used to measure the heat content of the ocean and its variability on basin


TOMOGRAPHY

51

50° N 40°

30°

20°

10°

0°

80° W

70°

50°

60°

20°

30°

40°

10°

0°

2.5 400

Model (deg)

Model (cm/s)

2 1.5 1 0.5

300 200 100 0

Zonal

0

Zonal

2.5 400

Model (deg)

Model (cm/s)

2 1.5 1 0.5 0

300 200 100 0 Meridional

Meridional 0

1 1.5 2 0.5 Measured (cm / s)

2.5

0

200 Measured (deg)

400

Figure 8 (Bottom) Comparison of the M2 current harmonic constants (amplitude and phase) in the North Atlantic Ocean derived from reciprocal acoustic transmissions (filled circles) and from current meter data (dots) with those predicted by a global tidal model derived from satellite altimeter measurements (TPXO.2). (Top) The acoustic data are from the pentagonal tomographic transceiver array deployed in the western North Atlantic between Puerto Rico and Bermuda during 1991–1992. The current-meter mooring locations are indicated by crosses. Adapted from Dushaw BD, Egbert GD, Worcester PF et al. (1997) A TOPEX/POSEIDON global tidal model (TPXO.2) and barotropic tidal currents determined from long-range acoustic transmissions. Progress in Oceanography 40: 337–367.

scales, taking advantage of the integrating nature of acoustic transmissions to rapidly and repeatedly make range- and depth-averaged temperature measurements at ranges out to about 5000 km. In the Mediterranean Sea, for example, a network of seven tomographic instruments was deployed for nine months during 1994 in the

THETIS-2 experiment, including cross-basin transmissions from Europe to Africa (Figure 11). Although it is normally difficult to obtain heat content measurements comparable to those provided by the acoustic data, in this case one of the transmission paths was intentionally aligned with the route of a commercial ship, from which expendable


52

TOMOGRAPHY

160 km

40° N

RTE87

30° Midway

20°

Hawaii

10 cm 10° km

180°

170° W

500

61

23

112

74

36

125

87

49

11

100

0 160°

150°

Figure 9 Schematic diagram showing the phase-locked internal tide generated at the Hawaiian Ridge and the triangular tomographic array deployed north of Hawaii during 1987 used to detect it. The dashed lines represent the crests of a wave with 160 km wavelength. Each leg of the tomographic array functions as a linear array with maximum sensitivity to an incident plane wave propagating perpendicular to the leg (i.e., with wave crests aligned parallel to the leg). The beam pattern (in dB) of the 750-km northern leg for a model-1 incident wave with a wavelength of 160 km is indicated. The satellite altimeter data that subsequently confirmed the tomographic observations are also shown. High-pass filtered M2 surface elevations (cm) are plotted along ten ascending TOPEX/ POSEIDON ground tracks. Adapted from Dushaw BD, Cornuelle BD, Worcester PF, Howe BM and Luther DS (1995) Barotropic and baroclinic tides in the central North Pacific Ocean determined from long-range reciprocal acoustic transmissions. Journal of Physical Oceanography 25: 631–647; Ray RD and Mitchum GT (1996) Surface manifestation of internal tides generated near Hawaii. Geophysical Research Letters 23: 2101–2104.

bathythermograph (XBT) measurements were made at two-week intervals. The acoustic average of potential temperature between 0 and 2000 m depth over the 600-km path and the corresponding XBT average between 0 and 800 m depth agreed within the expected uncertainty of 0.031C (Figure 11). Further, the evolution of the three-dimensional heat content of the western Mediterranean estimated from the acoustic data was found to be consistent with the integral of the surface heat flux provided by European Centre for Medium-range Weather Forcasts (ECMWF), after correction for the heat flux through the Straits of Gibraltar and Sicily (Figure 11). The acoustic data were subsequently combined with satellite altimeter data and an ocean general circulation model to generate a consistent description of the basin-scale temperature and flow fields in the western Mediterranean and their evolution over time. Acoustic and altimetric data are complementary for this purpose, with the acoustic

data providing information on the ocean interior with moderate vertical resolution and the altimetric data providing detailed horizontal coverage of the ocean surface. Similar measurements have been made in the Arctic Ocean in the Transarctic Acoustic Propagation (TAP) experiment during 1994 and in the Arctic Climate Observations using Underwater Sound (ACOUS) project beginning in 1999. During the TAP experiment, ultralow-frequency (19.6 Hz) acoustic transmissions propagated across the entire Arctic basin from a source located north of Svalbard to a receiving array located in the Beaufort Sea at a range of about 2630 km. Modal travel time measurements yielded the surprising result that the Atlantic Intermediate Water layer was about 0.41C warmer than expected from historical data. This result was subsequently confirmed by direct measurements made from icebreakers and submarines. Acoustic data collected on a similar path during


TOMOGRAPHY

53

USA 1000

80°

4000

e

tud

4000 o

6o

,4

69, 71 o

85

53, 70 o

2000 m

51

,3 14 o 62 o

101, 2

2

o

48

o

94, 252

6 46, 53

282

o

o

86

o

20

26

44,

9,

,3

76,

4

30

o

6

,2

76

o

53,

22°

1

o

49, 89

5

24°

30° N

25° 20°

K1

26°

ati

(A)

5500

5500

60°

gl

ch

65°

1

2

5500

Bermuda

nin

ren .T

Puerto Rico

6

3

P.R

70°

N

5

4

t ur

75°

K1

W

28°

2000

Cuba

1 6,

110 o

3

3

Caicos Is. Silver Bk.

20° 4000 m

Hispaniola

2000 m

18° (B)

Puerto Rico 72° W

70°

68°

66°

64°

_1

1

Figure 10 (A) Schematic diagram showing the predicted displacement of the lowest internal mode for the resonant diurnal (K1) internal tide north of Puerto Rico and the six-element tomographic array deployed during 1991–1992 used to observe it. The tomographic array is about 670 km in diameter. (B) The predicted displacement of the diurnal (K1) internal tide and the measured harmonic constants (amplitude and phase) for each acoustic path. Adapted from Dushaw BD and Worcester PF (1998) Resonant diurnal internal tides in the North Atlantic. Geophysical Research Letters 25: 2189–2192.


54

TOMOGRAPHY 44° N

42°

40°

38°

36°

(A)

2°W

0

2°E

4°

6°

8°

10°

12°

Potential temperature anomaly (°C)

0.15 0.10 0.05 0 _ 0.05 _ 0.10

(B)

Inversion CTD sections XBT sections

1 Feb

1 Apr

1 Jun

1 Aug

1 Oct

Potential temperature anomaly (°C)

0.15 0.10 0.05 0 _ 0.05 3-D average tomography ECMWF with straits corr. Climatology

_ 0.10 _ 0.15

(C)

April 1999 as part of the ACOUS project indicated further warming of about 0.51C, which was again confirmed by direct measurements made from submarines. Acoustic methods can provide the longterm, continuous observations in ice-covered regions that are difficult to obtain using other approaches. Finally, measurements of basin-scale heat content in the Northeast Pacific were made intermittently from 1983 through 1989 using transmissions from an acoustic source located near Kaneohe, Hawaii, and more recently from 1996 through 1999 during the Acoustic Thermometry of Ocean Climate (ATOC) project using sources located off central California and north of Kauai, Hawaii. Data from the ATOC project have shown that ray travel times may be used for acoustic thermometry at least out to ranges of about 5000 km. The estimated uncertainty in rangeand depth-averaged temperature estimates made from the acoustic data at these ranges is only about 0.01 1C. Comparisons between sea-surface height measurements made with a satellite altimeter and sea-surface height estimates derived using the range-averaged temperatures computed from the acoustic data indicate that thermal expansion alone is inadequate to account for all of the observed changes in sea level (Figure 12). Analysis of the results obtained when the acoustic and altimetric data were used to constrain an ocean general circulation model indicates that the differences result largely from a barotropic redistribution of mass, with variable salt anomalies a contributing, but smaller, factor.

1 Feb

1 Apr

1 Jun 1 Aug 1994

1 Oct

1 Dec

Figure 11 (A) Geometry of the THETIS-2 experiment in the western Mediterranean Sea, showing the instrument locations and acoustic transmission paths. The transmission path from source H to receiver W3 (heavy solid) coincided with an XBT section occupied every two weeks. (B) Range- and depthaveraged potential temperature (relative to 13.1111C) over 0– 2000 m depth and over the 600 km path from source H to receiver W3 derived from the acoustic data, from CTD data, and from XBT data. The shaded band indicates the uncertainty in the temperature estimates derived from the acoustic data. (C) Evolution of the three-dimensional average heat content for the western Mediterranean during 1994 derived from the acoustic data, from the ECMWF surface heat fluxes corrected for heat transport through the Straits of Gibraltar and Sicily, and from climatology. Adapted from Send U, Krahmann G, Mauuary D et al. (1997) Acoustic observations of heat content across the Mediterranean Sea. Nature 385: 615–617.

Appendix: Conversion from Sound Speed to Temperature Tomgraphic methods fundamentally provide information on the oceanic sound-speed and water-velocity fields. For most oceanographic purposes, however, temperature T and salinity Sa are of more interest than sound speed. Although sound speed C is a function of both T and Sa (as well as pressure), temperature perturbations are normally by far the most important contributor to sound-speed perturbations. A simple nine-term equation for sound speed due to Mackenzie is CðT; Sa; DÞ ¼ 1448:96 þ 4:591T 5:304 102 T 2 þ 2:374 104 T 3 þ 1:340ðSa 35Þ þ 1:630 102 D þ 1:675 107 D2 1:025 102 T ðSa 35Þ 7:139 1013 TD3


½37

55

cm (rms)

TOMOGRAPHY

34

l

32 30 28 26 24 22

k n o v1

20 18 16 14 12 10 8 6

v2

4 2 (A)

5

l

0

Sea surface height (cm)

_5 k

5 0 _5

5

o

0 _5 (B)

Jan 93

Jan 97

Jan 95

Figure 12 (A) The ATOC acoustic array is superimposed on a map of the root-mean-square (rms) sea level anomaly from altimetric measurements. Transmission paths from sources off central California and north of Kauai to a variety of receivers are shown. (B) The range-averaged sea-surface height anomaly along several of the acoustic sections from satellite altimeter data (solid black), inferred from the acoustic data (solid red), computed from climatological temperature fluctuations (dashed), and derived from an ocean general circulation model (solid blue). Uncertainties are indicated for the acoustic estimates. Adapted from ATOC Consortium (1998) Ocean climate change: comparison of acoustic tomography, satellite altimetry, and modeling. Science 281: 1327–1332.

where C is in ms1, T is in degrees Celsius, Sa is in parts per thousand (ppt), and D is the depth (positive down) in meters. Differentiating,

@C ¼ 4:59 0:106T þ 7:12 104 T 2 @T 1:03 102 ðSa 35Þ


½38

56

TOMOGRAPHY 0.45

where TðÞ is the reference temperature profile corresponding to the reference sound-speed profile CðÞ and DT ¼ T TðÞ. The fractional change in sound speed is then

0.40

DC=C ¼ aDT ð1 þ mb=aÞ

∂T / ∂C (°C / m s−1)

0.35

In midlatitudes a typical value for m might be 0.1(ppt)(1C)1, giving mb=a E 0:03. Thus the soundspeed perturbation DC depends to first order only on the temperature perturbation DT. The sound-speed perturbation profile DCðzÞ derived from the acoustic data can be easily converted to the corresponding temperature perturbation profile

0.30

0.25

DT ðzÞ ¼ 0.20

0.15

½44

ðCðÞþDC

@T dC; @C

½45

CðÞ

5

10

15 T (°C)

where the integral allows for the dependence of @T=@C on temperature.

20

Figure 13 The derivative @T =@C as a function of temperature. Adapted from Dushaw BD, Worcester PF, Cornuelle BD and Howe BM (1993) Variability of heat content in the central North-Pacific in summer 1987 determined from long-range acoustic transmissions. Journal of Physical Oceanography 23: 2650–2666.

See also Acoustics, Arctic. Acoustics in Marine Sediments. Deep Convection. Internal Tides. Inverse Models. Tides.

Further Reading

@C ¼ 1:34 1:03 102 T @Sa

½39

where a slight depth dependence has been dropped. The derivative @T=@C ¼ 1=ð@C=@TÞ varies significantly with temperature (Figure 13). To first order, the fractional change in sound speed is then DC=C ¼ aDT þ bDSa

½40

a¼

1 @C E 2:4 103 ðo CÞ1 C @T

½41

b¼

1 @C E 0:8 103 ðpptÞ1 C @Sa

½42

where

at 101C. For a locally linear temperature–salinity relation, Sa ¼ SaðT ðÞÞ þ mDT

Khil’ko AI, Caruthers JW, and Sidorovskaia NA (1998) Ocean Acoustic Tomography: A Review with Emphasis on the Russian Approach. Nizhny Novgorod: Institute of Applied Physics, Russian Academy of Sciences. Munk W, Worcester P, and Wunsch C (1995) Ocean Acoustic Tomography, Cambridge: Cambridge University Press. (The review given here draws heavily from, and uses the same notation as, this monograph, which provides a comprehensive account of the elements of oceanography, acoustics, signal processing, and inverse methods necessary to understand the application of tomographic methods to studying the ocean.) Munk Wand Wunsch C (1979) Ocean acoustic tomography: a scheme for large scale monitoring. Deep-Sea Research 26: 123--161. Worcester PF (1977) Reciprocal acoustic transmission in a mid-ocean environment. Journal of the Acoustical Society of America 62: 895--905.

½43


TOPOGRAPHIC EDDIES J. H. Middleton, The University of New South Wales, Sydney, NSW, Australia Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2986–2993, & 2001, Elsevier Ltd.

Introduction Topographic eddies in the ocean may have a range of scales, and arise from flow separation caused by an abrupt change in topography. This abrupt change may be of large scale, such as a major headland, in which case the topographic eddy is essentially a horizontal eddy of scale many tens of kilometers in a shallow coastal ocean. Eddies also occur at much smaller scales when ocean currents flow around small reefs, or over a rocky seabed. In this case the topographic eddies are perhaps only meters or centimeters in scale. A rule of thumb is that topographic eddies are generated at the same length scales as the generating topography. Perhaps the earliest recorded evidence of a topographic eddy in the ocean comes from Greek mythology, where there is mention of a whirlpool occurring beyond the straits of Messina, between Sicily and Italy. Jason and the Argonauts in their vessel the Argo had to find the path between the Cliff known as Scylla, and the whirlpool having the monster Charybdis. The whirlpool still exists and occurs as the tides flood and ebb through the narrow straits. Another well-known tidal whirlpool, intensified at times by contrary winds and often responsible for the destruction of small craft, occurs off the Lofoten Islands of Norway. The Norwegian word maelstrom is associated with the whirlpool. More recently, the fishing and marine lore of the Palau District of Micronesia, and the knowledge of ocean currents held apparently for hundreds, if not thousands, of years has been investigated. In fact, it was found ‘The islanders had discovered stable vortex pairs and used them in their fishing and navigation long before they were known to science.’ A sketch, drawn from the fishermen’s description, is shown in Figure 1. The flow appears to comprise a stable vortex pair in the lee of the island, with identifiable zones of rougher water which would result from the conflicting directions of currents and waves, and calmer waters directly upstream from the island. A most interesting feature is the description of concentrations of tuna and flying fish, which clearly

have a preference for congregating in certain zones, perhaps because there they find food more prevalent. This diagram underscores a fundamental importance of topographic eddies; they serve not only as a feature of the circulation, but also to provide preferential environments for the marine biota. Although such whirlpools, eddies, and wakes had been well known for centuries by seafarers, in the early 1900s pilots and aerodynamicists ‘rediscovered’ eddies. The additional feature that they discovered was that eddies and wakes draw their energy from the mean flow, and provide a ‘form drag’ on the incident flow, tending to slow it down. In the ocean, topographic eddies (or recirculating flows) comprise horizontal eddies generated by coastal currents flowing past coastal headlands, coral reefs, islands, or over undersea hills or ridges. They are important as they profoundly affect not only the

HAPITSETSE (very rough)

Daily tuna migration route ARM (rough)

(calm)

Flying fish concentrated along here

Launch canoes here Current fastest here

ISLAND REEF

SURIYOUT (calm) good fishing Prevailing current Figure 1 Island Wake of Tobi in Micronesia, showing flow patterns and concentrations of tuna and flying fish. (Reproduced from Johannes, 1981.) , Tuna concentrations; J, Flying fish concentrations.


57

58

TOPOGRAPHIC EDDIES

horizontal distribution of nutrients, pollutants, bottom sediments and biota through direct horizontal transport, but also the vertical distribution through the associated three-dimensional flow field. In addition, a cascade of turbulent energy to smaller scales provides a continued source of smaller-scale turbulence, which itself acts to further diffuse and transport such matters. Topographic eddies may also be produced by smaller-scale reefs (submerged wholly or partly) with scales typical of the width and height of the reef, in which case the turbulent eddies are fully three-dimensional in nature. These eddies are also unstable and break down into turbulence of progressively smaller scale, stirring the ocean, and creating strong spatial gradients, which enhance mixing and diffusion of passive materials. Perhaps it is appropriate now to discuss the terms turbulence and diffusion. Turbulence refers to a state of flow which is chaotic and random in its detail, such that any instantaneous state of flow will not ever be reproduced at any later time. However, there may be underlying physics which imply that measurements of properties made at any point will, after much averaging over time, produce an average which is predictable and reproducible. Diffusion refers to the stirring and mixing of waters as they flow in a turbulent manner. Diffusion tends to smear out or dilute unusually large concentrations of some property, such as a pollutant. The stronger the turbulence, the more rapid the diffusion.

Larger Scale Topographic Eddies Many coastal headlands protrude several kilometers into coastal currents, where the ocean depth is often less than 100 m or so. The coastal currents may be tidally induced, changing over a period of 12 h or so, or may be relatively steady, changing perhaps only once every 7–10 days as a result of local synoptic scale atmospheric systems (see Wind Driven Circulation), or as a result of coastal trapped waves. Any resulting eddies are somewhat two-dimensional, with horizontal size many times that of vertical size, and occur downstream of the headland. The generation of such recirculating flows or eddies has traditionally been considered to occur as follows. Flow separation occurring as a coastal current passes a headland is a result of the inability of the pressure field to allow the flow to follow the coastal contours, resulting in an adverse pressure gradient at the boundary. The separated flow has a very strong shear layer (with high vorticity), and a large-scale eddy may form, and either remain

attached to the headland, or be carried downstream. In some cases, a string of eddies (known as a vortex street) may be generated. Figure 2 shows a characteristic flow pattern behind Bass Point (near Sydney), with an overall larger-scale wake pattern, superimposed on which there are a number of smaller eddies. There are several dimensionless numbers which represent various balances between physical processes, and hence terms in the equations of motion, which have been proposed in an attempt to simplify the physical balances which exist. For example, classical laboratory studies of the breakdown to turbulence have utilized the Reynolds number Re ¼ UL=v where U is the scale for the incident flow, L is the horizontal scale of the obstacle (reef or headland), and n is the kinematic viscosity of the fluid. For example, Reynolds numbers between 4 and 40 for two-dimensional flows around a circular cylinder indicate a trapped and steady recirculating eddy-pair. For Re > 40 the trapped eddy-pair maintains its presence, but the downstream wake begins to become unstable. At Reynolds numbers larger than Re ¼ 80, the eddy-pairs are swept downstream as a von Karman vortex street. Reports of such studies invariably cite the need to have no ‘environmental noise’ in the system to ensure reproducibility of the wake at low Re , that is, a perfectly smooth incident flow upstream. Field observations show some features which are at first sight similar to the laboratory observations, however, characterization of wakes and eddies based on the Reynolds numbers (and/or other simple dimensionless parameters) have often produced conflicting results. For field observations, relevant dimensional quantities include the incident current speed U, the distance the headland protrudes into the free stream L, the Coriolis parameter f and the water depth D. Vertical density stratification plays a role in deeper water, and the effects of the winddriven surface layer and the frictional bottom boundary layer play a role in shallower water. Quantities arising from the flow itself are the horizontal eddy diffusivity nx and vertical eddy diffusivity nz associated with horizontal and vertical turbulent diffusion, respectively. Values of nx are usually several orders of magnitude greater than values of nz for most oceanic flows, indicating that horizontal diffusion dominates over vertical. The assumption that turbulent Reynolds stresses are proportional to the mean velocity gradient allows an eddy-diffusivity approximation for the mean components of a turbulent flow. Reynolds numbers


TOPOGRAPHIC EDDIES

59

Figure 2 Attached eddies and vortex street in the wake of Bass Point as simulated by a computer model. (Reproduced from Denniss et al., 1995.) The domain width is 6.67 km, and the maximum current vector, as indicated by the longest arrow, is 0.42 m s1.

for oceanic flows are then evaluated using the horizontal eddy diffusivity nx rather than the molecular viscosity. As an example, for flows around Bass Point Sydney, Re ¼ UL=nx B1000 using the overall headland width, and Re ¼ 5–10 for smaller-scale eddies produced by reefs at the tip of the headland, where LB100 m and nx B15 m2 s1. Thus there are at least two different scales of topographic eddies in the recirculation processes depicted in Figure 2. Other relevant dimensionless parameters include the Rossby number Ro ¼ U=fL which gives a ratio of advective acceleration (nonlinear) terms to Coriolis terms in the momentum equations. The Coriolis parameter f denotes the local rate of the earth’s rotation about the vertical axis. Low Ro flows (Ro 51) have the background rotation of the earth controlling the dynamics, with relatively slow flows, and a tendency to stable flow patterns. High Ro flows (Ro B1) have a stronger tendency to produce eddies, as the non-linear terms which characterize energetic flows tend to dominate. Most larger-scale flows in the ocean have Ro 51, indicating the rotation of the earth is a dominating effect, whereas for the Bass Point example described above,

Ro B1, indicating that the advective acceleration terms may be strong enough to produce eddies. Derived parameters include the bottom boundary layer or Ekman layer thickness d, which scales as dBð vz =f Þ1=2 This height is a measure of the vertical extent above the bottom where the flow is affected by transfer of vertical stresses. This results in a deceleration of current from the free stream value U in the flow above to zero at the sea bed. In this bottom boundary layer, currents will change in direction, turning to the left (right) in the Northern (Southern) Hemisphere as the seabed is approached from above. If the bottom depth Dbd, then the boundary layer provides a frictional decay on the overall flow. If, however, DBd, then the bottom turbulent layer dominates the entire water column, and somewhat different dynamics follow. The vertical Ekman number giving the ratio of vertical momentum diffusion terms to the Coriolis term is E ¼ vz =fHd Thus E may be interpreted as a ratio indicating relative importance of bottom frictional effects and those


60

TOPOGRAPHIC EDDIES

due to the Earth’s rotation. High E values are indicative of flows in which bottom friction dominates the flow (very shallow flows or flows with high vertical eddy viscosity), whereas E51 is indicative of deeper flows, or flows where bottom friction is less effective. The importance of an island wake parameter P (or its square root) defined by P ¼ UD2 =vz L is discussed by several authors as being the relevant parameter (see Island Wakes) to describe a wake some distance downstream; it is essentially a Reynolds number based on vertical eddy diffusivity rather than horizontal. A survey of data from a range of island wakes indicates that for P51 the current simply flows around the headland with no recirculating eddies, for PB1 the wake is steady and stable, and for Pb1 eddy shedding is observed. For very shallow water flows where bottom stress is dominant, a summary of data show that a ratio of Rossby to Ekman numbers defined by Ro =Ek ¼ UDd=vz L is perhaps a better parameter than P with eddy shedding for large numbers (Ro =Ek > 500), steady eddy formation for Ro =Ek B100 and fully attached eddies for Ro =Ek o10. In shallow waters where DBd, these parameters (P and Ro =Ek ) are essentially the same. The use of dimensionless numbers to characterize flows is based on the assumptions that the essential processes are characterized by simple dynamical balances which will hold essentially throughout the domain of interest. However, for unsteady, nonlinear flows the balances are dependent on both location and time. Thus the Reynolds and Rossby numbers above are a measure of the flow balance in the upstream region. Reynolds numbers higher than some critical value imply that the resultant downstream flow is unsteady and chaotic, and is fundamentally different from the steady flow which occurs at lower Reynolds numbers. By contrast, the island wake parameters P and the Ro =Ek represent physical balances of the wake downstream of the headland. The bottom boundary layer thickness and Ekman numbers are properties of the vertical profile of the flow at any location. A major feature, recognized in the early laboratory experiments was that flow stability at low Reynolds numbers was dependent on the absence of smallscale, rapidly changing background variability (referred to here as stochastic noise) occurring in the incident or upstream flow. However, it has been

demonstrated theoretically that transition of the larger-scale flow to an unsteady chaotic system can be linked to the system’s amplification of background stochastic noise. In the case of recirculating headland eddies, such stochastic noise might be due to variations in wind stress, wave activity (internal and surface), and nonlinear small-scale high-frequency turbulence caused by flow over or around bottom topography such as submerged or semisubmerged reefs. Support for these ideas is provided by analyses of data from Bass Point. It is hypothesized that the turbulence generated by the smaller-scale reefs at the tip of Bass Point creates a turbulent horizontal shear layer. This pushes the flow separation point downstream, inhibiting the formation of a larger-scale attached eddy except under strong incident current conditions. Thus the smaller-scale turbulence at the tip of the Point has a substantial effect on the largerscale wake flow. The small-scale turbulence is characterized in strength by a horizontal eddy diffusivity nx , and since nx is absent from the above wake parameters, the wake parameters cannot be definitive in terms of flow description. Thus it can be concluded that simulations or predictions of flow behavior based on dimensionless numbers alone, traditional stability analyses, or direct (nonlinear) numerical simulation may fail without exact knowledge of the background environmental stochastic noise. The above description of flows is essentially applicable to cases of steady upstream inflows, however, in many coastal regions the tidal flood and ebb creates an alongshore current which floods and ebbs in opposite directions alongshore. In this case, there may be transient eddies generated at each half tidal cycle at every headland. The schematic diagram given here in Figure 3 illustrates the point. Eddies are generated on each cycle, and sit either side of the headland, with a residual flow always directed offshore at the tip of the point (Figure 3A); depth variability offshore ensures that rotational motions are generated by differential bottom friction (Figure 3B). A simple headland eddy is shown in Figure 3C, and a vortex street in Figure 3D. The dynamics that allow generation of topographic eddies do not necessarily control their subsequent motion, the turbulent energy cascades or their ultimate dissipation. For topographic eddies in coastal waters, the presence of bottom boundary layers renders the flow partly three-dimensional, and so energy cascades to smaller and smaller scales as eddies break up. Vertical eddy viscosity in the bottom boundary layer, caused by friction at the seabed, also extracts energy from the system, creating a form drag on coastal currents.


TOPOGRAPHIC EDDIES

ebb

flood

flow downstream with Fr o1, are also known to occur in strongly flowing rivers. Flows which are density stratified are characterized by the flow speed U and the buoyancy frequency N, where N is defined by

(A)

N2 ¼

residual shallow

61

gdr rdz

In flows of depth D, the dimensionless number which reflects the ratio of current speed to wave speed is

deep

Fr ¼ U=ND

(B)

(C)

(D) Figure 3 The possible mechanisms for the generation of headland eddies in a tidally cyclic flow (A), a steady flow where the depth increases offshore (B), and cases where a steady flow induces a simple attached eddy (C) or a train of separated eddies (D). (Reproduced from (Robinson 1975.)

Topographic Eddies Due to Bottom Topography in Stratified Flows A dimensionless number which directly gives the ratio of current velocity U to the velocity of gravity waves in a current of depth D is known as the Froude number and is defined by Fr ¼ U=ðgDÞ1=2 The Froude number is the definitive number which divides a physical process where a disturbance may propagate upstream (called subcritical and denoted by Fr o1), or a disturbance is swept downstream (called supercritical and denoted by Fr > 1). Flows over shallow sills, or coral reefs, in areas of strong tidal flow (e.g., north-western Australia) may sometimes be so rapid as to be supercritical. Hydraulic jumps, caused by a flow transition from rapid smooth flow upstream with Fr > 1 to slow turbulent

Flows over and around obstacles depend not only on this internal Froude number, but also on the height H of the obstacle, its horizontal size and the steepness of the topography. Internal hydraulic jumps are also known to exist in the ocean where very strong tidal currents flow over steep topography, such as the Mediterranean outflow, or off the British Columbia coast (Figure 4). The subsequent turbulence is confined downstream causing a high level of mixing and turbidity, while upstream waters remain relatively placid and clear. Since such hydraulic jumps usually occur in irregularly shaped channels, they are often also the source of small topographic eddies. Consideration of stratified flow around obstacles having an infinite value Ro (zero Coriolis parameter) provides many examples of the generation of topographic edies. These include hydraulic jumps, exchange flows, waves and recirculating flows over two and three-dimensional obstacles in finite depth and infinitely deep stratified flows. In water much deeper than the obstacle height H, the relevant height scale is H, and the relevant dimensionless parameter is NH=U, an inverse Froude number based on the obstacle height. An interesting case study is depicted in Figure 5, for stratified flow with parameter NH/U ¼ 5. In this case the stratification is sufficiently strong to confine recirculation patterns to within about two obstacle heights of the seabed, with flow going both over and around the obstacle, and generating a pair of steady attached eddies. The dynamics are extremely complex, and consist of nodes where the flow separates, and stagnation points where the flow has zero current. The flows described have zero background rotation (i.e., f ¼ 0), and thus cannot describe a range of eddy-like flows, trapped above an obstacle in a current flow in a rotating reference frame and known as Taylor columns. For stratified flows over typical ocean seamounts in which the earth’s rotation plays a


62

TOPOGRAPHIC EDDIES 0

20

Capping surface layer

15

19

20

21

Depth (m)

22.5

24 23

Subcritical flow

23.5 24.2

40

15

16 22

Entraining interface

Weakly stratified layer

24

60 Internal hydraulic jump

24.2

80

100 _ 800 E

24.4

_ 600

_ 400

_ 200

0 Distance (m)

200

400 W

Figure 4 Schematic diagram of an internal hydraulic jump, in which the upstream flow (at left) is slow (with Fr o1=mn) accelerates rapidly down a steep slope (with Fr > 1=mn), and flows into a turbulent hydraulic jump (with Fr o1=mn) (Reproduced from Farmer and Armi, 1999.) The downstream turbulence cannot propagate up the steep slope as the stratification N is not sufficiently large for the internal wave speed ND to exceed U, and so the turbulent flow is confined downstream of the topographic slope.

in the form Bu ¼ ðNH=fLÞ2 ¼ ðRo =Fr Þ2 is known as the Burger number. Bu is also the ratio of Rossby number to Froude number squared. The Burger number is thus an indication of the balance of effects of stratification and earth’s rotation, adjusted for the vertical aspect ratio of the seamount. High values of Bu tend to keep topographic disturbances more confined vertically, whereas low values permit taller Taylor columns, in which the effects of the obstacle extend higher in the water column. A full description of Taylor columns above seamounts in stratified flows is beyond the scope of this article.

(A)

Turbulence Due to Small-scale Bottom Topography and Reefs

(B) Figure 5 Topographic eddy in stratified flow over a threedimensional obstacle with NH/U ¼ 5 (reproduced from Baines, 1995), showing the side view through a cross-section along the line of symmetry (A), and the plan view showing the horizontal current components at a depth below the top of the obstacle (B). Also shown in (B) is the zone in which upwelling occurs at that same level (hatched).

role, the relevant scale of vertical disturbance above the seamount is fL=N, where L is the horizontal scale of the seamount. The ratio of the vertical scale of the obstacle H to this scale height is thus NH=fL, which

For topography whose roughness scales are much smaller than the depth, and timescales are short, then the Ro number is high and Coriolis effects are negligible. Flow around such topography then has properties typical of those found in laboratory experiments, allowing for even smaller-scale turbulence to act as a stochastic noise at the inflow region. A number of topographic eddies may then be formed at different times and/or places, sometimes creating a wholly turbulent flow field over a limited region of the coastal ocean.


TOPOGRAPHIC EDDIES

Such turbulence may be caused by strongly flowing tidal currents over a rough seabed or over and through coral reefs, for example, where the roughness scales may be as small as a few centimeters. These flows may scour the seabed, raising sediments off the seafloor and transporting them elsewhere. Such turbulence can also act to thoroughly mix the water column in areas where strong tidal currents flow over significant bottom topography. This occurs, for example, over submerged coral reefs at 80 m of water depth offshore from Hydrographers Passage in the Great Barrier Reef where phytoplankton multiply rapidly as a consequence of the combination of nutrient supply and light. Turbulence on these scales is still subject to the energy cascade phenomenon, whereby eddies continually break down to smaller and smaller eddies until fluid viscosity damps out the motions.

Biological Implications of Topographic Eddies As depicted in Figure 1, there is clearly a relationship between the wake of Tobi Island and the fish concentrations, as described by the local fishermen. However, there are also some much more subtle responses. These are related to the vertical circulation which is necessarily part of a horizontally circulating eddy. The physics is relatively straightforward. In a horizontal eddy, the eddy can only maintain its structure if the eddy center has low pressure, which exists by virtue of a reduced sea level in the eddy center. Throughout the main part of the water column (away from the seabed) the horizontal pressure gradient balances the centripetal force. However, in coastal waters the pressure gradient has an effect right down through the bottom boundary layer to the seabed. In this bottom boundary layer, the centripetal force is reduced because the velocity is reduced, and so the pressure gradient drives a flow

Figure 6 Topographic eddy in the coastal ocean showing inflow in the bottom boundary layer, upwelling in the eddy center, and outflow at the surface.

63

toward the center along the seabed. This flow then upwells in the eddy center (Figure 6), and outflows on the surface. The upwelling brings with it fine sediments, nutrients, plankton, and perhaps larval fish. The combination of nutrients, plankton and greater light can enhance plankton growth. Thus eddies in shallow waters are intrinsically places of enhanced plankton growth, and perhaps enhanced concentrations of other elements of the marine food chain. In deeper waters, where the ocean is stratified, the lower pressure at the eddy center also results in a general uplift of deeper nutrient-rich waters, creating the same effect as in shallower waters. Observations in the wake of Cato Reef off eastern Australia showed higher concentrations of nutrients, phytoplankton, and larval fish of better condition, and the principal effects of this increased productivity were attributed to the uplift in the wake. Referring back finally to Figure 1, the diagram can now be interpreted. The arms along which the flying fish and tuna concentrate are zones in which upwelled nutrient-rich waters, now outflowing from the eddy centers, meet the nutrient poor free-stream currents. This is likely to be a zone where plankton in the free stream now have an opportunity to grow, and the larger fish are perhaps benefiting from the enhanced primary productivity.

See also Coastal Trapped Waves. Island Wakes. Mediterranean Sea Circulation. Mesoscale Eddies. Small-Scale Physical Processes and Plankton Biology. Three-Dimensional (3D) Turbulence. Wind Driven Circulation.

Further Reading Baines PG (1995) Topographic Effects in Stratified Flows. Cambridge: Cambridge University Press. Batchelor GK (1967) An Introduction to Fluid Dynamics. Cambridge: Cambridge University Press. Boyer DL and Davies PA (2000) Laboratory studies in rotating and stratified flows. Annual Reviews of Fluid Mechanics 32: 165--202. Coutis PF and Middleton JH (1999) Flow topography interaction in the vicinity of an isolated deep ocean island. Deep-Sea Research 46: 1633--1652. Denniss T and Middleton JH (1994) Effects of viscosity and bottom friction on recirculating flows. Journal of Geophysical Research 99: 10183--10192. Denniss T, Middleton JH, and Manasseh R (1995) Recirculation in the lee of complicated headlands; a case study of Bass Point. Journal of Geophysical Research 100: 16087--16101.


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Farmer D and Armi L (1999) Stratified flow over topography; the role of small-scale entrainment and mixing in flow establishment. Proceedings of the Royal Society of London A455: 3221--3258. Farrell BF and Ioannou PJ (1996) Generalized stability theory. Part I: Autonomous operators. Journal of the Atmospheric Sciences 53: 2025--2040. Huppert HE (1975) Some remarks on the initiation of internal Taylor columns. Journal of Fluid Mechanics 67: 397--412. Johannes RE (1981) Words of the Lagoon: Fishing and Marine Lore of the Palau District of Micronesia. San Diego: University of California Press. Kundu PK (1990) Fluid Mechanics. London: Academic Press. Middleton JH, Griffin DA, and Moore AM (1993) Ocean circulation and turbulence in the coastal zone. Continental Shelf Research13143--13168.

Pattiaratchi C, James A, and Collins M (1986) Island wakes; a comparison between remotely sensed data and laboratory experiments. Journal of Geophysical Research 92: 783--794. Rissik D, Taggart C, and Suthers IM (1997) Enhanced particle abundance in the lee of an isolated reef in the south Coral Sea; the role of flow disturbance. Journal of Plankton Research 19: 1347--1368. Robinson IS (1975) Tidally induced residual flows. In: Johns B (ed.) Physical Oceanography of Coastal and Shelf Seas. Amsterdam: Elsevier. Tennekes H and Lumley JL (1972) A First Course in Turbulence. Cambridge, MA: MIT Press. Tomczak M (1988) Island wakes in deep and shallow water. Journal of Geophysical Research 93: 5153--5154.


TOWED VEHICLES I. Helmond, CSIRO Marine Research, TAS, Australia Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 2994–3003, & 2001, Elsevier Ltd.

Introduction As a platform for marine instruments the towed vehicle (often termed a ‘towfish’ or ‘towed body’) combines the advantages of a ship-mounted instrument that gathers surface data while under way, and an instrument lowered from a stationary ship to gather data at depth. This article discusses the types of vehicles, the significance of the hydrodynamic drag of the tow cable and methods to reduce it. It also outlines the basic hydrodynamics of towed vehicles and presents the results of a model of the vehicle/cable system, indicating depths and cable tensions for a typical system.

•

Types of Towed Vehicles A towed vehicle system has three main components: the vehicle, the tow cable and a winch. The vehicles fall into two broad categories: those with active depth control and those without. Vehicles With Active Depth Control

Depth-controlled towed vehicles (or ‘undulators’) can move vertically in the water column while being towed horizontally by the ship. The main advantage they have over the lowered instrument is that they can quickly and conveniently measure vertical profiles of ocean properties with high horizontal spatial resolution. The main disadvantage is that it is difficult to reach depths greater than 1000 m while being towed at useful speeds; most available systems are limited to 500 m at best. The reason for the depth limitation is that the hydrodynamic drag of the tow cable must be overcome by a downward force on the vehicle, produced either by weight or a downward hydrodynamic force from hydrofoils or wings. However the cable’s strength limits the allowable size of these forces. The following are the common types of depthcontrolled vehicles.

•

A vehicle with controllable wings towed with an electromechanical cable that connects it to a

•

controlling computer on board the vessel. The electromechanical cable allows the data to be transmitted to the ship and displayed and processed in real time. This can be an advantage when following a feature such as an ocean front; the ship’s course can be adjusted to optimize the data collection. It also has the advantage of enabling fast, real-time response, an important consideration for bottom avoidance when operating in shallow water. As with most towed vehicles, cable drag dominates performance, so cable fairing is commonly used to reduce drag. Examples of this type of towed vehicle are the Batfish (Guildline Instruments, Canada), SeaSoar (Chelsea Instruments, UK) and the Scanfish (MacArtney A/S, Denmark). A vehicle with controllable wings that is totally self-contained. It is preprogrammed for maximum and minimum depths and records the data internally. As such a vehicle can be towed on a simple wire rope, it is convenient to use on ships not equipped for research. The lack of real-time control and data can be a disadvantage. An example of this type is the Aquashuttle (Chelsea Instruments, UK). A passive vehicle, often with fixed wings, where changes in depth are made by winching the tow cable in and out. This necessitates a high-speed, computer-controlled winch to produce the depth variation, but the vehicle can be simple. A recent development of this type is the Moving Vessel Profiler (Brooke Ocean Technology Ltd, Canada). This system has a winch that spools out the cable fast enough to allow the vehicle to free fall while the ship is under way, and then retrieves it after it has reached its maximum depth, which may be as deep as 800 m. Because the profiler free-falls on a slack cable, the usual cable drag constraints are not as relevant. This allows good depths to be achieved without the complication of cable fairing.

Vehicles Without Active Depth Control

The vehicle without active depth control produces the depressing force by means of its weight, fixed wings or both. It maintains a constant depth for a given tow-cable length and tow speed. The vehicle can be towed with either an electromechanical cable to provide real-time data to the ship (as used for underwater survey instruments such as the side-scan sonar) or with a wire rope (as often used for plankton recorders).


65

66

TOWED VEHICLES

Tow cables are either wire rope or double-armored electromechanical cables. In the latter, two layers of armor provide mechanical strength, and because the layers are wound in opposite directions they are approximately torque balanced, i.e., the cable has little tendency to rotate when loaded. The electrical conductors handle data and power and optical fibers are used for high data rates. Except for systems with short cables used for shallow operations the tow cable is usually the dominant part of a towed vehicle system. Even a modest cable of 8 mm diameter and 500 m length has a mass of about 150 kg and a longitudinal cross-sectional area of 4 m2. This large cross-sectional area means that the cable drag dominates the performance of the system. Drag Caused by Flow Normal to the Cable

The normal drag on a moving body in a fluid is given by DN ¼ CDN 12ru2N A For a cable, the cross-sectional area A is the product of the cable’s diameter (d) and length (l) (see list of symbols at end of article). This drag is the sum of drag due to the shape of the body (the form or pressure drag) and drag due to surface friction (additionally a shape that produces lift also generates induced drag). The value of the drag coefficient CDN depends on the Reynolds number Re (the ratio of inertial to viscous forces), which is defined as Re ¼ ud=v. For a long smooth cylinder with normal flow at Reynolds numbers less than about 3 105 the flow is laminar and CDN E 1:2. At higher Reynolds numbers, the flow becomes turbulent and CDN drops to about 0.35. This change in drag coefficient is due to the large area of separated flow in the wake of the cylinder in laminar flow decreasing when the flow becomes turbulent. For most cables used for towed vehicles, the value of the Reynolds number is less than 105. To exceed this value a 10 mm-diameter cable moving through water requires a normal velocity greater than 10 m s1. For cables with a rough surface, such as the usual stranded cable, CDN E 1:5 when 103oReo105 (Figure 1). There is a mechanism that increases the drag coefficient of a cable above the value of a rigid cylinder. This is vortex-induced oscillation, commonly referred to as ‘strumming’. Vortices shed from the region of flow separation alter the local pressure distribution and the cable experiences a time-varying force at the frequency of the vortex shedding. This frequency, f , is a function of the normal flow velocity uN and the

Drag coefficient (CD)

Tow Cable Drag

10 5 2 1 0.5 0.2 0.1 0.05 0.02 0.01 0.005 0.002 0.001

CDN

Cable Smooth cylinder

CDT

1

10

2

3

4

10 10 10 Reynolds number (Re)

5

10

6

10

Figure 1 Normal and tangential drag coefficients for a typical cable.

cable diameter d. The Strouhal number Sn is defined as Sn ¼ fd=uN , and Sn E 0:2 over the range of Reynolds numbers 102 oRe o105 . If this frequency is close to a natural frequency of the cable, an amplified oscillation occurs. A tow cable has many natural frequencies and there are many modes excited by vortex shedding; the result is a continuous oscillation of the cable. These vortex-induced oscillations, which can have an amplitude of up to three cable diameters, increase cable fatigue and drag. The increase in drag can be as much as 200%, resulting in drag coefficients as high as 3. Values around 2 are common. The values of the drag coefficient for towed cables cited in the literature differ widely because each case has its own set of conditions: cable curvature, tension, Reynolds number and surface roughness vary from case to case. A good starting point for estimating the normal cable drag is CDN E 1:5 for cables that are not strumming and CDN E 2 for strumming cables. For the towed vehicle to be able to dive, the normal cable drag force must be overcome by downward or depressing forces: the cable weight, the towed vehicle weight and the hydrodynamic forces produced by the vehicle. The normal drag does not contribute directly to cable tension but by influencing the angle of the cable in the water it determines the component of cable weight that contributes to tension. Drag Caused by Flow Tangential to the Cable

The tangential drag on a long cylinder is due to surface friction. It is given by: DT ¼ CDT 12ru2T pA For tow cables with Reynolds numbers greater than about 103, a typical value for the tangential


TOWED VEHICLES

drag coefficient (CDT) E 0.005 (Figure 1). With a towed vehicle system, the tangential drag contributes significantly to cable tension but has little influence on the depth achieved.

Reduction of Cable Drag Normal cable drag can be reduced by an attachment that gives the cable a streamlined or ‘fair’ shape. Alternatively the attachment can be designed to split the wake, so that the shed vortices cannot become correlated over a significant length of cable and strumming is prevented. These attachments are usually called ‘fairing’. Rigid Airfoil-shaped Fairing

‘Wrap-round’ fairing is the most effective method of reducing normal drag. An example is shown in Figure 2. A good airfoil shape can have a normal drag coefficient of 0.05, but the practicalities of having a rigid moulded plastic shape that can wrap around a cable, be passed over sheaves and spooled onto a winch often results in a drag coefficient of about 0.2. The greater drag is primarily due to the circular nose of the fairing (to accommodate the cable) and gaps between fairing sections. Because of the large surface area of the fairing, the tangential drag coefficient (based on cable surface area) is about 0.05. This means that, although the normal drag coefficient of a faired cable is only a tenth of a bare cable, the tangential drag coefficient is about ten times greater. A consequence of this is large cable tensions. For a typical system with 500 m of faired cable, at least 50% of the cable tension can be due to the fairing.

Cable

Another consequence of the high tangential drag is that this loading must be transferred from the fairing sections to the cable. Every 2–3 m, a ‘stop ring’ is swaged or clamped to the cable to take the load. If this force accumulates over too great a length the fairing sections will not rotate freely, and in the extreme they can break under the high compressive load. Another form of rigid fairing is the ‘clip-on’ fairing (see Figure 2). Unlike the ‘wrap-round’ type this does not totally enclose the cable, but is essentially an after-body attached to the cable with clips. Because of the gap between the cable and the fairing the drag coefficient is higher. Typical values are CDN E 0.4. A problem that can occur with rigid fairing is the phenomenon of ‘tow-off’. If the fairing sections are not free to align accurately with the flow, they can generate a considerable lift force, which can cause the cable to tow off to the side and decrease the depth it achieves. Although the rigid airfoil fairing is the most effective method of decreasing drag, it comes at a high cost. Not only is it expensive but it also requires special winches and handling gear. However, if a system is set up well it gives reliable performance. Flexible Ribbon and ‘Hair’ Fairing

Ribbon fairing is made of a flexible material in the form of trailing ribbons (Figure 2). Fibers or ‘hairs’ attached to the cable are also effective. These fairing devices do not usually produce a fair shape, but achieve their effect by splitting the wake. Their main effect is, therefore, to reduce strumming and reduce the normal drag coefficient to the bare cable value of

Fixed to cable

Fairing

Stop ring

Stop ring

(A)

(B)

67

(C)

Figure 2 (A) ‘Wrap-round’ fairing; (B) ‘clip-on’ fairing; (C) ‘ribbon’ fairing; (D) ‘hair’ fairing.


(D)

68

TOWED VEHICLES

about 1.5. In some cases ribbon fairing can streamline the cable, reducing the normal drag coefficient to about 0.7 (due to the reduction of form drag). Hair fairing reduces strumming but can increase the normal drag coefficient so that CDN E2. These devices increase the surface area over a bare cable so the tangential drag is increased, resulting in greater cable tensions. Flexible fairing does not need special handling gear and can be wound onto a regular winch. However, the fairing deteriorates rather quickly requiring regular repair and replacement.

L

Chord line Angle of attack

D

AC M

u

Mean camber line

Figure 4 Forces and moment on an airfoil.

M ¼ CM 12ru2 Sc

Basic Aerodynamics of the Towed Vehicle Vehicles that Generate Lift

Most towed vehicles use wings (hydrofoils) to generate the force required to pull the tow cable down. Figure 3 shows an example of a winged vehicle. As shown in Figure 4, a wing moving through a fluid experiences a force perpendicular to the direction of flow (the lift), a force directly opposing the motion (the drag), and a moment tending to rotate the wing (the pitching moment). The pitching moment is usually referenced about a point termed the aerodynamic center, chosen so that the moment coefficient is constant with angle of attack. The lift force is given by: L ¼ CL 12ru2 S The drag force is given by: D ¼ CD 12ru2 S

The lift coefficient CL is proportional to the angle of attack. The theoretical relationship for a thin, symmetrical airfoil gives the slope of the curve of lift coefficient against angle of attack dCL =da ¼ a0 ¼ 2p=radian ¼ 0:11=degree The theoretical aerodynamic center is at the quarter chord point ðc=4Þ and CM ¼ 0. If the angle of attack is increased beyond a certain value the flow separates from the low-pressure side of the wing rapidly causing the lift to decrease and the drag to increase. This is termed ‘stall’ (Figure 5). An asymmetrical or cambered airfoil, where the camber line (the line drawn halfway between the upper and lower surfaces) deviates from the chord line (Figure 4), has the same theoretical slope of the lift curve, 2p, but has a nonzero value of the lift coefficient when a ¼ 0. The aerodynamic center is also at the quarter chord point but CMa0 (Figure 6).

The pitching moment is given by:

Lift coefficient (CL)

1.0 _ 0.1

0.5

_ 0.5

CM

_ 1.0

_ 0.1

Moment coefficient (C M)

CL

1.5

_ 1.5 _ 15 _ 10 _ 5

5

10

15

Angle of attack

Figure 3 CSIRO (Australia) modified SeaSoar with a Sea-Bird Electronics Inc. conductivity, temperature and depth (CTD) instrument.

Figure 5 Typical section characteristics for symmetrical airfoil type NACA0012.


TOWED VEHICLES

Lift coefficient (CL)

1.0 0.5

_ 0.5

CM

_ 1.0

_ 0.1 _ 0.2

Moment coefficient (C M)

CL

1.5

_ 0.3

_ 1.5 _ 15 _ 10 _ 5

5

10

15

Angle of attack

Figure 6 Typical section characteristics for asymmetrical airfoil type NACA4412.

These properties describe airfoil sections that are two-dimensional. In a real wing, the span is finite and there is spanwise flow. The effect of this is a ‘leakage’ around the wing tips from the high-pressure side to the low-pressure side. This generates wing-tip vortices, which in turn produce a downward flow around the wing – the downwash. The angle of this local flow relative to the wing subtracts from the angle of attack so that the wing actually experiences a smaller effective angle of attack. Since the lift vector is normal to the local relative flow it becomes inclined behind the vertical and so has a rearward component – the induced drag. This drag can be the dominant drag on a towed vehicle. The induced drag coefficient is given by: CDi ¼ C2L =ðpARÞ The reduction in the effective angle of attack of a wing with finite span also reduces the slope of the lift curve, a. dCL =da ¼ a ¼ a0 =ð1 þ a0 =pARÞ The reduced slope of the lift curve means that the wing has a higher angle of attack at stall. To achieve the necessary mechanical strength, the aspect ratio of wings used on towed vehicles is usually very low. This results in high induced drag and low values of the lift curve slope. It will be shown later that the higher induced drag is not significant. The lower sensitivity to changes in the angle of attack (and the higher angle at stall) can be an advantage: it makes the vehicle more tolerant of flight

69

perturbations such as those experienced when towing in rough seas. The delta wing (a wing with a triangular planform) is a popular form for vehicles without active depth control. Flow over a delta wing is dominated by large leading-edge vortices and cross-flow that enable the wing to operate at large angles of attack without stalling. A delta wing has a typical lift curve slope of about 0.05/degree (about half that of a conventional wing), but can operate with an angle of attack up to 301 before stalling. Delta wings make robust depressors and are often given large dihedral angles to increase roll stability. The dihedral is the angle of inclination of the wings in relation to the lateral axis. To control the depth of a towed vehicle, the magnitude and direction of the wing’s lift force are usually varied by:

•

the use of control surfaces (flaps) on the trailing edge;

• •

an independent control surface, usually at the tail; rotating the entire wing about its aerodynamic center to vary its angle of attack.

The first two methods cause the whole vehicle to adopt an angle of attack and so the body of the vehicle also generates lift. Some towed vehicles, such as the Aquashuttle (Chelsea Instruments, UK), operate without wings, generating all the lift from the body. Others, such as the Scanfish (MacArtney A/S, Denmark) and the V-Fin (Endeco Inc, USA), are effectively flying wings. The third method, used by the Batfish (Guildline Instruments, Canada) and SeaSoar (Chelsea Instruments, UK), maintains the body aligned with the flow, which is an advantage for some types of sensors that need to be aligned to the flow. Both these vehicles use a highly cambered wing section (NACA6412) that has a large moment coefficient ðCM E 0:13Þ. Thus a large torque is needed to rotate the wing. If a small operating torque is required the wings are typically controlled by an electric servomotor. If large forces are needed a hydraulic system is used. A symmetrical wing section pivoted at the quarter chord point requires only a small torque, but the control system needs to be robust enough to survive rough handling on the ship’s deck. To gain the maximum benefit of any lift force, the vehicle must fly so that the force is directed near to vertical, that is it should fly without a significant roll angle. A towed vehicle often needs to be able to direct its lift force both down and up, to maximize its depth range. A consequence of this is that dihedral, a


70

TOWED VEHICLES T

1

D

AC

servomotor systems. The other basic requirement is for pitch and yaw stability. This is achieved by ensuring that the tow point is ahead of the aerodynamic center (AC) of the vehicle (Figure 7). The position of the vehicle’s aerodynamic center is controlled by providing a suitably sized tailfin.

Center of gravity

Vehicles That do not Generate Lift

W

L

Figure 7 Basic forces on a towed vehicle.

common method of providing roll stability, cannot be used. What would be stable for lift in one direction would be unstable in the other. By having the tow cable attachment point above the center of gravity of the vehicle, the vehicle’s weight contributes to roll stability (Figure 7). But to stabilize the large lift forces needed for good depth performance, additional aerodynamic control by means of ailerons or similar devices is needed. These can be simple systems driven directly with gravity by using a heavy pendulum device or more sophisticated electric

These passive towed vehicles use their weight to produce the required downward force. The drag of the cable is overcome by the combined weight of cable and vehicle. Depth is controlled by varying the cable length. The depth is also very dependent on the tow speed. This is because the cable drag is proportional to the square of the tow speed and the depressing force is fixed by the cable and vehicle weight. This contrasts with the vehicle that generates lift; its depth is less speed-dependent because both lift and drag vary with speed in the same manner. A heavy passive vehicle can have good pitch and roll stability if the position of the tow point, center of mass, center of gravity and the aerodynamic center are carefully chosen. The lack of flow separation over lifting surfaces also makes them acoustically quiet. This stability and quietness make them useful vehicles for underwater acoustic work (Figure 8).

Figure 8 The CSIRO (Australia) multifrequency towed vehicle used for fish stock measurements. Note the ribbon-faired cable.


TOWED VEHICLES

71

shown in Figure 7 the angle of the cable at the vehicle, f1 is determined by the weight ðWÞ, lift ðLÞ and drag ðDÞ of the vehicle.

0

DN

tan f1 ¼ ðW þ LÞ=D D u DT w Figure 9 Forces on the tow cable.

Performance of the Vehicle/Cable System When a cable is towed through water, it assumes an equilibrium angle where the drag force (D) is balanced by the cable weight (w). If the cable properties are uniform, this angle is constant along the cable length By referring to Figure 9

Effects of Wave-induced Ship Motion on the Towed Vehicle

tan f0 ¼ w=D Or expressed in terms of the normal drag coefficient cos f0 ¼ 17 1 þ 4B2

The cable profile starts at an angle of f1 at the vehicle and gradually approaches f0 up the cable. The vehicle drag is the sum of the form drag, the friction drag and the induced drag. With vehicles that generate lift the induced drag is the main component. Even with a poorly streamlined vehicle it dominates, providing perhaps 75% of the drag. The rather poor performance of the typical low aspect ratio wing used on towed vehicles gives the vehicle a lift to drag ratio of about 3. This makes the cable angle f1 E 701. Further improvement in the lift to drag ratio does not gain much in cable angle or depth. Table 1 compares the equilibrium depths for bare and faired cables; Table 2 compares the performance of bare and faired cable when towing a vehicle with controllable wings. These data were produced by a computer model of the vehicle/cable system.

1=2

=2B

where B ¼ ru2 dCDN =ð2wÞ The equilibrium depth is ðl sin f0 Þ. When a vehicle is attached to the end of the cable it perturbs this equilibrium depth by the extent that its weight and lift force either drag the cable deeper when diving, or lift the cable when climbing. This defines the depth range of the vehicle/cable system (Figure 10). As

Equilibrium depth

A problem with towing in rough seas is that the wave-induced motion of the towing vessel is propagated down the tow cable to the vehicle. Motion normal to the cable is rapidly damped, but there is surprisingly little attenuation of the tangential motion of the cable. The amplitude of the perturbation of the vehicle is approximately the same as the cable at the ship. This causes changes in the vehicle’s pitch angle and depth which can be very significant for towed acoustic systems and vehicles such as camera units operating very close to the seafloor.

0

Depth range

1

Figure 10 Towed cable profile.


72

TOWED VEHICLES

Table 1 Comparison of equilibrium depths for 500 m of 8.2 mm diameter cable with a weight of 2.5 N m1 towed at 4 m s1 for faired and bare cable

Faired CDN ¼ 0.2 Bare CDN ¼ 2.0

Equilibrium depth (m)

Tension (kN)

f0 (degrees)

142 61

3.6 0.7

17 7

The following methods are used to minimize the effects of ship motion.

• • •

•

•

The vehicle can be tuned to minimize the pitching effect by carefully adjusting the position of the vehicle’s aerodynamic center and center of mass in relation to the tow point. A constant-tension winch reduces the cable displacement at the ship by spooling cable in and out as the ship surges. A device called an accumulator produces an effect similar to the constant-tension winch. The cable runs over a pair of sheaves that are mounted on a sprung or pneumatic arrangement that allows them to take up and pay out cable as needed. In the ‘two-body system’ the instrumented vehicle is passive and near-neutrally buoyant. It is towed behind the depressor or depth-controlled vehicle on a cable that is approximately horizontal. This geometry decouples ship’s motion from the second vehicle. A system that has a cable angle close to horizontal at the ship is insensitive to ship pitch and heave as these displacements are almost normal to the cable. A system that uses a long cable, a cable without fairing or a high tow speed has this characteristic.

Flight Control Tow speeds vary from as low as 1 ms1 for seafloor survey instruments to 10 ms1 for high-speed systems. The fast systems are limited to shallow depths.

Speeds of 3–5 ms1 are common for oceanographic surveys and depths of up to 1000 m can be achieved. Depth-controlled vehicles operate with vertical velocities up to about 1 m s1. Depth-controlled vehicles are commonly operated in an undulating mode with maximum and minimum depths set to specific values to give a triangular flight path. The depth is measured by the water pressure at the vehicle and the wings or control surfaces are adjusted to make the vehicle follow the defined path by a servo system. The servo-loop parameters are usually controlled by the shipboard computer; however the actual servo-loop system may reside in the towed vehicle or combine ship- and vehicle-based components. The control algorithm needs to be carefully tuned to achieve smooth flight.

Sensors Some of the earliest towed vehicles were used to collect plankton (in fact a trawl net is a form of towed vehicle). These plankton collectors are often separate nets and depressors but can also be single units. An early system, the Hardy Continuous Plankton Recorder, dates from the 1930s. Several commercially available vehicles are a development of this type, for example the Aquashuttle (Chelsea Instruments, UK) and the U-Tow (Valeport Ltd, UK). Depth-controlled vehicles are commonly equipped with a conductivity, temperature and depth (CTD) instrument. They are also suitable platforms for

Table 2 Comparison of depths, cable tensions and cable angles (f2 cable angle at ship, f1 cable angle at vehicle) for the same cable as Table 1 towing a typical vehicle with the following characteristics: weight in water, 1500 N; cross–sectional area, 0.2 m2; wing area, 0.5 m2; wing aspect ratio, 1; tow speed, 4 m s1. Positive lift coefficients indicate lift force upwards

Faired CDN ¼ 0.2 Bare CDN ¼ 2.0

Wing CL

Depth (m)

Tension (kN)

f2 (degrees)

f1 (degrees)

1.0 þ 0.5 1.0 þ 0.5 þ 1.0

360 0 140 31 0

11.2 4.3 6.5 1.6 3.2

36 7 8 7 6

72 36 72 36 55


TOWED VEHICLES

Depth (m)

many other types of sensors, such as fluorometers, radiometers, nutrient analysers and transmissometers. In the case of a CTD it is recommended that the sensors be duplicated. When a CTD is lowered from a stationary ship in the usual manner the calibration of the conductivity sensor is checked by collecting water samples for laboratory analysis. This option is not usually available on a towed instrument so a check of sensor stability can be obtained by using dual sensors. The conductivity cells

100

73

can also be blocked by marine organisms especially when towing near the surface. Dual cells allow recognition of this problem. Figure 11 shows the data from a CTD section across an ocean front demonstrating the high spatial resolution realized with a towed system. Passive towed vehicles are often used for acoustic survey work. Examples are side-scan sonars, towed multibeam systems for seafloor mapping, and vehicles for estimating fish stocks. The towed vehicle

12

14 13

11

12

11

200

10

300

Salinity

Depth (m)

35.5 100

35.6

34.8

35.0

35.1

35.4 35.0

200

34.8 34.9

34.8

35.5 300

Density (sigma-t)

Depth (m)

26.55

26.60

100 26.50

26.55

26.70

26.70 26.75

200

26.80 300 0

50

100

150 200 Distance (km)

250

300

Figure 11 An example of a conductivity, temperature and depth (CTD) section across the Sub-Tropical Front south of Australia using a SeaSoar towed vehicle equipped with a Seabird CTD instrument. (From Tomczak M and Pender L (1999) Density compensation in the Sub-Tropical Front in the Indian Ocean South of Australia.http://www.es.flinders.edu.au/ Bmattom/STF/ fr1098.html.


74

TOWED VEHICLES

D DN DT L M Re S Sn T W a a0 Figure 12 A faired cable winch and SeaSoar towed vehicle. This winch holds 5000 m of cable, 400 m faired and 4600 m of bare cable. A combination of faired and bare cable can be a costeffective method of achieving greater depths.

offers advantages over hull-mounted transducers by deploying the acoustic transducer away from the high noise environment and bubble layer near the ship and closer to the object of interest. A well-designed system can also be a more stable platform for the acoustic transducers than a ship in rough seas.

The Winch Towed-vehicle systems using electromechanical cables usually require a special, purpose-built winch with accurate spooling gear and slip-rings to make the electrical connection to the rotating drum. Cable lengths can vary from 100 m to 5000 m. If a cable with rigid fairing is used special blocks and fairing guiding devices are needed to handle the cable. The faired cable has a large bending radius and can only be wound onto the winch drum in a single layer. As illustrated in Figure 12, this type of winch is quite large. If the towed vehicle system uses wire rope for the tow cable, then the winch can be a standard type.

b c cg d f l u uN uT w a f f0 f1 f2 m n r

Drag force Normal drag force Tangential drag force Lift force Pitching moment Reynolds number ¼ ud/v Wing planform area Strouhal number ¼ fd/u Cable tension Tow vehicle weight Slope of the lift curve for a wing ¼ dCL/da Slope of the lift curve for an airfoil section ¼ dCL/da Wing span Chord length Center of gravity Cable diameter Frequency Cable length Velocity Normal velocity Tangential velocity Cable weight per unit length Angle of attack Angle of the cable to the horizontal Cable equilibrium angle Cable angle at the towed vehicle Cable angle at the ship Dynamic viscosity (E1 10 3 kg m 1 s for water) Kinematic viscosity ¼ m/r (E1 10 6m2s 1 for water) Density (E1000 kg m 3 for water)

See also Acoustic Scattering by Marine Organisms. Platforms: Autonomous Underwater Vehicles. Ships. Sonar Systems.

Symbols used A AC AR CDi CDN CDT CL CM

Area Aerodynamic center Wing aspect ratio ¼ b2/S Induced drag coefficient Normal drag coefficient Tangential drag coefficient Lift coefficient Moment coefficient

Further Reading Abbott IH and von Doenhoff AE (1959) Theory of Wing Sections. New York: Dover Publications. Anderson JD (1991) Fundamentals of Aerodynamics, 2nd edn. New York: McGraw-Hill. Wingham PJ (1983) Comparative steady state deep towing performance of bare and faired cable systems. Ocean Engineering 10(1): 1--32.


TRACE ELEMENT NUTRIENTS W. G. Sunda, National Ocean Service, NOAA, Beaufort, NC, USA Published by Elsevier Ltd.

Introduction Life in the sea is dependent on fixation of carbon and nitrogen by unicellular algae, ranging in size from o1 to over 100 mm in diameter. These so-called phytoplankton consist of eukaryotic algae, which photosyntheticly fix carbon into organic matter, and photosynthetic bacteria (cyanobacteria) that carry out both carbon and dinitrogen (N2) fixation. Until recently, phytoplankton productivity in the ocean was thought to be primarily limited by available fixed nitrogen (nitrate, nitrite, ammonia, and various organic nitrogen compounds) and to a lesser extent phosphorus (orthophosphate and organic phosphorus compounds). However, in the past 20 years, enrichment experiments in bottles and in mesoscale patches of surface water have shown that iron regulates the productivity and species composition of planktonic communities in major regions of the world ocean, including the Southern Ocean, the equatorial and subarctic Pacific, and some coastal upwelling systems. In addition, it now appears that

iron limits N2 fixation by cyanobacteria in large regions of the subtropical and tropical ocean, and thus may control oceanic inventories of biologically available fixed nitrogen. Several other micronutrient metals (zinc, cobalt, manganese, and copper) have also been shown to stimulate phytoplankton growth in ocean waters, but their effect is usually much less than that of iron. However, these metals may play an important role in regulating the composition of phytoplankton communities because of large differences in trace metal requirements among species. In this article interactions between trace element nutrients (iron, zinc, cobalt, manganese, copper, nickel, cadmium, molybdenum, and selenium) and phytoplankton in seawater are discussed. In these interactions, not only do the trace nutrients affect the growth and species composition of phytoplankton communities, but the phytoplankton and other biota (e.g., heterotrophic bacteria and zooplankton) have a profound influence on the distributions, chemistry, and biological availability of these elements (Figure 1). There are many aspects to consider, including (1) the sources, sinks, and cycling of trace element nutrients in the ocean; (2) the distribution of these elements in time and space, and their chemical speciation (or forms); (3) the interactions of these elements with phytoplankton at different levels of biological organization (molecular, cellular, population, community,

Marine plankton - Growth rates - Biomass - Species composition Trace element chemistry - Concentrations - Speciation - Redox cycling

Figure 1 Conceptual diagram of the mutual interactions between trace element nutrients (Fe, Mn, Zn, Co, Cu, Cd, and Se) and phytoplankton in the sea. In these interactions, the chemistry of trace element nutrients, in terms of their concentrations, chemical speciation, and redox cycling, regulates the productivity and species composition of marine phytoplankton communities. These communities in turn regulate the chemistry and cycling of trace element nutrients through cellular uptake and assimilation, vertical transport of biogenic particles (intact cells and fecal pellets), grazer and bacterially mediated regeneration processes, production of organic chelators, and biological mediation of trace element redox transformations.


75

76

TRACE ELEMENT NUTRIENTS

ecosystem); and (4) the role of planktonic communities in regulating the chemistry and cycling of these nutrient elements in seawater.

Distribution in Seawater Knowledge of the distributions of trace element nutrients is essential to understanding the influence of these micronutrients on the productivity and species diversity of marine planktonic communities. Concentrations of filterable iron and zinc (that portion passing through a 0.2- or 0.4-mm-pore filter) typically are extremely low (only 0.02 to 0.1 nM) in surface waters of the open ocean. Cadmium, a nutrient analog for zinc, can reach values as low as 0.002 nM (Table 1). Concentrations of these and other trace element nutrients often increase by orders of magnitude in transects from the open ocean to coastal and estuarine waters due to inputs from continental sources, such as rivers, groundwater, eolian dust, and coastal sediments. Filterable iron can reach micromolar concentrations in estuaries and can approach 10–20 mM in rivers, 5 or 6 orders of magnitude higher than surface ocean values. This filterable iron occurs largely as colloidal particles (o0.4-mm diameter) consisting of iron oxides in association with organic matter. These are rapidly lost from estuarine and coastal waters via salt-induced coagulation and particulate settling. Because of this efficient removal, very little of the iron in rivers reaches the open sea, and most of the iron in ocean waters is derived from the deposition of mineral dust blown on the wind from arid regions of the continents. These eolian inputs change seasonally with variations in prevailing winds and are highest in

Table 1

Micronutrient elements and their abundance in ocean water and phytoplankton

Micronutrient element

Iron Manganese Zinc Cobalt Cadmium Copper Nickel Molybdenum Selenium

a

waters downwind of arid regions such as North Africa and Central Asia. Areas far removed from these eolian sources, such as the South Pacific and the Southern Ocean, receive little atmospheric iron deposition and are among the most iron-limited regions of the oceans. Because of the large gradients in trace metal concentrations between the open ocean and coastal waters, oceanic phytoplankton species have evolved the ability to grow at much lower available concentrations of iron, zinc, and manganese. In doing so they have been forced to rearrange their metabolic architecture (e.g., in the case of iron-rich protein complexes involved in photosynthesis) or to switch from scarce elements to more abundant ones in some critical metalloenzymes (e.g., Ni and Mn replacement of Fe in the antioxidant enzyme superoxide dismutase). Concentrations of many trace element nutrients (zinc, cadmium, iron, copper, nickel, and selenium) increase with depth in the ocean, similar to increases observed for major nutrients (nitrate, phosphate, and silicic acid) (Figures 2–4). In the central North Pacific, filterable concentrations of zinc and cadmium increase by 80-fold and 400-fold, respectively, between the surface and 1000-m depth. The similarity between vertical distributions of these trace elements and major nutrients indicates that both sets of nutrients are subject to similar biological uptake and regeneration processes. In these processes, both major and trace element nutrients are efficiently removed from surface waters through uptake by phytoplankton. Much of these assimilated nutrients are recycled within the euphotic zone by the coupled processes of zooplankton grazing and excretion, viral lysis of cells, and bacterial degradation of organic

Major input source

Wind-born dust Rivers Wind-born dust Rivers Rivers Rivers Rivers Rivers Rivers

Major dissolved chemical species

Organic chelates Mn2þ Organic chelates Organic chelates Organic chelates Organic chelates Ni2þ MoO4 2 Organic selenides, SeO4 2

Dissolved concentrationa (nM) Surface water

Deep water (Z0.8 km)

0.02–0.5 0.1–5 0.05–0.2 0.007–0.03 0.002–0.3 0.5–1.4 2–3 100–110 0.5–1.0

0.4–1 0.08–0.5 2–10 0.01–0.05 0.3–1.0 1.5–5 5–11 100–110 1.5–2.3

Dissolved is defined operationally as that passing through a 0.2- or 0.4-mm-pore filter.


Element:carbon ratio in phytoplankton (mmol:mol)

3–40 2–30 1–40 0.1–3 0.2–8 2–6 2–17 0.05–0.8 1–2

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Figure 2 Depth profiles for major nutrients (nitrate (Pacific only), phosphate, and silicic acid) and filterable concentrations (that passing a 0.4-mm filter) of trace nutrient elements (zinc, cadmium, nickel, copper, and manganese) in the central North Pacific (diamonds, 32.71 N, 145.01 W, Sep. 1977) and North Atlantic (squares, 34.11 N, 66.11 W, Jul. 1979). Manganese concentrations in the Pacific were analyzed in acidified, unfiltered seawater samples. The units mol kg1 are defined as moles per kilogram of seawater. Data from Bruland KW and Franks RP (1983) Mn, Ni, Cu, Zn and Cd in the western North Atlantic. In: Wong CS, Boyle E, Bruland KW, Burton JD, and Goldberg ED (eds.) Trace Metals in Sea Water, pp. 395–414. New York: Plenum.

(a) 0

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Figure 3 Depth profiles for nitrate and filterable concentrations of trace element nutrients (iron, zinc, and cobalt) in the subarctic North Pacific Ocean (ocean station Papa, 50.01 N, 145.01 W, Aug. 1987). Data from Martin JH, Gordon RM, Fitzwater S, and Broenkow WW (1989) VERTEX: Phytoplankton/iron studies in the Gulf of Alaska. Deep-Sea Research 36: 649–680.


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0.0 0

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Selenium (nmol kg−1) 1.0 1.5

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Organic Se Total Se

Depth (m)

400

600

800

1000 Figure 4 Depth profiles for concentrations of total selenium and different chemical forms of selenium (selenate, selenite, and organic selenium compounds) in filtered seawater samples from the eastern tropical North Pacific Ocean (181 N, 1081 W; Oct.–Nov. 1981). Data are from Cutter GA and Bruland KW (1984) The marine biogeochemistry of selenium: A reevaluation. Limnology and Oceanography 29: 1179–1192.

materials. However, a portion of the assimilated nutrients is continuously lost from the euphotic zone by vertical settling of intact algal cells or zooplankton fecal pellets. The macro- and micronutrients contained within these settling biogenic particles are then returned to solution at depth in the ocean via bacterial degradation processes. Ultimately the uptake, settling, and regeneration processes deplete nutrients within the euphotic zone to low levels while concentrations at depth are increased. This process also transfers CO2 to the deep sea and is often refered to as the biological CO2 pump. The cycle is completed when the nutrient and CO2 reservoirs at depth are returned to the surface via vertical advection (upwelling) and mixing processes. The deep-water concentrations of both major nutrient elements (N, P, and Si) and many micronutrients (Zn, Cd, Ni, and Cu) are much higher in deep waters of the Pacific than the Atlantic (Figure 2) because of large-scale ocean circulation patterns, in which deep waters are formed via subduction at high latitudes in the North Atlantic and are returned to the surface via upwelling in the northern regions of the North Pacific and Indian Oceans. Because of these patterns, the deep North Pacific contains waters that have resided at the bottom for much

longer (B1000 years) than the deep Atlantic waters and thus have had a much longer time to accumulate major nutrient and micronutrient elements from biological regeneration processes. Several trace element nutrients (molybdenum, manganese, and cobalt) provide exceptions to the general trend of increasing concentrations with depth. Molybdenum occurs almost exclusively as soluble, nonreactive molybdate ions MoO4 2 , which occur at a high concentration (B105 nM) relative to their biological demand (Table 1). Consequently, there is minimal biological removal of molybdenum from surface seawater and its concentration varies in proportion to salinity. By contrast, concentrations of manganese (Figure 2(h)) and cobalt (Figure 3(d)) are typically maximum near the surface and depleted at depth owing to deep-water scavenging processes.

Chemical Speciation Trace element nutrients exist as a variety of chemical species in the sea, which strongly influences their chemical behavior and biological availability. All but Se and Mo occur as cationic metal ions that are complexed (bound) to varying degrees by inorganic



and organic ligands (complexing agents) or are adsorbed onto or bound within particles. Many trace element nutrients (iron, copper, manganese, cobalt, and selenium) cycle between different oxidation states, which have quite different kinetic labilities (reaction rates), solubilities, binding strengths with organic ligands, and biological availabilities. Nickel, zinc, and cadmium exist in normal oxygenated seawater as highly soluble divalent cations that are complexed to varying degrees by inorganic ligands (Cl, OH, and CO3 2 ) and organic chelators. Nickel is bound to only a small extent (0–30%) by organic ligands. By contrast, B99% of the zinc ions and 70% of the cadmium are heavily complexed by unidentified strong organic ligands present at low concentrations in surface waters of the North Pacific. The strong chelation of zinc reduces the concentration of dissolved inorganic zinc to B1 pM in surface seawater, sufficiently low to limit the growth of many algal species. Manganese undergoes redox transformations, but is minimally bound to organic ligands. The stable redox species of manganese in oxygenated seawater, Mn(IV) and Mn(III) oxides, are insoluble, although Mn(III) can exist in some instances as soluble organic chelates. Mn(III) and Mn(IV) can be reduced chemically, photochemically, or biologically to dissolved Mn(II), which is fully soluble in seawater and is not appreciably bound by organic ligands. Although Mn(II) is unstable with respect to oxidation by molecular oxygen, the chemical kinetics of this reaction are exceedingly slow in seawater. However, Mn(II) oxidation is greatly accelerated by bacterial enzymes that catalyze Mn(II) oxidation to Mn(IV) oxides. The bacterial formation of Mn oxides, and subsequent removal via coagulation and settling of oxide particles, results in short residence times (20–40 years in the North Pacific) and low concentrations for manganese in deep-ocean waters (Figure 2(h)). Oxidation is absent or greatly diminished in the ocean’s surface mixed layer due to photo-inhibition of the Mn-oxidizing bacteria. The absence of bacterially mediated oxidation of Mn(II) and minimal organic chelation often results in high concentrations of Mn2þ ions in surface seawater (Table 1; Figure 2(h)), enhancing the supply of Mn to phytoplankton. Iron is the most biologically important trace metal nutrient, and its chemical behavior is perhaps the most complex. Its stable oxidation state in oxygencontaining waters is Fe(III), which forms sparingly soluble iron hydroxide and oxide precipitates. This oxide formation and the tendency of ferric ions to adsorb onto particle surfaces results in the scavenging of iron from seawater via particulate aggregation and settling processes. This removal results in

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short residence times for iron in deep-ocean waters (B50–100 years) and low concentrations (0.4– 0.8 nM) despite the high crustal abundance of iron (it is the fourth most abundant element by weight). Most (499%) of the dissolved ferric iron in seawater is bound to organic ligands which minimizes iron adsorption and precipitation, and thus reduces the removal of iron from seawater by particulate scavenging processes. Some of these organic ligands may be strong ferric chelators (siderophores) produced by bacteria to solubilize iron and facilitate intracellular iron uptake. Ferric iron can be reduced in seawater to highly soluble Fe(II) (ferrous iron) by a number of processes including photo-reduction of organic chelates in surface waters, biological reduction of iron at cell surfaces, and reduction by chemical reducing agents. Because ferrous iron binds much more weakly to organic chelators than ferric iron, the photo-reduction or biological reduction of iron in ferric chelates often results in the dissociation of iron from the chelates, which increases iron availability for biological uptake (see below). The released ferrous ions are unstable in oxygenated seawater, and are reoxidized to soluble ferric hydrolysis species, and recomplexed by organic ligands on timescales of minutes. Thus iron undergoes a dynamic redox cycling in surface seawater, which can greatly enhance its biological availability to phytoplankton. Other micronutrient metals such as copper and cobalt also exist in multiple oxidation states and are heavily complexed by organic chelators. Copper can exist in seawater as thermodynamically stable copper(II), or as copper(I). Most (499%) of the copper in near-surface seawater is heavily chelated by strong organic ligands present at low concentrations (2– 3 nM in ocean waters). This chelation decreases free cupric (copper II) ion concentrations to very low levels (0.1–1 pM). Copper(II) can be reduced to Cu(I) by photochemical and biological processes or by reaction with chemical reducing agents, such as sulfurcontaining organic ligands. The resultant Cu(I) can be reoxidized by reaction with molecular oxygen, but the effect of this redox cycling on the biological availability of copper is currently unknown. The chemistry of cobalt is also highly complex. Cobalt exists in seawater as soluble cobalt(II) or as cobalt(III), which forms insoluble oxides at the pH of seawater. The formation of these oxides appears to be microbially mediated and is largely responsible for the removal of cobalt from deep-ocean waters and for the resultant low deep-ocean concentrations (Figure 3(d)). Much of the dissolved cobalt in seawater is strongly bound to organic ligands, and recent evidence suggests that this cobalt exists as


80


kinetically inert cobalt(III) chelates. There is also evidence that these cobalt(III)-binding ligands are produced by marine cyanobacteria and that these ligands may facilitate microbial uptake of cobalt. Selenium is a metalloid, which occurs immediately below sulfur in the periodic table. Consequently, its chemical behavior often mimics that of sulfur. Selenium exists in subsurface seawater primarily as soluble oxyanions selenate (SeO4 2 ; þ 6 oxidation state) and selenite (SeO3 2 ; þ 4 oxidation state). Phytoplankton preferentially take up selenite which depletes its concentration in surface seawater (Figure 4). Selenate is then taken up and depleted following the removal of selenite. The selenate and selenite ions taken up by phytoplankton are metabolically reduced to the selenide ( 2 oxidation state) and used to synthesize selenomethionine and selenocysteine, chemical analogs of the sulfur-containing amino acids methionine and cysteine. In surface waters a majority of the selenium occurs as biologically regenerated organic selenide compounds of unknown chemical structure (Figure 4).

Biological Uptake All trace elements are taken up intracellularly by specialized transport proteins (enzymes) on the outer membrane of plankton cells. Consequently, uptake rates generally follow Michaelis–Menten enzyme kinetics: Uptake rate ¼ Vmax S=ðKs þ SÞ Vmax is the maximum uptake rate, S is the concentration of the pool of chemical species that react with receptor sites on the transport protein, and Ks is concentration of the substrate pool at which half of the transport protein is bound, and the uptake rate is half of Vmax. Virtually all of these proteins act as pumps and require energy for intracellular uptake. Each transport system reacts with a single chemical species or group of related chemical species and thus chemical speciation is extremely important in regulating cellular uptake. Uptake systems range from simple to highly complex depending on the chemical speciation of the nutrient element and its biological demand (requirement) relative to its external availability. Uptake systems appear to be simplest for dissolved Mn(II), which is taken up in phytoplankton by a single high-affinity transport system that is under negative feedback regulation. In this negative feedback, as the concentration of dissolved Mn(II) decreases, the Vmax of the transport system is increased

to maintain relatively constant Mn uptake rates and intracellular concentrations. Uptake systems for zinc, cadmium, cobalt(II), and copper(II) are somewhat more complex. The phytoplankton species examined to date have at least two separate zinc transport systems: a low-affinity system whose Vmax is relatively constant, and an inducible high-affinity system. The low-affinity system has high Vmax and high Ks values and transports zinc at high zinc ion concentrations. The high-affinity system is responsible for zinc uptake at low zinc ion concentrations, and has low Ks, and variable Vmax values that are under negative feedback regulation. At sufficiently low concentrations of dissolved inorganic zinc species (B10 pM), the cellular uptake approaches limiting rates for the diffusion of labile inorganic zinc species to the cell surface. The existence of high- and low-affinity transport systems results in sigmoidal relationships between zinc uptake rates (and cellular Zn:C ratios) and concentrations of dissolved inorganic zinc species as seen in Figure 5 for an oceanic diatom. Cobalt and sometimes cadmium can metabolically substitute for zinc in many metalloenzymes. To facilitate this substitution, the uptake of these divalent metals is increased by over 100-fold in diatoms with decreasing dissolved inorganic zinc concentrations and resulting decreases in cellular zinc uptake rates (Figure 5). Uptake of Cd by this inducible transport system is repressed at high intracellular zinc levels, and under these conditions, cadmium leaks into the cell through the cell’s Mn(II) transport system. Thus cellular uptake of cadmium in the ocean is regulated by complex interactions among dissolved inorganic concentrations of Cd, Zn, and Mn. Likewise, since cobalt uptake is repressed at high zinc ion concentrations, biological depletion of cobalt often does not occur until after zinc is depleted, as observed in the subarctic Pacific (Figure 6). The binding and subsequent intracellular uptake of the above divalent metals (Zn2þ, Mn2þ, Cd2þ, Co2þ, and Cu2þ) by the various intracellular uptake systems are regulated by the concentration of dissolved inorganic metal species (free aquated ions and inorganic complexes with chloride ions, hydroxide ions, etc.). Organic complexation of these metals reduces their uptake by decreasing the concentration of dissolved inorganic metal species. This effect can be substantial in cases such as zinc, where up to 99% or more of the metal is bound to organic ligands in surface seawater. Since iron is the most limiting of the trace element nutrients and its chemistry the most complex, it is perhaps not surprising that the transport systems for iron are the most varied and complex. Iron is highly



81

100

Cellular metal uptake rate (µmol (mol C)−1d−1)

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Zinc Cobalt Cadmium

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log [Zn′] Figure 5 Cellular uptake rates for zinc, cobalt, and cadmium (normalized per mol of cell carbon) for the oceanic diatom Thalassiosira oceanica plotted as a function of the log10 of the molar concentration of dissolved inorganic zinc species (Zn0 , aquated zinc ions plus inorganic zinc complexes). Dissolved inorganic cobalt and cadmium species in the seawater medium were held constant at concentrations of 1.5 and 2.7 pM (1012 M), respectively. Uptake rates for cadmium and cobalt increase by at least 2 orders of magnitude when Zn0 concentrations decrease below 1010 M. The large increase in uptake rates reflects the induction of high-affinity cellular transport systems for Cd and Co in response to declining intracellular Zn concentrations. Data are from Sunda WG and Huntsman SA (2000) Effect of Zn, Mn, and Fe on Cd accumulation in phytoplankton: Implications for oceanic Cd cycling. Limnology and Oceanography 45: 1501–1516.

bound as ferric oxides and organic chelates and prokaryotic and eukaryotic plankton cells have evolved different strategies to access these bound forms of iron. Prokaryotic cells (cyanobacteria and heterotrophic bacteria) have evolved high-affinity uptake systems that are induced under iron deficiency. These systems involve the biosynthesis and extracellular release of a variety of high-affinity iron chelators (siderophores) that strongly bind iron(III) in the surrounding seawater. The siderophore chelates are then actively taken up into the cells by transport proteins on the outer cell membrane. The siderophore chelates have different chemical structures, and different outer membrane siderophore transport proteins are needed to take up structurally distinct siderophores or groups of siderophores with similar chemical structures. Bacteria often take up not only

their own siderophores, but those produced by other bacteria, resulting in complex ecological interactions among bacteria. Eukaryotic phytoplankton do not appear to produce siderophores and there is little evidence for direct cellular uptake of ferric siderophore chelates. Instead there is mounting evidence for the utilization of a high-affinity transport system that accesses ferric complexes via their reduction at the cell surface and subsequent dissociation of the resulting ferrous-ligand complexes. The released ferrous ions bind to iron(II) receptors on iron transport proteins located on the outer cell membrane, which transport the iron into the cell. This intracellular transport involves the reoxidation of bound iron(II) to iron(III) by a copper protein, and thus copper is required for cellular iron uptake. The availability of iron to this transport


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Zinc (nmol kg−1)

(a)


10

Metabolic Requirements and their Relation to Other Limiting Resources

8

Trace element micronutrients are essential for the growth and metabolism of all marine algae and bacteria. They play critical roles in photosynthesis, respiration, and the assimilation and transformation of essential macronutrients (nitrogen, phosphorus, and silicic acid). Thus trace metal requirements can be influenced by the availability of light, CO2, and major nutrients and the cycles of major nutrient elements are influenced by trace element nutrients. Of the micronutrient metals, iron is needed in the greatest amount and is the metal that most frequently limits algal growth. Iron serves essential metabolic functions in photosynthetic electron transport, respiration, nitrate assimilation, N2 fixation, and detoxification of reactive oxygen species (e.g., superoxide radicals and hydrogen peroxide). Because of its heavy involvement in photosynthetic electron transport, cellular iron requirements increase with decreasing light intensity and photoperiod. Such effects can lead to iron–light co-limitation in low-light environments such as regions where the depth of the surface wind mixed layer greatly exceeds the depth of light penetration (as often occurs in the Southern Ocean and at high latitudes during the winter) or in the deep chlorophyll maximum at the bottom of the euphotic zone (the sunlit layer) in thermally stratified surface waters. Iron also occurs in the enzymes (nitrate and nitrite reductases) involved in the reduction of nitrate to ammonium in phytoplankton and the enzyme complex (nitrogenase) that fixes nitrogen (reduces dinitrogen molecules to ammonia) in cyanobacteria. Both processes require cellular energy (in the form of ATP molecules) and reductant molecules (NADPH), and iron is also needed in high amounts for the photosynthetic production of the needed ATP and NADPH. Algal cells growing on nitrate need B50% more iron to support a given growth rate than cells growing on ammonium. Consequently, iron can be especially limiting in oceanic upwelling systems (such as the equatorial and subarctic Pacific) where waters containing high nitrate concentrations, but low iron, are advected to the surface (see Figures 3(a) and 3(b)). Even higher amounts of iron (up to 5 times as much) are needed for diazotrophic growth (growth on N2) than for equivalent growth on ammonium due to high energetic (ATP) cost for nitrogen fixation and the large amount of iron in the nitrogenase enzyme complex. As a result, iron appears to limit N2 fixation in large regions of the ocean and is thought to control oceanic inventories of fixed nitrogen. As a consequence, nitrogen is the primary limiting major nutrient in most ocean waters, while in lakes, where

6 4 Zinc, T-5

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Figure 6 Plots of filterable zinc and cobalt concentrations vs. phosphate at two stations in the subarctic Pacific (Station T-5, 39.61 N, 140.81 W and Station T-6, 45.01 N, 142.91 W, Aug. 1987). The decrease in zinc with decreasing phosphate is caused by the simultaneous removal of both metals via cellular uptake and assimilation by phytoplankton. Cobalt becomes depleted by phytoplankton uptake only after zinc concentrations drop to very low levels (o0.2 nmol kg1). This pattern is consistent with metabolic replacement of cobalt for zinc, as observed in phytoplankton cultures (see Figure 5). Data plots after Sunda WG and Huntsman SA (1995) Cobalt and zinc interreplacement in marine phytoplankton: Biological and geochemical implications. Limnology and Oceanography 40: 1404–1417.

system is dependent on the reduction potential of the ferric complexes; consequently, readily reducible ferric species such as dissolved inorganic ferric hydroxide complexes are accessed much more readily by this system than are strongly bound ferric siderophore chelates. Thus, iron uptake by this system is highly dependent on the chemical speciation of iron in seawater. Photo-reductive dissociation of ferric chelates increases iron availability to this system, since the released ferrous ions can directly react with the membrane transport protein and the reoxidized ferric hydrolysis species are readily reduced and taken up.



iron concentrations are much higher, phosphate is the primary limiting nutrient. Due to iron limitation of C fixation and N2 fixation in major regions of the ocean, iron plays a significant role in regulating carbon and nitrogen cycles in the ocean. It thus helps regulate the biological CO2 pump discussed earlier, which through transport of carbon to the deep ocean, controls the ocean/atmosphere CO2 balance and CO2-linked greenhouse warming. There is evidence that climatically driven variations in the input of iron-rich continental dust to the ocean has played an important role in regulating glacial–interglacial climate cycles. Manganese occurs in the water-splitting complex of photosystem II, and thus is essential for photosynthesis. Consequently, like iron, it is needed in higher amounts for growth at low light. Manganese also occurs in superoxide dismutase, an antioxidant enzyme that removes toxic superoxide radicals, produced as byproducts of photosynthesis. Because it has fewer metabolic functions, its cellular growth requirement is less than that of iron. Manganese may limit algal growth in certain low-Mn environments such as the subarctic Pacific and Southern Ocean, where manganese additions have been observed to stimulate algal growth in bottle incubation experiments. Zinc serves a variety of metabolic functions and has a cellular requirement similar to that for manganese. It occurs in carbonic anhydrase (CA), an enzyme critical to intracellular CO2 transport and fixation. Higher amounts of this enzyme are needed at low CO2 concentrations, leading to potential colimitation by zinc and CO2 in the ocean. However, the B35% increase in CO2 in the atmosphere and surface ocean waters from the burning of fossil fuels makes Zn–CO2 co-limitation less likely in the modern ocean than in preindustrial times. Zinc also occurs in zinc finger proteins, involved in DNA transcription, and in alkaline phosphatase, needed to acquire phosphorus from organic phosphate esters, which dominate phosphate pools in low-phosphate ocean waters. Consequently, Zn and P may co-limit algal growth in regions where both nutrients occur at low concentrations such as the central gyre of the North Atlantic. Cobalt, and sometimes cadmium, can substitute for zinc in many zinc enzymes such as CA, leading to complex interactions among the three metals in marine algae (Figure 5). The presence of cadmium in CA appears to explain its nutrient-like distribution in ocean waters (Figure 2(e)), and the identification of a unique Cd-CA enzyme in marine diatoms means that it functions as a micronutrient in these organisms. Cobalt also occurs in vitamin B12, an essential

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vitamin required for growth of many eukaryotic algal species. This vitamin is synthesized only by bacteria, resulting in potential interactions between B12-producing bacteria and B12-requiring eukaryotic algae in the ocean. A specific requirement for cobalt not involving B12 is seen in marine cyanobacteria and bloom-forming prymnesiophytes (including Emiliania huxleyi), but the biochemical basis for this is not known. Both zinc and cobalt additions have been shown to stimulate phytoplankton growth in bottle incubation experiments in the subarctic Pacific and in some coastal upwelling regimes along the eastern margin of the Pacific, but the effects were modest relative to those for added iron. However, zinc addition had a large effect on algal species composition, and preferentially stimulated the growth of coccolithophores, an algal group largely responsible for calcium carbonate formation in the ocean. Biogenic CaCO3 formation helps regulate the alkalinity (acid–base balance) of ocean water, which in turn affects oceanic CO2 concentrations, and air– sea flux of this important greenhouse gas. By influencing the growth of coccolithophores, zinc could indirectly affect atmospheric CO2 levels and global climate. Copper occurs in cytochrome oxidase, a key protein in respiratory electron transport, and in plastocyanin, which substitutes for the iron protein cytochrome c6 in photosynthetic electron transport in oceanic phytoplankton. It is also an essential component of the high-affinity iron transport system of many eukaryotic algae. Because copper is needed for iron uptake and can metabolically substitute for iron, co-limitations can occur for Cu and Fe, as observed in some diatoms. Nickel and molybdenum, like iron, play important roles in nitrogen assimilation. Nickel occurs in the enzyme urease, and thus is required by phytoplankton grown on urea as a nitrogen source. It also occurs in Ni-superoxide dismutase found in many marine cyanobacteria, which, like the Mn and Fe forms of the enzyme, removes harmful superoxide radicals from cells. Little is currently known about the potential for nickel limitation in the ocean. Molybdenum occurs with iron in the enzymes nitrate reductase and nitrite reductase and in nitrogenase, and consequently is utilized in nitrate assimilation and N2 fixation. Along with the Fe–Mo enzyme, there are two other isoforms of nitrogenase, a primitive less-efficient form containing only iron in its active center, and another which contains iron and vanadium. Thus molybdenum is not absolutely essential for dinitrogen fixation, although the predominance of the more efficient Fe–Mo isoform in the modern ocean helps to minimize iron limitation of


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nitrogen fixation. Because of its high concentration in seawater (c. 105 nM), Mo does not appear to limit algal growth or N2 fixation in the ocean. The metalloid selenium is also essential for the growth of many marine phytoplankton. It occurs in glutathione peroxidase, an enzyme that degrades hydrogen peroxide, and thus is important in antioxidant protection. However, it is likely that selenium has other as-yet-unidentified metabolic functions. The potential for selenium limitation in the ocean is currently unknown.

Biological Feedback on Seawater Chemistry Trace elements not only influence the productivity and species composition of planktonic communities, but the plankton have a profound effect on the chemistry and cycling of these elements on a variety of temporal and spatial scales (Figure 1). The most obvious example is the effect of algal uptake, particulate settling, and regeneration cycles on the vertical distribution and interocean transfer of trace element nutrients (Fe, Zn, Cd, Ni, Cu, and Se; Figures 2–4). In addition, bacteria largely mediate the removal of dissolved manganese and cobalt from subsurface seawater via the formation of Mn(IV) and Co(III) oxides. There is evidence that the organic ligands that strongly bind iron, copper, zinc, and cobalt are produced either directly or indirectly by the biota. In the North Pacific, the organic ligands that strongly bind copper occur at highest levels at the depth of maximum productivity, and decrease below the euphotic zone. Ligands having the same copper-binding strength are produced by Synechococcus, an abundant group of oceanic cyanobacteria. There is evidence that these organisms produce the chelators to detoxify copper by decreasing free cupric ion concentrations. The organic ligands that strongly bind iron(III), cobalt, and zinc also have a beneficial effect. The iron ligands tightly bind ferric ions in soluble chelates and thereby minimize the abiotic removal of iron from seawater via the formation of insoluble ferric oxides or ferric ion adsorption onto particulate surfaces. Without such chelating ligands, iron concentrations would likely be much lower, and the productivity of the ocean would be greatly reduced. The Co(III)-binding ligands serve a similar function in limiting the formation of insoluble Co(III) oxides, a major mechanism for removal of cobalt from seawater. Recent culture experiments and seawater incubation experiments suggest that these ligands are produced by the cyanobacterial genus Synechococcus,

whose growth may be limited by cobalt in some regions of the ocean. Zinc chelators also serve a beneficial function, not only by minimizing abiotic scavenging of zinc in surface waters, but also by preventing the extremely efficient uptake systems of eukaryotic phytoplankton from completely depleting this essential micronutrient element from surface ocean waters. Thus trace element nutrients and marine plankton comprise an interactive system in the ocean in which each exerts a controlling influence on the composition and dynamics of the other (Figure 1). On longer geological timescales, the feedback interactions between the biota and trace metal chemistry and availability have been profound. Currently, the air we breathe and virtually the entire ocean, with the exception of a few isolated anoxic basins (e.g., the Black Sea), contain free dioxygen molecules (O2), generated over billions of years from its release as a byproduct of oxygenic photosynthesis. The presence of free O2 sets the redox state of modern ocean toward oxidizing conditions, which as noted previously, limits the solubility of essential transition metals (Fe, Co, and Mn) whose stable oxidation states under these conditions are insoluble Co(III) and Mn(IV) oxides or sparingly soluble Fe(III) oxides. However, prior to the advent of oxygenic photosynthesis c. 3 billion years ago, the chemistry of the ocean was far different from that which exists today. There was no free oxygen and the entire ocean and Earth’s surface was much more reducing. Under these conditions, the stable redox state of Fe, Mn, and Co was soluble Fe(II), Mn(II), and Co(II), and that of copper was Cu(I). Furthermore, the stable redox form of sulfur was sulfide ( 2 oxidation state), rather sulfate (þ 6 oxidation state), which occurs in present-day seawater at a relatively high concentration (28 mM). The presence of moderate to high levels of sulfide greatly restricted the availability of zinc, copper, molybdenum, and cadmium, which form insoluble sulfide precipitates; but it had a much lesser impact on other metals (Mn2þ, Fe2þ, Co2þ, and Ni2þ) whose sulfides are much more soluble. Thus, early life in the ocean evolved in an environment of high availability of Fe, Mn, Co, and Ni and low availabilities of Zn, Mo, Cu, and Cd, contrasting the situation in the modern ocean. Given the utility of Fe as a redox catalyst and its relative abundance in the Earth’s crust and ancient ocean, it is perhaps not surprising that this metal was utilized in the evolution of the major redox catylysts of life. It occurs in high amounts in the redox centers of nitrogenase responsible for dinitrogen fixation and in the various proteins and protein complexes involved in oxygenic photosynthesis (photosystem I, photosystem II,



cytochorme b6/f complex, ferredoxin, and cytochrome c). In addition, the abundant soluble manganese in the early ocean was utilized in the wateroxidizing centers of photosystem II. The combined action of these iron- and manganese-containing biological redox catalysts provided for efficient fixation of N2 and CO2 needed for production of proteins and other biological compounds. The concomitant release of O2 from photosynthesis and sequestration of organic carbon in marine sediments and sedimentary rocks, slowly (over 1–2 billion years) oxidized ferrous iron to ferric oxides and sulfide species to soluble sulfate, ultimately resulting in the buildup of free O2 first in the surface ocean and atmosphere, and gradually in the ocean as a whole. The precipitation of ferric oxides from the sea has resulted in the chronic Fe limitation of carbon fixation and N2 fixation that we currently observe in the ocean. However, this negative effect is more than balanced by the usefulness of molecular oxygen in highly efficient oxygen-dependent respiration utilized by all present-day aerobic microbes, plants, and animals. Furthermore, the release of zinc, copper, molybdenum, and cadmium from insoluble sulfides allowed for the subsequent evolution of numerous new enzymes utilizing these metals, which appear to have evolved following the appearance of free O2. Thus, evolution has involved a continuous feedback between biological systems and the surrounding chemical environment, with biological trace metal catalysts playing a central mediating role in this process.

Glossary ATP Adenosine triphosphate; a high-energy compound produced in photosynthesis and respiration which is used as the main energy currency of cells. Chemical speciation The different chemical forms of trace elements. Chelate A strong complex between an organic ligand and a metal. Chelation The reaction of a metal with an organic ligand to form a chelate. Chelator An organic ligand that forms stable complexes with metal ions. Cytochrome b6/f complex An iron-rich protein complex involved proton pumping and ATP synthesis in photosynthesis. Fe(II), Fe(III) Iron with oxidation states of þ 2 and þ 3, respectively, also referred to as ferrous and ferric iron. Ferredoxin A soluble iron–sulfur protein involved in photosynthetic electron transport.

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Ferric hydrolysis species Inorganic complexes of iron(III) with one to four hydroxide ions: FeOH2 þ , FeðOHÞþ 2 , Fe(OH)3, and FeðOHÞ4 . Metalloenzyme An enzyme containing a metal as an essential functional component. Mn(II), Mn(III), Mn(IV) Manganese with oxidation states of þ 2, þ 3, and þ 4, respectively. NADPH The reduced form of nicotinamide adenine dinucleotide phosphate, which is produced in photosynthesis and serves as the primary reductant molecule in plant cells. Nitrogenase An iron-containing enzyme complex responsible for nitrogen fixation. Photosystem I and photosystem II The two photochemical reaction centers in photosynthesis. Redox Chemical reduction and oxidation. Siderophore A high-affinity organic ligand produced by bacteria to complex iron and facilitate its intracellular uptake. Superoxide radical A free radical of chemical structure (dO 2 ) formed from the single electron reduction of molecular oxygen.

See also Carbon Cycle. Iron Fertilization. Nitrogen Cycle. Primary Production Processes. Transition Metals and Heavy Metal Speciation.

Further Reading Anbar AD and Knoll AH (2002) Proterozoic ocean chemistry and evolution: A bioinorganic bridge? Science 297: 1137--1142. Barbeau K, Rue EL, Trick CG, Bruland KW, and Butler A (2003) Photochemical reactivity of siderophores produced by marine heterotrophic bacteria and cyanobacteria based on characteristic Fe(III) binding groups. Limnology and Oceanography 48: 1069--1078. Boyle E, Edmond JM, and Sholkovitz ER (1977) The mechanism of iron removal in estuaries. Geochimica Cosmochimica Acta 41: 1313--1324. Brand LE, Sunda WG, and Guillard RRL (1983) Limitation of marine phytoplankton reproductive rates by zinc, manganese and iron. Limnology and Oceanography 28: 1182--1198. Bruland KW (1989) Complexation of zinc by natural organic ligands in the central North Pacific. Limnology and Oceanography 34: 269--285. Bruland KW (1992) Complexation of cadmium by natural organic ligands in the central North Pacific. Limnology and Oceanography 37: 1008--1017. Bruland KW and Franks RP (1983) Mn, Ni, Cu, Zn and Cd in the western North Atlantic. In: Wong CS, Boyle E, Bruland KW, Burton JD, and Goldberg ED (eds.)


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Trace Metals in Sea Water, pp. 395--414. New York: Plenum. Coale KH (1991) Effects of iron, manganese, copper, and zinc enrichments on productivity and biomass in the subarctic Pacific. Limnology and Oceanography 36: 1851--1864. Coale KH, Johnson KS, Chavez FP, et al. (2004) Southern Ocean iron enrichment experiment, carbon cycling in high- and low-Si waters. Science 304: 408--414. Crawford DW, Lipsen MS, Purdie DA, et al. (2003) Influence of zinc and iron enrichments on phytoplankton growth in the northeastern subarctic Pacific. Limnology and Oceanography 48: 1583--1600. Cutter GA and Bruland KW (1984) The marine biogeochemistry of selenium: A reevaluation. Limnology and Oceanography 29: 1179--1192. da Silva JJRF and Williams RJP (1991) The Biological Chemistry of the Elements. Oxford, UK: Clarendon. Donat JR and Bruland KW (1995) Trace elements in the oceans. In: Salbu B and Steinnes E (eds.) Trace Elements in Natural Waters, pp. 247--281. Boca Raton, FL: CRC Press. Duce RA and Tindale NW (1991) Atmospheric transport of iron and its deposition in the ocean. Limnology and Oceanography 36: 1715--1726. Falkowski PG (1997) Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean. Nature 387: 272--275. Ho T, Quigg A, Findel ZV, et al. (2003) The elemental composition of some marine phytoplankton. Journal of Phycology 39: 1145--1159. Hutchins DA, Hare CE, and Weaver RS (2002) Phytoplankton iron limitation in the Humboldt Current and Peru Upwelling. Limnology and Oceanography 47: 997--1011. Ito Y and Butler A (2005) Structure of synechobactins, new siderophores of the marine cyanobacterium Synechococcus sp. PCC 7002. Limnology and Oceanography 50: 1918--1923. Johnson KS, Gordon RM, and Coale KH (1997) What controls dissolved iron concentrations in the world ocean? Marine Chemistry 57: 137--161. Kustka AB, San˜udo-Wilhelmy S, Carpenter EJ, et al. (2003) Iron requirements for dinitrogen and ammonium supported growth in cultures of Trichodesmium (IMS 101): Comparison with nitrogen fixation rates and

iron:carbon ratios of field populations. Limnology and Oceanography 48: 1869--1884. Maldonado MT and Price NM (1996) Influence of N substrate on Fe requirements of marine centric diatoms. Marine Ecology Progress Series 141: 161--172. Martin JH, Gordon RM, Fitzwater S, and Broenkow WW (1989) VERTEX: Phytoplankton/iron studies in the Gulf of Alaska. Deep-Sea Research 36: 649--680. Morel FMM and Price NM (2003) Biogeochemical cycles of trace metals in the oceans. Science 300: 944--947. Morel FMM, Reinfelder JR, Roberts SB, et al. (1994) Zinc and carbon co-limitation of marine phytoplankton. Nature 369: 740--742. Rue EL and Bruland KW (1995) Complexation of iron(III) by natural organic ligands in the central North Pacific as determined by a new competitive ligand equilibration/adsorptive cathodic stripping voltammetric method. Marine Chemistry 50: 117--138. Saito MA, Moffett JW, and Ditullio GR (2004) Cobalt and nickel in the Peru Upwelling region: A major flux of labile cobalt utilized as a micronutrient. Global Biogeochemical Cycles 18: GB4030. Saito MA, Sigman DM, and Morel FMM (2003) The bioinorganic chemistry of the ancient ocean: The coevolution of cyanobacterial metal requirements and biogeochemical cycles at the Archean–Proterozoic boundary? Inorganica Chimica Acta 356: 308--318. Shaked Y, Kustka AB, and Morel FMM (2005) A general kinetic model for iron aquisition by eucaryotic phytoplankton. Limnology and Oceanography 50: 872--882. Strzepek RF and Harrison PJ (2004) Photosynthetic architecture differs in coastal and oceanic diatoms. Nature 431: 689--692. Sunda WG and Huntsman SA (1995) Cobalt and zinc interreplacement in marine phytoplankton: Biological and geochemical implications. Limnology and Oceanography 40: 1404--1417. Sunda WG and Huntsman SA (1997) Interrelated influence of iron, light and cell size on marine phytoplankton growth. Nature 390: 389--392. Sunda WG and Huntsman SA (2000) Effect of Zn, Mn, and Fe on Cd accumulation in phytoplankton: Implications for oceanic Cd cycling. Limnology and Oceanography 45: 1501--1516. Turner DR and Hunter KA (eds.) (2001) The Biogeochemistry of Iron in Seawater. New York: Wiley.


TRACER RELEASE EXPERIMENTS A. J. Watson, University of East Anglia, Norwich, UK J. R. Ledwell, Woods Hole Oceanographic Institution, Woods Hole, MA, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3004–3009, & 2001, Elsevier Ltd.

atmosphere. The signatory nations are thus committed to controlling the rate of its production. However, for any realistic future emission scenario, SF6 will remain insignificant (o1%) as a contributor to the anthropogenic greenhouse effect for the foreseeable future.

Mixing Experiments in the Deep Ocean

Introduction Since the mid 1980s, analytical and engineering techniques have been developed to enable the compound sulfur hexafluoride (SF6) to be used as a tracer for oceanographic experiments. SF6 is a stable and inert substance with an exceptionally low level of detection, and its use enables large bodies of water to be unambiguously marked, allowing the investigator to keep track of a particular parcel of water. Three kinds of experiment have thus far made use of this technique: (1) measurement of mixing and transport integrated over large regions; (2) estimates of gas transfer velocities at the surface of the sea; (3) open ocean iron enrichment experiments. This article briefly describes the techniques used, and the major results from each of these types of process study.

The Tracer Sulfur hexafluoride is an inert perfluorine, routinely detectable in sea water at B0.01 fmol kg1 by electron-capture gas chromatography (1 fmol ¼ 1015 mol). At room temperature and pressure SF6 is a gas, but it forms a dense (r ¼ 1880 kg m3) liquid at pressures exceeding 20 bar. It is extremely stable in the environment and, other than being an asphyxiant, the pure compound has no known toxic effects. It is produced commercially largely (B80%) for use as a gaseous insulator in high-voltage installations. Much of this industrial production eventually finds its way into the atmosphere. The atmospheric mixing ratio was about 4 1012 in 1999, and is growing at about 7% per year. Its solubility is very low, so that the surface concentrations in equilibrium with the atmospheric concentration are on the order of 1 fmol kg1. The combination of very low detection limit, nontoxicity, low marine background concentration, ease of analysis and inertness make SF6 a nearly ideal tracer. SF6 is included in the Kyoto Protocol because, molecule-for-molecule, it is a powerful greenhouse gas with a long (41000 years) lifetime in the

To measure diapycnal mixing (i.e. mixing acrossdensity surfaces) by tracer release, the tracer is released, as near as possible, onto a single, well-defined density surface, and its subsequent spread onto neighboring surfaces is monitored. If the mixing occurs in accordance with Fick’s law, the square of the mean width of the concentration distribution increases linearly with time, the rate of increase being a direct measure of the diffusivity. The advantage of this strategy compared to the documentation of temperature or velocity microstructure, is that it gives an unambiguous measurement integrated over a substantial time and space scale. In practice, in the open ocean these scales are of order months or years, and hundreds or thousands of kilometers – hence also the method’s main disadvantage, which is that it must be done on a large scale. At the time of writing, five experiments of this kind have been initiated in the open ocean. The first two, relatively small-scale releases, were made in the ocean-floor basins off the coast of Southern California and the remaining three in the thermocline of the North Atlantic, the deep Brazil Basin and the central Greenland Sea. Below we describe the release method used in most of these experiments, and the results of the North Atlantic experiment in more detail. Mixing rates from all five experiments are then compared. Release Method

Sulfur hexafluoride is very insoluble; for small-scale experiments it can be dissolved by presaturating drums or tanks of water with the gas. However, the practical limit for the amount which can be injected in this way is a few moles, sufficient for tracer experiments on the 10–100 km scale only. For large open ocean releases, we designed an injection package which releases liquid SF6 into water by pumping it through fine orifices at high pressure, so that it breaks into an emulsion of fine droplets on contact


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with the sea. These droplets are sufficiently small that they dissolve before they have settled an appreciable distance. The apparatus is designed to allow the accurate delivery of SF6 at rates of up to 3 kg h1 onto a given ‘target’ density surface at any depth greater than 200 m in the ocean, when towed behind a ship on a conducting cable. In use, the injector was controlled by a computer in the laboratory of the ship. The output of the CTD was used to calculate in real time the density of the water at the package, and compare it to the ‘target’ density. The computer issued commands to the automated winch to haul in wire if the density was higher than the target, or pay out if it was significantly lower. During the North Atlantic Tracer Release Experiment (NATRE) this system was able to deliver tracer with an overall RMS accuracy of 72 m from the target surface. With such an injection system, it is practical to initiate experiments using several hundred kilograms of tracer, sufficient to enable investigations at the ocean-basin scale.

150

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The tracer results from NATRE have been reported in detail. Major findings were that the diapycnal diffusivity was 0.12 cm2 s1 for the first 6 months, and then 0.17 cm2 s1 for the subsequent 24 months. The mean vertical profile for each survey was nearly Gaussian, and as a set they illustrate an approximate solution of the diffusion equation in one dimension (Figure 1). The result that the diapycnal diffusivity in the pycnocline is of order 0.1 cm2 s1 confirms estimates based on internal wave dynamics and on measurements of turbulent dissipation rates. Some analyses of the penetration of transient tracers into the deep pycnocline also have implied diffusivities on the order of 0.1 cm2 s1. Values of diffusivity of 1 cm2 s1 were inferred by Munk’s classic ‘abyssal recipes’ analysis, but this was for depths between 1000 and 4000 m and included boundary processes as well as interior processes. It is now clear that 1 cm2 s1 is an overestimate for the interior pycnocline. The lateral dispersion of the tracer revealed surprisingly efficient mechanisms of stirring at scales from 0.1 to 30 km. The lateral diffusivity setting the width of tracer streaks at 6 months was found to be about 2 m2 s1. The mechanism is not well understood, but may be due to shear dispersion by vortices generated during the adjustment to diapycnal mixing events. The experiment did confirm the predictions of C. Garrett, that a tracer patch remains streaky only for a year or so, after which time the exponential growth of the area actually tainted by the

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NATRE: Overview of Results

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Figure 1 Mean vertical profiles from NATRE at 0, 5, 6, 12 and 30 months after the initial survey. The SF6 concentration has been averaged on isopycnal surfaces, approximately, and plotted versus height above the target isopycnal surface using the mean relation between depth and density for the 12-month survey. The profiles are normalized to have equal areas. The initial profile (y .) is allowed to run off the graph so that the others are clear.

tracer streaks catches up with a power-law growth of the overall area occupied by the tracer patch. It is important in a tracer study of mixing in the ocean to measure the hydrodynamic forcing, and also to measure hydrodynamic parameters that are believed to be useful for estimating diffusivities, so that existing theories can be tested. Several groups were involved in profiling fine structure and microstructure during NATRE. Dissipation of turbulent kinetic energy and temperature variance measured by profiling instruments gave estimates of diapycnal diffusivity which agreed closely with the tracer results. Measurements of the fine structure have helped reveal the roles of shear and double diffusive gradients in driving the mixing. Dependency of Diapycnal Mixing and Buoyancy Frequency from Tracer Release Experiments

Figure 2 shows diapycnal diffusivities as a function of buoyancy period for the deep ocean tracer release experiments so far published. Except for the recent


TRACER RELEASE EXPERIMENTS 4.5

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_1

Diapycnal mixing rate (cm s )

Gas exchange is dependent on environmental conditions that affect the near-surface turbulence in the 4.0 sea and which are not easily reproduced in laboratory facilities, such as wind speed, sea state, and the 3.5 chemical state of the air–sea interface. In laboratory wind-wave facilities for example, a strong depend3.0 Brazil Basin ence on wind speed is observed, but the functional form depends on the experimental set-up. As a con2.5 sequence, though substantial theoretical understanding has been gained from experiments in 2.0 laboratory facilities, there has also been a need to assemble a body of gas transfer measurements made 1.5 at sea. Santa Cruz The first aqueous use of SF6 as a tracer was the Basin 1.0 measurement of gas exchange in lakes by R. WanGreenland Sea ninkhof, in 1985. Lake experiments are compara0.5 tively easy to set up and perform, and give absolute Santa Monica estimates of gas exchange. The basis of the technique Basin NATRE 0 is to keep track of the total amount of gas in the lake. 0 0.5 2.0 2.5 3.0 1.0 1.5 The results of the first experiment gave unambiguous 1/ N (hours) evidence in a field situation, for a strong dependence of gas exchange on wind speed, and the data form Figure 2 Vertical mixing coefficients for five tracer release experiments in the open ocean, plotted as a function of 1/N where the calibration for the ‘Liss–Merlivat’ formulation of N is the buoyancy frequency. For discussion see text. gas exchange. However, the gas exchange rates found in that experiment, when scaled and applied to Brazil Basin experiment, the data indicate correlation carbon dioxide, are lower by about a factor of two between the two, as would be expected if the forcing than might be expected from an analysis of the glowere in some sense held constant. However, the reader bal 14C budget of the ocean. This uncertainty in should beware of such relationships, as the Brazil marine gas exchange rates remains unresolved up to Basin result shows. There is evidence from internal the present. In recent years, many investigators who wave phenomenology and energy dissipation meas- need to parameterize gas exchange as a function of urements that the diffusitivity in the interior of the wind speed, have bracketed the uncertainty by apocean, when driven only by the background internal plying both the Liss–Merlivat relation (scaled to wave field, is independent of the buoyancy period with agree with the lake SF6 experiment), and a relation a value of approximately 0.05 cm2 s1. The only one due to Wanninkhof that is scaled to agree with global of our experiments that has been conducted in the 14C values. interior of the ocean, well away from boundaries, was NATRE, and that was probably influenced by salt The Dual Tracer Technique fingering. The measurements shown in Figure 2 are those made before the tracer-containing water had This long-standing uncertainty in marine gas extime to contact the boundaries; mixing increased change rates provided a good reason to adapt the dramatically in the California basin experiments once lake SF6 technique to the measurement of gas exsuch contact occurred. Nevertheless, the energy input change at sea. However, whereas in a lake it was easy for all but the NATRE site may have been enhanced to determine the total amount of tracer present and by the proximity of the boundaries. If this is the reason the area over which it is spread, in the open ocean the why most of these experiments show elevated values, tracer release is unenclosed and dilutes into a conit is evident that in many situations of interest, the stantly larger volume of water. A means must be diffusivity, and presumably the energy flux through found to account for this dilution. Theoretically, this the internal wave field, must be enhanced even at could be accomplished by releasing a nonvolatile conservative tracer with the gaseous one, and then considerable distance from boundaries. use the change in ratio between the two to define gas exchange rates. In practice, no such ideal conservaGas Exchange Experiments tive nonvolatile tracer is available, so instead SF6 and The rate of air–sea gas transfer is a parameter which 3He were released, two volatile tracers having very is needed in a wide range of biogeochemical studies. different molecular diffusivities. When the water



column is well mixed and of constant depth H, the ratio r ¼ c2 =c1 of the concentrations of the tracers (in excess of any concentration in equilibrium with the atmosphere) evolves according to the equation:

where k1 and k2 are the gas transfer velocities appropriate to each tracer. This suggested that in the right environment, that is a shallow sea, well-mixed and with a constant depth, measurement of the tracer ratio could be used to define the difference between the two gas transfer rates. If another relation between the gas transfer rates could be defined, the ‘dual tracer’ technique would enable absolute values for k1 and k2 to be derived. For this second relation, dual tracer experimenters have used a power law dependence of gas transfer velocities on Schmidt number (the ratio of kinematic viscosity of water to the diffusivity of the gas): k1 ¼ k2

North Sea Georges Bank 60

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For most conditions in which bubbles and spray are not affecting gas exchange, n ¼ 0:5. This result is derived from models and supported by measurements, in the laboratory and on lakes. At very low wind speeds when the sea is glassy smooth, this relation does not hold and n ¼ 0:67 is the theoretical result, but this condition is very rarely met at sea. In rough seas where substantial bubble-mediated gas transfer may occur, the theory is more complex and different assumptions have been made to derive absolute values under these conditions. Recent theoretical work suggests that the square-root assumption is reasonably accurate even in the presence of bubble-mediated transfer, though care is needed in scaling the results obtained using these insoluble tracers to more soluble gases such as carbon dioxide. In one experiment, a third tracer, bacterial spores specially treated to be suitable for this purpose, were used as a nonvolatile tracer, and these results also support the use of the square-root law. Figure 3 shows a compilation of results from dualtracer experiments at sea. The dual-tracer results confirm the strong dependence of gas exchange on wind speed. They generally lie between the Liss– Merlivat and Wanninkhof parameterizations. In the light of recent results, concerning the effect of ubiquitous natural organic films, we can hypothesize that the trends in these data are due to the decreasing effect of organics as one moves away from coastally influenced sites out into the open ocean. The

(1) (2)

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Wind speed at 10 m (m s ) Figure 3 Compilation of dual tracer gas exchange measurements. The North Sea results include some previously published data for which revised wind speeds have been estimated using the procedures detailed by P. D. Nightingale. Data from (1) Wanninkhof (1992) and (2) Liss and Merlivat (1986).

Wanninkhof parameterization, being tuned to global C exchange rate, is most affected by the open ocean and the Liss–Merlivat formulation, originally calibrated from the result of lake experiments, the most affected by organics. The two data sets lie in between these. Georges Bank might be expected to be less coastally influenced than the North Sea, and the trend in the results is consistent with that expectation. 14

Small-scale Surface Patch Experiments for Biogeochemical Studies A practical problem in carrying out open-sea dualtracer gas exchange experiments was the difficulty of keeping track of the released tracer patch. To overcome this, in the late 1980s instrumentation was built which took advantage of the uniquely fast gas chromatographic analysis for SF6. Gas chromatography is normally a slow, batch process, but for SF6 using a molecular sieve column, the actual separation takes only 30 seconds and the entire analysis can be completed in three minutes. Thus it was possible to build an instrument which continually measured the concentration of SF6 in a supply of water, and use this to ‘chase’ the tracer patch from a ship. This opened the possibility of using the tracer



In-situ Iron Enrichment Experiments

The first use of the tracer technique to guide biogeochemical studies was in the IRONEX experiments in the equatorial Pacific. At about the time the tracer-release technique had been developed for gasexchange experiments, the idea was suggested of testing the ‘Iron hypothesis’ of phytoplankton limitation by releasing a large amount of iron in the surface waters of, for example, the equatorial Pacific. A difficulty was that if the experiment was too small, then the iron-enriched patch would be easily lost, whereas if it were large enough to be easily found (probablyB100 km in scale) then it would be logistically difficult and expensive. The use of the tracer release to guide a 10-km scale experiment was an obvious next step, and the design for such a study was published in 1991. The first two unenclosed iron-enrichment studies were carried out in 1993 and 1995 in the Equatorial Pacific. In both, nanomolar concentrations of iron were induced in the surface layer by release of iron sulfate, the patches being labeled by SF6 addition. The SF6(o1 mol in total) was added in a constant ratio to the initial addition of iron, the tracer component was then used as a guide to keep track of the affected patch of ocean. Sampling could be reliably categorized as ‘in’ or ‘out’ the patch, even after all the measurable iron had disappeared from solution. In the second study, the main experiment included reseeding the patch with iron, but not tracer, twice after the initial release. Important secondary aims of the tracer component of the experiments have been the study of mixing rates both horizontally in the mixed layer, and vertically across the thermocline. Figure 4 shows a summary of the results for the effect of the iron releases on surface water fugacity of carbon dioxide (fCO2) from Ironex I and II. fCO2 is plotted against SF6 measured in the water on paired samples, for various times following the initiation of the experiment. Such a plot shows whether the fCO2 (or any other variable of interest) develops a

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to guide experiments to investigate the biology or chemistry of an accurately marked patch of surface water, over a period of days to weeks. Such ‘lagrangian’ experiments have frequently been performed in the past using drogued drifting buoys to mark movement of water. However, an early observation from the trial tracer experiments made in the English Channel was that such buoys do not normally stay co-located with a patch of water marked by a tracer release. Surface buoys are subject to windage and tend to slip downwind of the marked water.

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Figure 4 Linear regressions of sea surface values of fugacity of CO2 (fCO2) with SF6 concentration, for specified periods after the start of the Ironex I (lower) and Ironex II (upper). Representative data points are shown, for the ‘day 4–5’ period during Ironex I (J), and the ‘day 6–8’ period during the Ironex II (}). The hashed region around the two regression lines which correspond to these data shows the confidence limit (3-s) on the slope of the line. (Data from Cooper et al., 1996; Law et al., 1998; Watson et al., 1994.)

relationship with the tracer concentration over time. It is a useful summary of the effects observed even if the evolution of the patch shape is complex and not readily mapped in space. Data at low and background SF6 show the ‘control’ condition, outside the patch, while data at high SF6 show the evolution of the center of the marked water. Figure 4 shows the contrasting results of the experiments. Ironex II produced an intense bloom of diatoms which fixed substantial carbon, resulting in a drawdown of carbon dioxide in the surface water which at its peak amounted to 70–80 matm below the starting ‘outside patch’ condition. The drawdown continued to build up beyond the first week of the experiment, and a substantial signal was left in the water even after the bloom began to fade. By contrast, during Ironex I (shown on the same fCO2 scale) the effect on the carbon concentration of the surface water was small, only 10–20% of that seen on the Ironex II, and it was already fading by the end of the first week. As measured by carbon uptake, the response of the two experiments in the first 3–4 days


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is similar. The divergence between the two time histories after that time is probably attributable to the fact that there were further additions of iron to the patch water on days 3 and 7 of Ironex II, but only the single initial iron enrichment during Ironex I, after which the disappearance of the added Fe, presumably by sedimentation, occurred very quickly. The simplest possible interpretation of the Ironex results is therefore that iron supply, when increased in the equatorial Pacific, allows diatoms to bloom and the chemistry of the water to change, providing that the iron concentration is elevated for several days at least.

routine. Three experiments at the 1000-km scale have so far been initiated, to measure ocean mixing on these scales. There have been more than twenty smaller scale experiments, of increasing sophistication, since they were first begun in 1986. For topics to which they are suited, such as iron limitation, biogeochemical budgets, gas exchange and diapycnal mixing rates, these experiments have enabled something of the precision of the land-based laboratory investigation to be brought to bear in at-sea oceanography.

See also Conclusion Several further applications of the tracer technique are presently under way. Two ‘large scale’ experiments in the open ocean are being actively monitored, in the Greenland Sea and the Brazil Basin. Numerous useful subsurface experiments can be imagined. However, because of the conflict between such subsurface release experiments and the use of SF6 as a transient tracer, there is a need to establish a forum by which the wider oceanographic community can have input into the planning of prospective release experiments. Small-scale releases in surface waters should not normally compromise the transient tracer signal. One obvious application now under way is that of iron fertilization experiments to examine the extent to which ‘high nutrient low chlorophyll’ regions other than the equatorial Pacific are limited by iron availability. The recent Southern Ocean Iron Enrichment Experiment (SOIREE) has shown unequivocal evidence that iron supply does affect the biology of that region. This experiment was carried out during sometimes stormy weather, confirming that the patch-tracking technique works well in the open ocean under storm conditions. To summarize, experiments using SF6 tracer in the open ocean are now reduced to practice, if not

Air–Sea Gas Exchange. Long-Term Changes. Tracers of Ocean Productivity.

Tracer

Further Reading Cooper DJ, Watson AJ, and Nightingale PD (1996) Large decrease in ocean-surface CO2 fugacity in response to in-situ iron fertilization. Nature 383: 511--513. Law CS, Watson AJ, Liddicoat MI, and Stanton T (1998) Sulphur hexaflouride as a tracer of biogeochemical and physical processes in an open-ocean iron fertilisation experiment. Deep-Sea Research II 45: 977--994. Ledwell JR, Montgomery ET, Polzin KL, et al. (2000) Evidence for enhanced mixing over rough topography in the abyssal ocean. Nature 403: 179--182. Ledwell JR, Watson AJ, and Law CS (1998) Mixing of a tracer in the pycnocline. Journal of Geophysical Research 103: 21499--21529. Watson AJ, Law CS, Van Scoy K, et al. (1994) Minimal effect of iron fertilization on sea-surface carbon dioxide concentrations. Nature 371: 143--145. Watson AJ, Messias M-J, Fogelqvist E, et al. (1999) Mixing and convection in the Greenland sea from a tracer release. Nature 401: 902--904. Watson AJ, Upstill-Goddard RC, and Liss PS (1991) Air– sea gas exchange in rough and stormy seas measured by a dual-tracer technique. Nature 349: 145--147.


TRACERS OF OCEAN PRODUCTIVITY W. J. Jenkins, University of Southampton, Southampton, UK Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3020 –3026, & 2001, Elsevier Ltd.

Introduction Primary production is the process whereby inorganic carbon is fixed in the sunlit (euphotic) zone of the upper ocean, and forms the base of the marine food pyramid. It occurs when marine phytoplankton use sunlight energy and dissolved nutrients to convert inorganic carbon to organic material, thereby releasing oxygen. The total amount of carbon fixed during photosynthesis is called gross production, whereas the amount of carbon fixed in excess of internal metabolic costs is referred to as net production. It is understood that a significant fraction of the carbon fixed in this manner is rapidly recycled by a combination of grazing by zooplankton and in situ bacterial oxidation of organic material. New production is that portion of net production that is supported by the introduction of new nutrients into the euphotic zone. Traditionally, this has been regarded as production fueled by nitrate as opposed to more reduced forms of nitrogen, such as ammonia and urea. Some portion of the fixed carbon sinks out of the euphotic zone in particulate form, or is subducted or advected away as dissolved organic material from the surface layers by physical processes. This flux is regarded collectively as export production. The ratio of new (export) to net production, referred to as the f-ratio (e-ratio) can vary between 0 and 1, and is believed to be low in oligotrophic (‘blue water’), low productivity regions, and higher in eutrophic, high productivity regions. Finally, net community production is the total productivity in excess of net community metabolic cost. On sufficiently long space- and time-scales, it can be argued that new, net community, and export production should be equivalent in magnitude. Net production has been measured ‘directly’ by radiocarbon incubation experiments, whereby water samples are ‘spiked’ with radiocarbon-labeled bicarbonate, and the net rate of transfer of the radioisotope into organic matter phases determined by comparison of light versus dark incubations. Global maps of net productivity have been constructed on the basis of such measurements, and current

estimates indicate a global fixation rate of order 50 GT C a1 (1 GT ¼ 1015 g). Rates of export, new, and net community production are more difficult to determine directly, yet are of equal importance as determinants of biogeochemically important fluxes on annual through centennial timescales. Geochemical tracer techniques have been used to make such estimates, and offer significant advantages in that they are fundamentally nonperturbative, and integrate over relatively large space-scales and long time-scales. Conversely, such determinations must be viewed from the perspective that they are indirect measures of biogeochemical processes, and have characteristic implicit space- and time-scales, as well as boundary conditions, and sometimes ambiguities and model dependence. Further, the specific tracer or physical system used to obtain production estimates determines the type of productivity measured. Thus any treatment of geochemical tracer estimates must include a discussion of these attributes.

Measuring Oceanic Productivity with Tracers Just a few approaches will be discussed here. Other techniques have been used with some success, particularly with relation to particle interceptor traps, but this section will concentrate on basic mass budgeting approaches using water column distributions or seasonal cycling of tracers. There are three basic, yet fundamentally independent approaches that can be used. 1. Aphotic zone oxygen consumption rates that, when vertically integrated, provide a net water column oxygen demand that can then be related stoichiometrically to a carbon export flux. 2. Seasonal timescale euphotic zone mass budgets, particularly of oxygen, carbon, and carbon isotopes, which lead to estimates of net community production. 3. Tracer flux-gauge measurements of physical mechanisms of nutrient supply to the surface ocean, which place lower bounds on rates of new production. These techniques, summarized in Figure 1, yield estimates of subtly different facets of biological production. On annual timescales, however, these different modes of production should be very close to equivalent, and hence the results of these various


93

94

TRACERS OF OCEAN PRODUCTIVITY

Figure 1 A schematic of the upper ocean, showing material fluxes and various tracer constraints on primary production.

measurement approaches should be comparable. As shown below, their quantitative agreement coupled with their essential independence lends an inductive support to the validity of their results.

Aphotic Zone Oxygen Consumption Rates In the surface ocean air–sea gas exchange controls the composition of dissolved gases and phytoplankton release oxygen. Below, in the aphotic (nonsunlit) zone, oxygen is generally undersaturated, because bacterially mediated oxidation of sinking organic material consumes oxygen. Credible estimates of aphotic zone oxygen consumption rates have been made since the 1950s. However, the earliest quantitative linkage to primary production was in 1982. The principle behind it is dating water masses and dividing the age of the water mass into the observed oxygen deficit. Another approach involves correlating water mass age along streamlines with oxygen concentration (older water has less oxygen). This dating can be achieved by a technique such as tritium-3He dating, which uses the ingrowth of the stable, inert noble gas isotope 3He from the decay of the radioactive heavy isotope of hydrogen (tritium), according to: 3

12:45y

H - 3He

If surface waters are in good gas exchange contact with the atmosphere, then very little 3He will accumulate due to tritium decay. Once isolated from the surface, this 3He can accumulate. From the measurement of both isotopes in a fluid parcel, a tritium-3He age can be computed according to: 3 He t ¼ l ln 1 þ 3 ½ H 1

where l is the decay probability for tritium, and t is the tritium-3He age (usually given in years). Under typical Northern Hemispheric conditions with current technology, times ranging from a few months to a few decades can be determined. Although a conceptually simple approach, under normal circumstances mixing must be accounted for because it can affect the apparent tritium-3He age in a nonlinear fashion. Furthermore, in regions of horizontal oxygen gradients, lateral mixing may significantly affect apparent oxygen consumption rates. For example, following a fluid parcel as it moves down a streamline, mixing of oxygen out of the parcel due to large-scale gradients will masquerade as an augmentation of oxygen consumption rates. These issues can be accounted for by determining the three-dimensional distributions of these properties, and applying the appropriate conservation equations. With additional constraints provided by geostrophic velocity calculations, these effects can be separated and absolute oxygen


TRACERS OF OCEAN PRODUCTIVITY _1

95

_1

Oxygen utilization rate (µmol kg y ) 0

0

10

20

30

0

0

10

20

100 100

200

200

400

Depth (m)

Depth (m)

300

500 600

300

400

700 800

500

900 1000

600

(A) Sargasso Sea

(B) Eastern North Atlantic

Figure 2 Aphotic zone oxygen consumption rates as a function of depth for two locales in the subtropical North Atlantic. These consumption rates are based on tritium-3He dating and other tracer techniques.

consumption rates can be computed as a function of depth. Figure 2 shows profiles of oxygen consumption rates as functions of depth for two locales in the subtropical North Atlantic. Integration of these curves as a function of depth gives net water column oxygen demands of 6.571.0 mol m2 a1 for the Sargasso Sea and 4.770.5 mol m2 a1 in the eastern subtropical North Atlantic. Using the molar ratio of oxygen consumed to carbon oxidized for organic material (170 : 117), the flux of carbon from the euphotic zone above required to support such an oxygen demand can be calculated for the two regions (4.570.7 and 3.270.4 mol C m2 a1). The character of these estimates bears some consideration. Firstly, according to the definitions of primary production types described earlier, this represents a determination of export productivity. Secondly, the determinations represent an average over timescales ranging from several years to a decade or more. This is the range of ages of the water masses for which the oxygen utilization rate has been determined. Thirdly, the corresponding space-scales are of order 1000 km, for this is the region over which the age gradients were determined. Fourthly, although the calculation was done assuming that the required carbon flux was particulate material, it cannot distinguish between the destruction of a particulate rain of carbon and the in situ degradation of dissolved organic material advected along with the

water mass from a different locale. These characteristics must be borne in mind when comparing this with other estimates.

Seasonal Euphotic Zone Mass Budgets There have been three basically independent approaches to estimating net community production based on observation of the seasonal cycles of oxygen and carbon in the upper ocean. Photosynthesis in the euphotic zone results in the removal of inorganic carbon from the water column, and releases oxygen (Figure 3). Recycling of organic material via respiration and oxidation consumes oxygen and produces CO2 in essentially the same ratios. It is only that carbon fixation that occurs in excess of these processes, i.e., processes that result in an export of organic material from the euphotic zone, or a net biomass increase, that leaves behind an oxygen or total CO2 (SCO2) signature. Estimates of productivity based on euphotic zone oxygen or carbon budgets are consequently estimates of net community production. Such productivity estimates are characterized by seasonal to annual timescales, and spacescales of order of a few hundred kilometers. In subtropical waters, excess oxygen appears within the euphotic zone just after the onset of


96

TRACERS OF OCEAN PRODUCTIVITY _1

Total CO2 (µmol kg ) 0

55

20

100

55

2040

280

320

360

320

360

20

20 70

Depth (m)

50

150

208

200

5 0

40

80

120

(A)

160

200

240

Day number _1

Dissolved oxygen anomaly (µmol kg ) 0

10 0

100

_ 10

150

_ 20

200

0 (B)

_ 20

Depth (m)

50

40

80

_ 20

120

160

200

240

280

Day number

Figure 3 Euphotic zone seasonal cycles of total inorganic carbon (A) and oxygen (B) near Bermuda. Note the build-up of oxygen anomaly and reduction of total CO2 in the euphotic zone during the summer months due to photosynthetic activity.

stratification, and continues to build up throughout summer months. Use of the seasonal accumulation of photosynthetic oxygen in the upper ocean to estimate primary production is complicated by the fact that it tends to be lost to the atmosphere by gas exchange at the surface. Furthermore, temperature changes due to seasonal heating and cooling will change the solubility of the gas, further driving fluxes of oxygen across the air–sea interface. In addition, bubble trapping by surface waves can create small supersaturations. While such processes conspire to complicate the resultant picture, it is possible to use observations of noble gases (which do not undergo biological and chemical processing) and upper ocean physical models to interpret the seasonal cycle of

oxygen. These calculations have been successfully carried out at a variety of locations, including the subtropical North Atlantic and the North Pacific. In the Sargasso Sea, estimates of oxygen productivity range from 4.3 to 4.7 mol m2 a1. Using the molar ratio of oxygen released to carbon fixed in photosynthesis of 1.4 : 1, the carbon fixation rate is estimated to be 3.270.4 mol m2 a1. There is also a net seasonal decrease in SCO2 attributable to photosynthesis at these locations. Such decreases are simpler to use in productivity estimates, principally because air–sea interaction has a much weaker influence on SCO2. On the other hand, precise measurements are required because the photosynthetically driven changes are much smaller



compared with the background SCO2 levels. Because of these differences, estimates based on SCO2 seasonal cycles offer an independent measure of euphotic zone mass budgets. Finally, differences in the carbon isotopic ratio between organic and inorganic carbon, as well as atmospheric CO2, allow the construction of yet a third mass budget for the euphotic zone. There is a clear carbon isotope signature that can be modeled

97

as a function of primary production, air–sea exchange, and mixing with deeper waters.

Tracer Flux-gauge Determinations The third tracer constraint that may be used to determine primary production involves the use of ‘tracer flux gauges’ to estimate the flux of nutrients to

0

3

Mixed layer Del He (%)

_ 0.5

_ 1.0

_ 1.5

Equilibrium _ 2.0 83

(A)

84

85

86

87

88

86

87

88

Year 8

3

He flux % m d

_1

6

4

Average = 1.84 + 0.25% _1 md

2

0

_2 (B)

83

84

85 Year

Figure 4 An approximately 6 year history of surface water 3He isotope ratio anomalies (A) and computed flux to the atmosphere near Bermuda (B).


98


10

_1

Nitrate (µmol kg )

8

6

4

2

0

_2

0

2

3

4

6

8

10

Del He (%) Figure 5 The correlation of 3He isotope ratio anomaly (in %) and nitrate (in mmolkg 1 ) in the upper ocean near Bermuda for the period 1985–88.

the euphotic zone. This approach relies on the premise that the physical mechanisms that serve to transport nutrients to the euphotic zone from the nutrient-rich waters below also carry other tracers in fixed proportion. If the rate at which these other tracers are transported can be determined, and the nutrient to tracer ratio at the ‘source’ is known, then the corresponding nutrient flux may be inferred; that is: FNutrient ¼

Nutrient Tracer

FTracer Source

Inasmuch as there may be alternate, biologically mediated pathways (such as zooplankton migration), such a calculation would serve as an underestimate to the total nutrient flux. Measurements of the rare, inert isotope 3He in the mixed layer of the Sargasso Sea near Bermuda reveal a persistent excess of this isotope over solubility equilibrium with the atmosphere (Figure 4). The existence of this excess implies a flux of this isotope to the atmosphere, which can be calculated using the estimated gas exchange rate. Although 3He is produced in the water by the in situ decay of tritium, it can be shown that only about 10% of the observed flux can be explained by tritium decay within the euphotic zone. The greater portion of this 3He flux arises from the upward ‘exhalation’ of old tritium-

produced 3He from the waters below. That is, the 3 He flux observed leaving the surface ocean is largely the loss of this isotope from the main thermocline. The ocean–atmosphere flux of 3He shows a pronounced seasonal variation, with the greatest fluxes in the winter months. The winter maximum is due to high rates of gas exchange (more vigorous winter winds lead to higher gas exchange rates) and deeper winter convection. This is the time history of the 3He flux out of the upper ocean. The time history of the 3 He flux to the upper ocean may be different. However, the annual mean fluxes must be the same, since the winter mixed layer penetrates below the bottom of the euphotic zone. The annual average 3 He flux from the ocean surface near Bermuda is 1.8470.25%-m d1. To estimate the flux of 3He entering the euphotic zone from below, this flux must be corrected for the in situ production of 3He by the decay of tritium within the euphotic zone, which produces a 3He flux of 0.2070.02%-m d1. The resultant flux is thus 1.6470.25%-m d1. Insofar as there is a strong correlation between the concentrations of this isotope and nutrients within the waters below the euphotic zone (older waters are richer in both 3He and nutrients), the ratio of 3He to nutrient can be employed to compute nutrient flux. Figure 5 is a composite plot of 3He versus nitrate in the upper 600 m over a 3 year period. The slope of the relationship is 0.8770.05 mmol kg1%1.



Applying the flux equation presented above, a nitrate flux of 0.5670.16 mol m2 a1 is computed. Using the average biological C : N ratio of 6.6, this leads to a carbon fixation rate of 3.77 1.0 mol m2 a1. The estimate thus obtained is a local, annual-scale measure of new production. A similar calculation can be made by observing the long-term (decade timescale) trends in thermocline 3 He inventories. The long-term evolution of 3He inventory in the thermocline must respond to the opposing processes of production by tritium decay and ‘exhalation’ upward to the euphotic zone. Knowing the former gives the latter. Using nutrient-3He ratios, a gyre-scale, decadal average estimate of the nutrient flux to the euphotic zone can be obtained. A detailed analysis of the long-term trends of tritium and 3He in the upper 1000 m of the Sargasso Sea, coupled with the observed nitrate : 3He ratios, yields an estimate of 0.7070.20 mol m2 a1. This leads to a somewhat higher carbon fixation rate of 4.671.3 mol m2 a1. This estimate differs from the surface layer flux calculation in that it is a much longer-term average, since it depends on the very long-term evolution of isotopes in the thermocline. Moreover, it represents a very large-scale gyre-scale determination, rather than a local measure: horizons within the thermocline probably connect to regions of higher productivity further north.

Comparing Tracer-derived Estimates Although the various techniques described here are based on differing assumptions, and measure different types of production, they should be mutually consistent on annual or greater timescales. Table 1 is a comparison between the various estimates near Bermuda in the Sargasso Sea. A weighted average of these determinations gives a productivity of 3.670.5 mol (C) m2 a1 for the Sargasso Sea near Bermuda. The determinations are within uncertainties of each

99

Table 1 Comparison of tracer-derived estimates near Bermuda in the Sargasso Sea Type of determination

Type of production

Aphotic zone oxygen consumption rates Euphotic zone cycling

Export Tritium-3He dating production

4.5 7 0.7

Net Oxygen cycling community Carbon isotopes New Mixed layer 3He production Thermocline budgets

3.2 7 0.4

Tracer flux-gauge

Technique used

Carbon flux (mol m 2a 1)

3.8 7 1.3 3.7 7 1.0 4.6 7 1.3

other, although they utilize different tracer systems, are reliant on different assumptions, and are virtually independent of each other. This agreement provides some confidence as to their accuracy.

See also Air–Sea Transfer: N2O, NO, CH4, CO. Carbon Cycle. Primary Production Distribution. Primary Production Processes. Tritium–Helium Dating.

Further Reading Falkowski PG and Woodhead AD (1992) Primary Productivity and Biogeochemical Cycles in the Sea. New York: Plenum Press. Jenkins WJ (1995) Tracer based inferences of new and export primary productivity in the oceans. IUGG, Quadrennial Report 1263–1269. Williams PJ and le B (1993) On the definition of plankton production terms. ICES Marine Science Symposium 197: 9--19.


TRANSITION METALS AND HEAVY METAL SPECIATION J. Donat and C. Dryden, Old Dominion University, Norfolk, VA, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3027–3035, & 2001, Elsevier Ltd.

Introduction The transition metals and heavy metals (those with atomic weights greater than 20) enter the ocean via river runoff, wind-blown dust, diffusion from sediments, hydrothermal inputs resulting from reactions of sea water with newly formed ocean crust at midocean seafloor spreading centers, and from anthropogenic activities. Some of these metals (e.g., manganese, iron, cobalt, nickel, copper and zinc) are extremely important micronutrients needed by phytoplankton for various metabolic functions. Several trace metals that are nonconservative with short oceanic residence time (e.g., manganese and aluminum, though the latter is not a heavy metal) are valuable as tracers for circulation and mixing in the ocean. Micronutrient metals, as well as metals like mercury, lead, and silver, which have no biochemical role, can be toxic very low concentrations. Until recently, marine chemists and chemical oceanographers, using sample collection and analytical techniques of the time, could not accurately measure the naturally low concentrations of these metals in unpolluted sea water because of sample contamination problems and lack of instrumental sensitivity. Development of modern techniques for collection, storage, and analysis of uncontaminated samples, plus the development of highly sensitive analytical techniques and instrumentation, have only recently enabled marine trace metal chemists to determine accurate concentrations of these elements in sea water, furthering our understanding of their distributions and chemical behavior in the oceans. These procedural, analytical, and instrumental advancements led to the discoveries that the concentrations of many of these metals were orders of magnitude lower than previously believed, and that the depth distributions (‘vertical profiles’) of transition and heavy metal concentrations result from biological, physical, and geochemical processes in the oceans.

100

We now have a basic understanding of the concentrations and distributions of nearly all the naturally occurring elements in sea water. However, it has become increasingly clear that this information alone is insufficient for providing a complete understanding of the biological and geochemical interactions of these metals in the sea. Metals in sea water can exist in different physical forms (dissolved, colloidal, particulate) and chemical forms (ions, inorganic complexes, organic complexes, organometallic compounds) and in different oxidation states (collectively termed ‘species’) within a given chemical form. Knowing the distribution of a metal’s total concentration among these various forms (‘speciation’) is critically important because the different forms can have very different biological and geochemical behaviors, and thus different fates and transport. Before considering the speciation of the transition and heavy metals, we first present a brief overview of the concentrations and distributions of these elements.

Overview: Transition Metal and Heavy Metal Concentrations and Distributions Concentrations of the transition metals and heavy metals vary both horizontally and vertically through the world’s oceans. Table 1 lists the ranges in the oceanic concentrations of the transition metals and heavy metals. For a representation of the North Pacific depth profiles of the elements in the periodic table, including the transition metals and heavy metals (see Elemental Distribution: Overview). The relative rates of supply and removal of the elements determine their horizontal and vertical distributions. These elements are supplied to the oceans primarily by riverine input, atmospheric precipitation, hydrothermal venting, and anthropogenic activities, and they are removed by adsorption onto sinking particles (‘scavenging’) or by incorporation into sinking biologically produced material by active uptake by phytoplankton. On the basis of their vertical profiles, these elements can be classified into one of the following categories: (1) conservative type, (2) scavenged type, (3) nutrient (recycled) type, and (4) mixed type. Figure 1 shows the shapes of the vertical profiles for the conservative, scavenged, and nutrient (recycled) categories and lists the elements that display them.


TRANSITION METALS AND HEAVY METAL SPECIATION

Table 1 Element

Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge Y Zr Nb Mo Rh Pd Ag Cd In Sn Te La Hf Ta W Re Os Ir Pt Au Hg Tl Pb Bi a

101

Oceanic concentrations of transition metals and heavy metals Concentration unitsa

pmol l1 pmol l1 nmol l1 nmol l1 nmol l1 nmol l1 pmol l1 nmol l1 nmol l1 nmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 nmol l1 fmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 pmol l1 fmol l1 fmol l1 pmol l1 fmol l1 pmol l1 pmol l1 pmol l1 pmol l1

North Pacific

North Atlantic

Surface

Deep

Surface

Deep

8 4–8 32 3 0.5–3 0.02–0.5 4–50 2 0.5–1.3 0.1–0.2 12 5 66–187 12–95 2.8 93 370 0.18 1–5 1–10 0.09–1.8 4 1.2 20 0.2–0.4 0.09 41 28–82

18 200–300 36 5 0.08–0.5 0.5–1 10–20 11–12 4.5 8.2 30 100 306–383 275–325 3.9 105 900 0.66 23 1000 0.07–0.09

14 30–60 23 3.5 1–3 1–3 18–300 2 1.0–1.3 0.1–0.2 25–30 1

20 200

0.5 0.4 50–150 0.5–10 60–80 14–50 0.2

1 50–70 1–2 0.3 51 20 0.8 0.3–1.2 2–10 80 3–6 0.02

4.5 0.25–0.5 0.25–0.5 20–30 6 2 1.6 20

100

0.69–4.6 1–10 2.7 10–20 1–1.5 12–15 0.4

32–43 15 0.2–0.4 50–150 1–7 60–70 100–150 0.25

2.7–6.9 350 0.9 8 0.4–1 80–84

17 0.2–0.4 1 60 20

1 nmol l1 ¼ 109 mol l1; 1 pmol l1 ¼ 1012 mol l1; 1 fmol l1 ¼ 1015 mol l1.

Conservative Type

Owing to their low reactivity, conservative type transition metals and heavy metals (V, Mo, W, Re, and Tl) are present in sea water at relatively high concentrations that are in constant proportion to salinity. Conservative metals have long mean oceanic residence times (44105 y), their distributions are considerably homogeneous throughout the ocean due to the ocean’s 1000-year circulation, and their concentrations are controlled by physical processes (e.g., advection and turbulent mixing). Scavenged Type

Scavenged type transition metals and heavy metals (Mn, Co, Ga, In, Te, Pb, Bi, Ce) typically have strong

interactions with particles, short mean oceanic residence times (102 103 y), and low concentrations. Their removal from sea water is dominated by adsorption onto the surfaces of particles and transport to the sediment via interactions with large, rapidly settling particles. Their depth profiles typically show enrichment in surface waters owing to sources from rivers and atmospheric dust, and rapid depletion to low concentrations at depth. Nutrient (Recycled) Type

Metals having nutrient type distributions (Fe, Ni, Zn, Ge, Se, Y, Ag, Cd, Ba, La) are characterized by surface water depletion and enrichment at depth. Surface depletion is caused by biological uptake, and


102


Type

Profile

Conservative (C)

Increasing depth

Increasing [X]

V, Mo, W, Re, T1

Scavenged (S)

Increasing depth

Increasing [X]

Mn, Co, Ga, In, Te, Hg, Pb, Bi, Ce

Nutrient (or recycled) (R)

Increasing depth

Increasing [X]

Fe, Ni, Zn, Ge, Se, Y, Ag, Cd, Ba, La

enabled oceanographers to make large numbers of measurements of the concentration of a few transition metals across some ocean basins to construct two- and three-dimensional horizontal profiles, instead of just presenting an element’s vertical profile. For example, two-dimensional ocean basin-scale distribution maps have been produced for aluminum and iron. These two-dimensional distribution maps can help identify the input and distribution mechanisms of an element and can be useful as tracers of water mass movements. Although such detailed information has been obtained for a few transition metals and heavy metals, initial measurements of the oceanic concentrations and distributions need to be made for elements such as Ti, Ga, Ru, Pd, Ir, Pt, Au, Re Te, Zr, and Hf in many ocean basins before simple vertical and horizontal profiles can be constructed. Using newly developed analytical techniques, researchers have begun to obtain initial data on these metals. For example, the first concentration data on iridium in sea water (North Pacific) have been reported. Iridium concentrations ranged from 0.5 1015 mol l1 in North Pacific surface waters and increased with depth to a maximum of 0.8 1015 mol l1 near the bottom.

Speciation Introduction

Figure 1 Oceanic profile classifications.

enrichment at depth is due to regeneration of the elements from particles back into solution by bacterial oxidation of the biological particulate matter. Deep waters of the North Pacific and Indian Ocean typically have higher concentrations of these elements than North Atlantic deep waters owing to biogeochemical cycles and ocean circulation. Mixed Type

Some transition metals and heavy metals, such as Cu, Fe, Ga, Zr, Ti, La and other rare earths, have distributions that are influenced by both recycling and scavenging processes. For example, copper displays the characteristic surface depletion and deep-sea enrichment of the recycled element type; however, its concentration increases only gradually (almost linearly) with depth, indicating the effects of scavenging.

Modern Advances Development of new analytical techniques, especially those that can be used at sea aboard ship, have

Knowing the oceanic concentrations and distributions is only part of the picture in understanding the biological and geochemical interactions of transition metals and heavy metals. Dissolved metals can exist in different oxidation states and chemical forms (‘species’). These forms include free solvated ions, organometallic compounds, organic complexes (e.g., metals bound to proteins or humic substances), and inorganic complexes (e.g., metals bound to Cl, 2 OH, CO2 3 , SO4 , etc.). Knowledge of the concentrations of these various species of a transition metal or a heavy metal, in conjunction with its distribution and concentration, is critical to understanding how the various chemical species interact biologically and geochemically. For example, the nutrient availability and toxicity of several transition metals have been found to be proportional to the concentrations of their free metal ions and not their total concentrations. Complexation of a metal by an organic ligand will decrease the concentration of the free ion form of the metal, thereby decreasing its toxicity or bioavailability. Organic complexation may also decrease or increase adsorption of metals onto metal oxide particles. These examples illustrate


103


the importance of speciation information for fully understanding a metal’s oceanic biogeochemical cycle. Inorganic Speciation

Inorganic forms of the transition metals and heavy metals in sea water include hydrated metal ions, complexes with inorganic ligands, and species with different oxidation states. Transition metals and heavy metals with different oxidation states can exist in sea water when the potential required to change valence states falls within the range of the sea water’s oxidizing/reducing potentials. Examples of transition metals and heavy metals having multiple oxidation states in sea water include Fe(II)/Fe(III), Mn(II)/ Mn(IV), Cr(III)/Cr(VI), and Cu(I)/Cu(II). In oxygenated sea water, the thermodynamically stable form is usually the higher of the two oxidation states. However, species whose existence is thermodynamically unfavorable (i.e., usually the lower oxidation states) can be produced biochemically (e.g., by photosynthesis) and/or chemically (e.g., by photochemistry), as a result of the input of solar energy. Calculational estimates of the inorganic speciation of many of the transition metals and heavy metals in sea water have been given in two landmark papers by Turner et al. and Byrne et al. (see Further Reading). The extent to which a metal is complexed by inorganic ligands is expressed by the inorganic sidereaction coefficient, a. This, in turn, is calculated from eqn[1] where b is the overall conditional stability constant for the inorganic complex MXi of the transition or heavy metal M with the inorganic ligand Xi, and X½0i is the concentration of uncomplexed Xi. a¼1þ

X

bMXi X0i

½1

i

The inorganic side-reaction coefficient, a, is also equal to the ratio of the sum of the concentrations of all inorganic species of the metal Mð½M0 Þ to the concentration of its free hydrated cation M ½Mnþ (eqn [2]). a¼

½ M0 Mnþ

½2

For zinc and the first transition series metals manganese, iron, cobalt, and nickel, the free hydrated divalent cation form dominates the dissolved inorganic speciation. The trivalent metal cations Al3þ, Ga3þ, Tl3þ, Fe3þ, and Bi3þ are strongly hydrolyzed (i.e., they form strong complexes with

Table 2

Influence of pH and temperature on the a of Al3þ

pH

Temperature (1C)

a

7.6 7.6 8.2

5 25 5

105.76 107.23 109.39

Source: Byrne et al. (1988).

OH). With respect to complexation by OH, the inorganic side-reaction coefficients of the strongly hydrolyzed metals range from 105.76 for Al3þ to 1020.4 for Tl3þ, and their inorganic speciation is strongly influenced by pH and temperature. For example, at a pH of 7.6, a for Al3þ increases 300-fold as the temperature is increased from 5 to 251C; and at a temperature of 51C, a for Al3þ increases 4000fold as the pH increases from 7.6 to 8.2 (Table 2). Other important inorganic species are the chloride and carbonate complexes. Chloride complexes are important in the inorganic speciation of Agþ, Cd2þ, and Hg2þ. Unlike the strongly hydrolyzed metals, chloride dominated metals are only moderately affected by temperature and pH. Of this group, Hg2þ is complexed by chloride to the greatest extent. The side reaction coefficient of Hg2þ with respect to chloride is 1015.10 at 51C. Carbonate complexes dominate the inorganic speciation of the lanthanides and some actinides (e.g., U(VI) and La(III)). These carbonate complexes are considerably influenced by temperature and pH, although less than the strongly hydrolyzed metal cations. Organic Speciation

Organic forms of the transition metals and heavy metals in sea water include complexes with organic ligands (e.g., metals bound to proteins or humic substances) and organometallic compounds in which the metal is covalently bound to carbon (e.g., methyl forms of As, Ge, Hg, Sb, Se, Sn, and Te; ethyl-Pb forms; butyl-Sn forms). A most interesting discovery is that 90% of the germanium in open-ocean sea water exists in methylated forms so stable to degradation that they have been called the ‘Teflon of the sea.’ Methyl forms of metals are generally highly toxic because these compounds are soluble in cell walls and accumulate in cells. This accumulation is one example of how a nonessential metal can become biologically available. The organically complexed fraction of certain transition metals and heavy metals in sea water has been reliably estimated only relatively recently, and attempts have been made to characterize the nature of these complexes. Early studies of metal


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Table 3

Techniques used to determine the speciation of copper in natural waters

Technique

Limitations/considerations

Referencesa

Fixed-potential amperometry (FPA)

Applicable to high [Cl] solutions only and low organic ligand concentrations (r1000 mol l1) Limited sensitivity and chloride interferences

Waite and Morel (1983); Hering et al. (1987)

Copper ion-selective electrode (ISE) Biological assays Solid-phase extraction (SPE) Competitive equilibration with MnO2 Differential pulse anodic stripping voltammetry (DPASV) Competitive ligand equilibration/ adsorptive cathodic stripping voltammetry (CLE/CSV)

a

Assumes only free metal ion activity causes biological inhibition May underestimate the extent of organically complexed copper in oceanic surface waters Assumes only Cu2þ adsorbs to MnO2 Assumes only inorganic copper is detected and that natural copper complexes dissociate too slowly to be detected Assumes that samples at equilibrium during measurement and that natural copper complexes are not detected (i.e., not electroactive)

Belli and Zirino (1993); Zirino et al., 1998 Sunda and Ferguson (1983); Hering et al. (1987) Mills and Quinn (1981); Hanson and Quinn (1983); Donat et al. (1986) van den Berg (1982) Coale and Bruland (1988); Donat et al. (1994) van den Berg (1985); Donat and Bruland (1990)

See Further Reading list.

complexation showed little agreement between values for ligand concentrations, conditional stability constants, and the extent to which copper was organically complexed, which ranged from 0 to 100%. Organic speciation work on copper, zinc, and iron shows that the organically complexed fraction dominates the dissolved speciation of these metals in oceanic surface waters and is critically important in controlling the free metal ion concentrations of these metals. Although the chemical nature and complete chemical characteristics of the complexing ligands remains unknown, preliminary investigations have shown that the ligands are generally hydrophillic and of low molecular weight. Methods for determining the speciation of transition metals and heavy metals in natural waters include fixed-potential amperometry (FPA), ionselective electrodes (ISE), biological assays, solidphase extraction (SPE), competitive equilibration with MnO2(s), differential pulse anodic stripping voltammetry (DPASV), and competitive ligand equilibration with adsorptive cathodic stripping voltammetric detection (CLE/CSV). Table 3 lists the methods utilized for copper speciation with pertinent limitations and considerations. These techniques involve physical isolation or detection of one of the metal’s species, or of a metal species not originally present in the sample but created for the speciation determination by introduction of a competing ligand. The speciation methods must operate under some general constraints: (1) samples must be at equilibrium, and (2) the technique must detect only the species intended.

Copper The fraction of organically complexed copper in sea water has been determined throughout many of the world’s oceans including the Pacific, Atlantic, and Indian. The percentage of organic copper found in these oceans ranges from 89% to 99.9%. In the surface waters of the North Pacific (i.e., the upper 200 m), more than 99.7% of total dissolved Cu(II) is organically complexed (Figure 2A). The organic complexation is dominated by two coppercomplexing ligands (or classes of ligands), L1 and L2. The stronger L1 ligand class has an average concentration of B1.8 nmol l1 in the upper 100 m and from the surface down to 200 m and its concentration exceeds that of dissolved copper (Figure 2B). The great strength of the L1 class and its excess concentration relative to dissolved copper causes the inorganic copper fraction to account for less than 0.3% of total dissolved copper, and causes the free hydrated Cu2þ to account for only about 0.012% of total dissolved copper. A comparison of Figure 2C with Figure 2B shows that while dissolved copper ranges only from 0.3 to 1.5 nmol l1 (a factor of 5), the Cu2þ concentration ranges from 1013 to 1010 (a thousand-fold)! Measurements made in the Sargasso Sea revealed concentrations of the stronger L1 copper-complexing ligand class to be equal to or less than the dissolved copper concentration, causing the weaker L2 ligand class to dominate organic copper speciation, with a concomitant increase in the inorganic copper fraction and free Cu2þ concentration. Some evidence exists that the ligand concentrations and extent of organic complexation can vary seasonally.


TRANSITION METALS AND HEAVY METAL SPECIATION % Organically complexed copper 0

0

20

40

60

80

100

100

Depth (m)

200 300 400 500 (A)

600 _

0

Copper and L1 (nmol l 1) 1 2

0

3

100

Depth (m)

200 300 400 500

(B)

600

0

_13

log [Cu _12

2+

_

(nmol l 1)] _11

_10

100

Depth (m)

200 300 400 500

(C)

600

Figure 2 North Pacific surface waters dissolved Cu(II) speciation: (A) depth profile of L1, the stronger coppercomplexing organic ligand; (B) dissolved Cu(II) depth profile; (C) depth profile of free Cu2þ ion as logarithmic concentration values.

Zinc The fraction of organically complexed zinc found in North Pacific waters averages 98.7% (Figure 3A). As with copper, organic complexation of zinc is dominated by a relatively zinc-specific organic ligand (or ligand class) in surface waters shallower than 200 m (Figure 3B). In this upper 200 m, the zinc-

105

complexing ligand averages 1.2 nmol l1 and exceeds the concentration of dissolved zinc at depths above 300 m (Figure 3B). The high degree of organic complexation of zinc in the upper 300 m is caused by the excess in ligand relative to that of dissolved zinc and the strength of its zinc complexes. Organic complexation of zinc reduces the concentration of inorganic zinc species to 2 1012 mol l1. Concentrations of free Zn2þ vary with depth from B1011.8 mol l1, at depths less than 200 m, increasing to B108.6 mol l1 at a depth of 600 m. Iron Fe3þ forms complexes with natural organic ligands (like humic substances) that help keep this very insoluble cation in solution at elevated levels in estuarine and coastal waters. In the North Pacific and in the North Sea, researchers have determined that more than 99% of dissolved Fe(II) is bound with an extremely strong ligand class whose concentration ranges from 1 to 5 nmol l1 and is in excess of the ambient dissolved iron concentration. These ligands have conditional stability constants consistent with low molecular weight organic substances called siderophores, which are produced by bacteria to specifically obtain iron. The availability of iron to aquatic primary producers has become the focus of many research projects since experiments have shown that in certain areas of the world’s oceans iron availability is very low and may regulate productivity and perhaps influence atmospheric levels of carbon dioxide. Other metals Organic complexation of other dissolved transition metals and heavy metals (i.e., Cd, Pb, Co, Ni, and Fe) has been investigated only much more recently and the information on these metals is not as defined or as extensive as for copper, iron and zinc. Recent measurements of dissolved cadmium in the North Pacific revealed that 70% was bound by cadmium-specific organic ligands found only at depths less than 175 m. Inorganic cadmium concentrations varied from 0.7 1012 mol l1 in surface waters to 800 1012 mol l1 at 600 m. The free Cd2þ concentration ranged from 20 1015 mol l1 in the surface, where organic complexation dominates the speciation, to 22 1012 mol l1 at 600 m where chloro complexes appear to dominate the inorganic speciation. In the North Pacific, measurements of dissolved lead in the surface waters revealed that 50% was organically complexed by one class of strong organic ligands found to have concentrations between 0.2 and 0.5 nmol l1. The free Pb2þ surface water concentration as a result of inorganic and organic complexation was B0.4 1012 mol l1.


106

TRANSITION METALS AND HEAVY METAL SPECIATION % Organically complexed zinc 0

0

20

40

60

80

100

4

5

100

Depth (m)

200 300 400 500 600 (A) _

Zinc and L (nmol l 1) 0

0

1

2

3

Organic complexation of dissolved cobalt and nickel in the open ocean has not been reported; however, organically complexed cobalt and nickel in estuarine and coastal samples have been found. The fraction of organic complexation is highly variable from estuary to coastal ocean. About 50% of the dissolved cobalt in coastal sea water was found to be organically complexed. In UK coastal waters and south San Francisco Bay, 30–50% of the nickel was bound in extremely strong organic complexes. The information presented in this section demonstrates the importance of organic complexation of several transition metals and heavy metals. These organic ligands exist at low concentrations and form very strong complexes (i.e., they have high conditional stability constants). Although the actual chemical structures of these complexing organic ligands are still unknown, new analytical techniques may soon uncover their structure.

100

How Speciation Relates to Biology

Depth (m)

200 300 400 500 600 (B) _

0

_12

log [Zn2+ (nmol l 1)] _11 _10

_9

100

Depth (m)

200 300 400 500 600 (C) Figure 3 North Pacific zinc speciation: (A) depth profile of zinccomplexing organic ligand presented as percentage of organically complexed zinc; (B) dissovled zinc depth profile; (C) Zn2þ ion depth profile as logarithmic concentration values.

Early researchers suggested that some organic compounds present in sea water in trace quantities may influence the primary production of marine communities by reducing toxic free metal concentrations (especially Cu2þ) to nontoxic levels. Data show that maximum levels of organically complexed copper occur in the surface euphotic zone at depths near the productivity maximum, and decrease dramatically below the vernal mixed layer in the North Pacific. The speciation of dissolved zinc is dominated by organic complexes and it may suggest a biological influence, as discussed for copper. Yet, the reasons for organic zinc speciation are not completely understood and only speculations exist. Laboratory evidence exists for production of a strong copper-binding ligand by four marine phytoplankton (three species of eukaryotes and one prokaryote). The ligand that was produced has identical copper-complexing strength (i.e., similar conditional stability constants) to that of the stronger ligand observed in surface waters of the North Pacific and Sargasso Sea. The production of this L1-like ligand may demonstrate a detoxification mechanism used by phytoplankton to lower the free Cu2þ concentration. Laboratory studies of the sensitivity of phytoplankton to varying Cu2þ concentrations revealed the following trend: cyanobacteria were the most sensitive; diatoms were the least sensitive; and coccolithophores and dinoflagellates showed intermediate sensitivity. Using this laboratory work, researchers are theorizing how cyanobacteria might produce strong L1 ligands to lower the free Cu2þ



concentration in oceanic surface waters to levels at which their growth would not be impacted (o1012 mol l1). During an upwelling event, cyanobacterial production of the L1 ligand might not exceed the newly upwelled Cu2þ, therefore cyanobacteria abundance would decline. Actual field evidence is supporting the speculation that species composition and seasonal species successions of phytoplankton are influenced by Cu2þ concentrations, especially in high-nutrient–low-chlorophyll areas. Growth limitation experiments, like those for copper, have also been performed for iron, zinc, and manganese. These experiments showed that sufficiently low free ion activities of these nutrient metals could result in species shifts in phytoplankton communities. Iron is perhaps the most important nutrient transition metal to phytoplankton and its speciation is extremely complex and is not known with any reliability. Forms of iron that are speculated to have biological importance are organic Fe(III) complexes, Fe(III) oxides, and Fe(III)–siderophore complexes. Unlike Cu2þ which acts as a toxin, increased free Zn2þ concentrations in upwelled water could enhance reproduction of phytoplankton communities. Manganese in sea water, which shows no evidence of any organic complexation, appears to be maintained by photochemical reduction processes and photoinhibition of microbial oxidation of Mn2þ. Low manganese concentrations could potentially limit oceanic productivity if not supplied in sufficient quantities by atmosphere or horizontal mixing. Therefore, the distributions of Zn2þ, Mn2þ, and dissolved iron have important consequences for species composition and species succession of a phytoplankton community. Oceanic concentrations of dissolved cadmium may be outside the range causing cadmium toxicity. However, in estuarine and riverine areas, anthropogenic sources could supply excessive cadmium inputs, leading to cadmium toxicity in aquatic phytoplankton. On the other hand, some researchers have shown that cadmium can promote growth of zinc-limited oceanic phytoplankton by substituting for zinc in certain macromolecules, thereby causing growth at lower than expected free Zn2þ concentrations. It has been speculated that this biochemical substitution of cadmium for zinc by phytoplankton could account for the nutrient-type oceanic distribution of cadmium.

Summary Major advances in procedural, analytical, and instrumental techniques have advanced our knowledge

107

of the concentrations, distributions, and speciation of the transition metals and heavy metals in the oceans, and therefore our understanding of their biogeochemical cycling. For most of the transition metals and heavy metals we have a first-order understanding of their oceanic distributions, and now with more data and better sea-going analytical techniques, basin-wide cross-sections of the distributions of some metals (e.g., aluminum, manganese, and iron) are becoming available. These basin-wide distributions allow more interpretation of sources and fates of these metals. Mediation by light and microorganisms dominates the biogeochemical cycling of certain metals such as copper, iron, and manganese. Organic complexation has come into the forefront of metal speciation research. Not only has the evidence for the existence of organic complexation been overwhelming, but organic ligands dominate the speciation of copper, zinc, and iron in oceanic surface waters. Organic complexation of certain metals in the oceans has important biological implications (i.e., controlling availability of metals as nutrients and toxicants) for phytoplankton.

See also Bacterioplankton. Carbon Cycle. Metal Pollution. Tracers of Ocean Productivity.

Further Reading Belli SL and Zirino A (1993) Behavior and calibration of the copper(II) ion-selective electrode in high chloride media and marine waters. Analytical Chemistry 65: 2583--2589. Brand LE, Sunda WG, and Guillard RRL (1986) Reduction of marine phytoplankton reproduction rates by copper and cadmium. Journal of Experimental Marine Biology and Ecology 96: 225--250. Broecker WS and Peng TH (1982) Tracers in the Sea. New York: Eldigio Press. Bruland KW (1983) Trace elements in sea-water. In: Riley JP and Chester R (eds.) Chemical Oceanography, vol. 8, pp. 157–220. London: Academic Press. Bruland KW, Donat JR, and Hutchings DA (1991) Interactive influences of bioactive trace metals on biological production in oceanic waters. Limnology and Oceanography 36: 1555--1577. Bruno J (1990) The influence of dissolved carbon dioxide on trace metal speciation in seawater. Marine Chemistry 30: 231--240. Burton JD and Statham PJ (1988) Trace metals as tracers in the ocean. Philosophical Transactions of the Royal Society of London Series A 325: 127--145.


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Byrne RH, Kump LR, and Cantrell KJ (1988) The influence of temperature and pH on trace metal speciation in seawater. Marine Chemistry 25: 163--181. Coale KH and Bruland KW (1990) Spatial and temporal variability in copper complexation in the North Pacific. Deep-Sea Research 37: 317--336. Donat JR and Bruland KW (1990) A comparison of two voltammetric techniques for determining zinc speciation in Northeast Pacific Ocean waters. Marine Chemistry 28: 301--323. Donat JR and Bruland KW (1995) Trace elements in the oceans. In: Steinnes E and Salbu B (eds.) Trace Elements in Natural Waters, pp. 247--281. Boca Raton, FL: CRC Press. Donat JR, Lao KA, and Bruland KW (1994) Speciation of dissolved copper and nickel in South San Francisco Bay: a multi-method approach. Analytica Chimica Acta 284: 547--571. Donat JR, Statham PJ, and Bruland KW (1986) An evaluation of a C-18 solid phase extraction technique for isolating metal–organic complexes from central North Pacific Ocean waters. Marine Chemistry 18: 85--99. Hanson AKJ and Quinn JG (1983) The distribution of organically complexed copper and nickel in the midAtlantic Bight. Canadian Journal of Fisheries and Aquatic Sciences 20: 151--161. Hering JG, Sunda WG, Ferguson RL, and Morel FMM (1987) A field comparison of two methods for the determination of copper complexation: bacterial bioassay and fixed-potential amperometry. Marine Chemistry 20: 299--312. Li YH (1991) Distribution patterns of the elements in the ocean. Geochimica et Cosmochimica Acta 55: 3223--3240. Millero FJ (1992) Stability constants for the formation of rare earth inorganic complexes as a function of ionic strength. Geochimica et Cosmochimica Acta 56: 3123--3132. Mills GL and Quinn JG (1981) Isolation of dissolved organic matter and copper–organic complexes from estuarine waters using reverse-phase liquid chromatography. Marine Chemistry 10: 93--102.

Nozaki Y (1997) A fresh look at element distribution in the North Pacific. Eos, Transactions of the AGU 78: 221. Quinby-Hunt MS and Turekian KK (1983) Distribution of elements in sea water. Eos, Transactions of the AGU 64: 130--131. Rainbow PS and Furness RW (eds.) (1990) Heavy Metals in the Marine Environment. Boca Raton, FL: CRC Press. Sunda WG and Ferguson RL (1983) Sensitivity of natural bacterial communities to additions of copper and to cupric ion activity: a bioassay of copper complexation in seawater. In: Trace Metals in Sea Water, NATO Conference Series 4, Marine Science, Vol. 9, pp. 871– 890. New York: Plenum Press. Turner DR, Whitfield M, and Dickson AG (1981) The equilibrium speciation of dissolved components in freshwater and seawater at 251C and 1 atm pressure. Geochimica et Cosmochimica Acta 45: 855--881. van den Berg CMG (1982) Determination of copper complexation with natural organic ligands in seawater by equilibration with MnO2. II. Experimental procedures and application to surface seawater. Marine Chemistry 11: 323--342. van den Berg CMG (1985) Determination of the zinc complexing capacity in seawater by cathodic stripping voltammetry of zinc–APDC complex ions. Marine Chemistry 16: 121--130. Waite TD and Morel FMM (1983) Characterization of complexing agents in natural waters by copper(II)/ copper(I) amperometry. Analytical Chemistry 55: 1268--1274. Wong CS, Boyle E, Bruland KW, Burton JD, and Goldberg ED (eds.) (1983) Trace Metals in Seawater. New York: Plenum Press. Whitfield M and Turner DR (1987) The role of particles in regulating the composition of seawater. In: Stumm W (ed.) Aquatic Surface Chemistry, pp. 457--493. New York: Wiley. Zirino A, DeMarco DJ, VanderWeele DA, and Belli SL (1998) Direct measurement of copper(II) (aq) in seawater at pH 8 with the jalpaite ion-selective electrode. Marine Chemistry 61: 173--184.


TRANSMISSOMETRY AND NEPHELOMETRY C. Moore, WET Labs Inc., Philomath, OR, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3035 –3045, & 2001, Elsevier Ltd.

Introduction Transmissometry and nephelometry are two of the most common optical metrics used in research and monitoring of the Earth’s oceans, lakes, and streams. Both of these measurements relate to what we perceive as the clarity of the water, and both provide vital information in numerous studies of natural processes and human activities’ impact upon water bodies. Applications involving these measurements range from monitoring drinking water suitability to understanding how carbon is transferred into and transported within ocean waters. Transmissometry refers to measurements made by transmissometers or beam attenuation meters. These sensors infer the total light lost from a beam of light passing through the water. These losses are caused by two primary mechanisms. Suspended particles and the molecules of the water itself scatter the light away from its original path; the water, and dissolved and particulate matter contained within, absorb the light and convert it into heat, photosynthetic activity, fluorescence, and other forms of energy. Larger concentrations of scattering and absorbing substances therefore result in greater losses in signal. Nephelometry refers to measurements made by optical scattering sensors, often referred to as turbidity sensors or nephelometers. These sensors project a beam of light into the water and measure the radiant flux of light scattered into the direction of a receiver. Since the receiver signal increases with greater numbers of particles, the device infers the concentration of suspended particles in the water. Scattering sensors are used more commonly in environmental monitoring applications, especially in highly turbid waters with large concentrations of particles; transmissometers see more use in general scientific studies. However, the uses for which they are employed broadly overlap. Nevertheless, transmissometers perform quite different measurements from those of scattering sensors and the quantities they measure are independent of one another and typically offer no direct comparison. In fact, while the data products they provide may covary, the relationship between the values most certainly will

differ depending upon the composition of the materials in the water. Using a transmissometer one can derive an attenuation coefficient that mathematically describes the ability of the water to transmit light. This coefficient is a fundamental optical characteristic and an absolute quantity for a given medium. The scattering sensor, on the other hand, collects a very small portion of the scattered light and is usually calibrated to some secondary standard. The units of measurement are themselves relative to that standard. Other differences also prove crucial in defining these measurements. Limitations imposed by the instruments themselves, application-specific requirements, sensor sizes, and cost all play roles in determining the possible suitability of one measurement versus another. Thus, in order to best fit these two methods to potential applications, it is necessary to understand the measurements, the design of the sensors performing them, and the products that the sensors provide.

Measurements and Fundamental Values In the realm of water sciences, transparency and turbidity are two of the most commonly used terms in describing optical clarity. These are general terms and typically not tied to absolute physical quantities other than through the use of secondary standards. However, the set of underlying optical processes that describe the impact of water-based media upon light propagating through them are well defined, if not completely understood. In the study of the transmission of light energy through water, the inherent optical properties (IOPs) refer to the set of intrinsic optical characteristics of the water and components contained therein. The IOPs define how light propagates through the water. In comparison to apparent optical properties (AOPs), the other general class of in-water optical measurements, the IOPs are not affected by changes in the radiance distribution from sunlight or other sources. The IOPs include coefficients for the attenuation, absorption, and scattering of light as well as the volume scattering function. The coefficients of attenuation (c), absorption (a), and scattering (b) determine radiance losses of a ray of light propagating through the water. Light is either lost to absorption by the water and material


109

110

TRANSMISSOMETRY AND NEPHELOMETRY 3

contained within or it is scattered by the same. The attenuation coefficient accounts for losses attributed to both the absorption and the scattering and is equal to the sum of these coefficients eqn [1].

Dissolved organic matter Particulate absorption Particulate scattering

½1

Total attenuation

_

One determines the beam attenuation coefficient by comparing the radiant flux of a collimated beam of light at source (Fs) with the radiant flux of the beam at a receiver detector (Fd), a finite distance (r) away. This ratio is known as the beam transmittance (T), given by eqn[2] or equivalently by eqn[3].

Total non-water attenuation

2

Attenuation (m 1 )

c¼aþb

Water

Wavelength of most common transmissometers

1

Fd =Fs ¼ T ¼ ecr

½2

c ¼ lnðT Þ=r

½3

Here r is the path length between the source and the receiver. This coefficient is the value ultimately determined by a transmissometer. The attenuation coefficient is expressed in units of inverse meters (m1). Thus, when one refers to water with an attenuation coefficient of 1 m1, the implication is that within a 1 m path the available light within a collimated beam is reduced to 1/e or approximately 37% of its original energy. Within the visible light spectrum the scattering and absorption losses from the water itself remain effectively constant, and thus variability found in field measurements results from non-water particulate and dissolved matter. The extent of absorption-based losses compared to scattering-based losses depend both on the materials being measured and on the spectral configuration of the meters. Both the scattering and absorbing properties of water-based components are prone to variation with the wavelength of light at which measurements are conducted. Variations in the absorption depend heavily upon the amount of colored dissolved organic matter (CDOM) and chlorophyll content. CDOM absorbs very strongly in the blue wavelengths; chlorophyll absorbs heavily in the blue and in addition has a pronounced absorption peak in the deep red portion of the spectrum (676 nm). Absorption by these materials provides the appearance of color to the water. Visually, CDOM laden waters tend to appear brown, and chlorophyll-rich waters appear green. A deep blue cast to the water indicates very low levels of both of these substances. The spectral dependency of the scattering signals is largely due to the size of the particles from which the light is scattered (Figure 1). In addition to the optical loss coefficients, the volume scattering function (VSF) forms another important component of the IOPs in describing the fate

0 400

450

500

550 600 Wavelength (nm)

650

700

Figure 1 Relative contributions of water and non-water scattering and absorbing components are seen in formulation of the attenuation coefficient within ‘typical’ waters.

of light in water. The VSF describes optical scattering as a function of the angle, y, away from the direction of propagation of the incident beam of light. The VSF coefficient, bðyÞ, defines the radiant energy lost into a given angular region of the light scattering and is expressed in terms of inverse meters per steradian. The VSF integrated over the entire spherical volume into which light is scattered provides b, the total scattering coefficient (eqn [4]). ðp

b ¼ 1p bðyÞsinðyÞdy

½4

0

The actual shape of the VSF depends upon the particle field being measured. Specific properties that define this shape include the particle size and shape and the index of refraction. Particle size is probably the single most pronounced factor in defining the VSF in that it dictates the regime of light interaction with the particles themselves. Very small particles that fall within the wavelength of the light impinging upon the particles are subject to molecular or Rayleigh scattering. This interaction is relatively weak, and creates a VSF that is relatively constant with angle. While Rayleigh scatterers are by far the most prevalent in most waters, most of the scattering signal seen by sensors is attributed to particles ranging


TRANSMISSOMETRY AND NEPHELOMETRY

from 1 mm to >50 mm. The scattering behavior of these particles is typically modeled using Mie theory. Mie theory uses Maxwell’s equations to predict perturbations of an incident planar wave by spherical particles in its path. In general, larger particles will create a greater degree of near-forward scattering. Most scattering sensors are not considered tools for determination of in-water optical properties, but all scattering sensors including turbidity sensors measure the VSF within a given angular region, typically somewhere in the region of 90–1601 with respect to the incident direction of the light. It is perhaps ironic that while these sensors are among the most ubiquitous of in-water optical tools, the VSF is one of the least-characterized of all the IOPs. This is because no single angle measurement can account for the shape of the entire function. This in turn points to a major source of error in all turbidity-based measurements. Different materials dictate different VSFs and a single angle measurement will vary with concentration from one type of material to the next. In actual fact a diverse amalgam of organic and inorganic particulates reside within most waters. This ultimately tends to homogenize the VSFs such that the variability in the VSF of the composite is less than the variability of individual components (Figure 2). Most scattering measurements are based upon some standard such as formazin, diatomaceous earth, or more recently spherical styrene bead suspensions. These standards are used because they tend to be reproducible and easy to mix into various concentrations for calibrations. Units of quantity are expressed in form of turbidity units such as NTU (nephelometric turbidity units). Because of the _3

10

Normalized VSF

disparate VSFs of these standards and natural waters, total attenuation (or particle concentration) cannot be obtained from turbidity measurements without intercalibrating with transmissometers (or by filtering and weighing) in natural waters.

Sensors Transmissometers

A basic transmissometer consists of a collimated light source projected through an in-water beam path and then refocused upon a receiver detector. Typically single-wavelength transmissometers employ a light-emitting diode coupled with an optical bandpass filter as the source. Source light is often split so that a portion of the beam impinges upon a reference or compensation detector that is either used in numerical processing of the data or integrated into a source stabilization feedback circuit. The source output is often modulated and the lamp and receiver detector samples are in phase with the source modulation. This greatly reduces ambient light detection by the receiver from the sun or other unwanted sources. Path lengths are fixed with distances typically ranging from 5 cm to 25 cm depending upon the waters in which the sensors are used (Figure 3). The receiver detector converts radiant flux into current and its output is thus proportional to the radiant energy passed through the water. Electronics subsequent to the detector amplify and rectify the signal for digitization or direct output as a DC voltage level. This signal is known as the instrument transmittance (Ti) (eqn [5]).

Open Ocean

1

San Diego Harbor

10

111

California Coast

_4

23

4 5 6

8

11 10

9

12 13

14

This is a common scattering angle for many nephelometers

10

10

_5

7 _6

10

_7

0

50

100 Angle (deg)

150

200

Figure 2 Normalized VSF data for three representative ocean water types. Note that at 901, the most common nephelometer scattering angle, significant differences exist for the respective coefficients. Data collected by Theodore Petzold and Seibert Duntley of Scripps Institute of Oceanography.

15

Figure 3 Cutaway view showing the primary optical components found in a modern transmissometer. A transmitter assembly and receiver assembly are mounted and aligned within a rigid frame. The transmitter assembly consists of (1) a source lamp; (2) a pinhole aperture; (3) a collimating lens; (4) field aperture; (5) an interference filter; (6) a beam splitter; (7) a reference detector; and (8) a pressure window. The beam (9) then passes through a fixed-path volume of water and enters the receiver assembly. The receiver consists of (10) a pressure window, (11) field aperture, (12) a refocus lens, (13) a pinhole aperture, and (14) the receiver detector. Signals from the detector are then fed to the electronics for processing and output (15).


112


Ti ¼ S T

½5

S represents the instrument transmittance scaling constant. This constant is a combined term that includes signal amplification, losses through windows and lenses, and other sensor gain factors. From eqn[5] and assuming a 25 cm pathlength, we obtain eqn[6] or equivalently eqn[7]. Ti =S ¼ ecð0:25Þ

½6

c ¼ 4ln Ti Q

½7

The constant Q ¼ 4 ln S is a general scaling term that is removed, or compensated for, during the calibration process. An ideal transmissometer would reject all but the parallel incident light into its receiver. This implies that there is no error associated with near-forward scattered light getting into the receiver. However, limitations in real-world optics make this a near impossibility. Transmissometers thus provide a value for a system attenuation coefficient that has a finite scattering error and is defined primarily by the acceptance angle of the receiver optics. These values range from around 0.51 to 11 in water for most commercial instruments. Because that VSF for inwater particles is highly peaked at these angles, this can result in underestimation of the attenuation coefficient and can also lead to sensor-to-sensor discrepancies in measurement. It thus becomes important to know this angle in treating data carefully. While it is possible to build sensors with narrower acceptance angles than 0.51, scattering in the very near-forward direction becomes dominated by turbulent fluctuations in the density of the water itself. This turbulence-induced scattering is irrelevant to particulate studies and, depending upon the distances and receiver sizes involved, to most signal transmission applications. The conceptual framework for the transmissometer measurement involves starting with a full signal and monitoring small negative deviations from it. The sensitivity of the instrument thus depends upon its ability to resolve these changes. In many oceanic and other clear water investigations, signal changes as small as 0.001 m1 become significant. In a 25 cm instrument this implies a requirement for transmittance resolution on the order of 0.025%. At the other end of the environmental spectrum, many inland waterways and some harbor areas would render a 25 cm path instrument ineffective due to loss of all signal. Therefore, range and resolution become the two critical factors in determining a

transmissometer’s effectiveness in a given application. While it is easy to imagine using arbitrarily long path lengths to obtain increased sensitivity, the instrument path begins to impose other limitations upon its utility. Size and mechanical stability both reduce utility of the longer path instruments. On the other hand, shorter paths impose more demands than just high levels of precision in measurement. Cleaning of optical surfaces also becomes a major issue in maintaining sensor reproducibility and accuracy. Again using the 25 cm path length instrument as an example, maintaining signal reproducibility of 0.01 m1 over time requires a cleaning technique that gives results that repeat within 0.25% transmittance. For a 10 cm path length instrument, repeatability would need to be within 0.10% transmittance. Likewise, internal correction mechanisms such as compensation of temperature-related drift impose stringent requirements upon the sensor’s electronics as well as the subsequent characterization process. Long-term drift and general mechanical stability also must be tightly constrained for the instrument to provide accurate results over time. The requirements prove challenging in light of the forty degree (centigrade) temperature swings and the 6000 meter depth excursions to which the instruments potentially get exposed. While the calculation of the attenuation coefficient from raw transmittance is independent of the crosssectional area of the beam, the beam size does play an important role in the transmissometer’s ability to measure. Accurate transmittance measurements rely upon the water and the materials it contains acting as a homogenous medium. This model starts to break down in two important cases: when the number concentration of particulates becomes significantly low compared to the total volume of the illuminated sample area; and when the particle sizes become significantly large in comparison to the cross-sectional area of the beam. Taken in the extreme, one can easily imagine a very narrow beam providing a binary response at the receiver depending upon whether a particle occludes its path. Practically speaking, most transmissometers need to show minimal spiking for particle sizes up to 100 mm diameter. Particles more than a few micrometers in diameter are ‘seen’ by the receiver at about two times their actual size as a result of diffraction. This means for a beam of 5 mm nominal width that a single 100 mm particle could reduce signal at the receiver by approximately 0.08% or on the order of 0.0032 m1 in a 25 cm path (or 0.008 m1 in 10 cm path). This proves acceptable for most operational conditions. On the other hand, a 1 mm particle could create an 8% deviation in sensor output, creating a noticeable



spike. Fortunately, 1 mm particles are extremely rare except in active erosion zones. There are presently two primary methods used in calibrating transmissometers. The first uses fundamental principles of beam optics and knowledge of the index of refraction difference between air and water to directly estimate the sensor output. Electrooptical linearity in response to signal changes is assumed or verified. The sensor’s gain level is set near full scale for transmission in air and the sensor is checked to ensure that if the source output is completely blocked it provides a real zero output. Accounting for the differences in reflection and transmission of the air–glass interfaces compared to the water–glass interfaces, one can then assume that, upon immersion, any further deviations in signal are due to the attenuation of the water and materials contained therein. This measurement is then verified by immersion in clean water and subsequent comparison to clean water values. Error terms in this method usually include deviations of the modeled optics from the real world. These errors include lensinduced focusing aberrations, alignment issues, spectral content of the source, and any dust or film on any of the optical components. The primary advantages of this method are that the calibration process relies only upon the air value measured by the meter, and that the attenuation due to the water is included in the water-based measurements. The second method involves blanking the meter directly with clean water. More akin to calibration approaches used in spectrophotometry, this method involves immersion of the instrument into optically clean water, measuring the value, and setting that value as full-scale transmittance or, conversely, 0.000 m1 attenuation (clean water values for the attenuation can then be added back in accordance with published values). The chief disadvantage of this method lies in the difficulty of creating and verifying optically clean water. While various levels of filtering can remove most of the particulates from the water, filters can also introduce bubbles. These bubbles are seen as particles by the sensor. Assuming that one achieves filtration without introducing any bubbles, bubble creation is still a concern in that any partial pressure imbalances between the gases contained within the water and the surrounding environment will result in subsequent bubble formation. Added to that is the possibility that the containers and the sensors themselves may also act as sources of particulate contamination. The chief advantage of this method is that it accommodates for small deviations in the real instrument with respect to the ideal. The overriding issue with calibration of transmissometers is the same as in the discussion of the

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need for and difficulty of proper cleaning. In order to calibrate an instrument to operate accurately in cleaner waters, the calibrations must achieve accuracy to within 0.25% of full-scale measurement. Ultimately, reproducibility of results becomes the best check for calibration. That said, this level of accuracy is really only required in conditions where particle concentrations are approaching minimal levels. Relative changes of transmittance will still be precisely reflected in the instrument’s measurements.

Scattering Sensors A simple scattering sensor consists of a source element projecting a beam of light in the water and a receiver detector positioned at a fixed angle with respect to the source. The source beam is sometimes stabilized by inclusion of a second receiver that receives a portion of the light coming directly out of the lamp. This signal is then fed back into the lamp driver circuitry to compensate for fluctuations in the source with time and temperature. The source beam has a defined primary projection angle and a distribution of light about that angle. Conversely, the receiver is placed at a specific angle and maintains a defined field of view about that angle. These factors combine to form the distribution of angular response for the scattered light (Figure 4). As with transmissometers, it is necessary to reject ambient light from the sun and other non-sensor sources during measurement. With scattering sensors this is achieved both through the use of synchronously modulated light and detector amplification and also through the use of direct optical rejection. Direct optical rejection is employed at the source through the use of relatively narrow spectral band sources that emit light in the infrared away from the waterpenetrating wavelengths of sunlight. Accordingly the receiver incorporates narrowband optical filters that

Figure 4 Typical scatter sensors and transmissometers.


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reject wavelengths away from the primary emission bands of the source. Specific angular configurations used in modern scattering sensors vary widely. Some sensors are designed to operate within a highly constrained, narrowband, angular relationship, and some are designed to collect as much scattered light as possible and thus encompass a very wide angular range. In general two truths hold for all the designs: they will all provide a roughly linear response that is proportional to the particle concentration (at least in low to moderate concentrations); and different optical configurations will demonstrate different absolute response curves with respect to each other even when calibrated with the same standard (Figure 5). A scattering sensor works by the simple principle that when particles are present they will scatter light and the receiver will collect some of that light. Using Beer’s law, which states that increasing concentrations will result in a linear increase in output signal, the sensor’s output varies from a zero value in clean water to a full-scale value at the upper end of its range. While it is convenient to assume a linear response with concentration, this is not strictly true. Light reaching the volume of interaction and the light scattered back into the detector is subject to secondary losses due to attenuation. As the concentration of scattering components in the water increases, so does the attenuation. This produces a nonlinearity in the output signal. In sensors with large interaction volumes and a wide angular response, this becomes a particularly messy analytical problem in that the light is subject to a large range of effective path lengths in propagation from the source and back to the receiver. In the extreme case, sensors exist that position a near-isotropic source next to a

Figure 5 One of many possible optical configurations for a scattering sensor. A source assembly consisting of a LED lamp, reference detector, lens, and right angle prism projects light into the water. The receiver is placed to receive light at 901 with respect to projected source beam.

wide-angle detector such that they both project out, perpendicular to the same plane. In these sensors the effective volume of interaction is strictly a function the attenuation coefficient in that it is infinite other than for induced losses of light. As with transmissometers, the volume of interaction also affects a scattering sensor’s sensitivity and the effect of larger particles upon the signal. Small volumes show less sensitivity and measure larger particles as signal spikes. The combined issues of long-path attenuation coupling and volumetric sensitivity point to the preference of designs incorporating larger beams with greater interaction volumes for measurements of cleaner waters and narrower beams with interaction volumes close to the sensor surface for use in highly turbid waters. The response of a given scattering sensor is very highly dependent upon its specific optical configuration. Angle of interaction, angular distribution, wavelength at which the source emits, and the relative path distance from the source and back to the receiver are all factors in how a sensor will behave. As mentioned earlier, it should be expected that two different designs will provide two different responses. In studies in which researchers require only relative responses with space or time, this is not a major issue. A twofold change in a given concentration of particles will generate an associated response in the instrument output. However, many studies require some form of reproducible results. It is not enough that two sensors are calibrated to the same medium. They must also respond in the same way to any other medium that they might mutually measure. Standards such as ISO 7027 have been published. These standards impose constraints on the angle of interaction between the source and the receiver (901), the angular distribution of the source, and its wavelength of operation, as well as other design parameters. The goal is to ensure that all sensors built within the constraints imposed by the standard will provide similar results in similar waters. This is a very important step toward achieving consistent results amenable to intercomparison. Straightforward in concept, sensor calibration employing a standard suspension, provides several pitfalls in practice. First and foremost, no calibration can be achieved to better accuracy than the standard solutions themselves. Secondly, it is critical to ensure that the container in which the calibration takes place is not a cause of secondary reflections of light that can get back to the receiver. Care must also be taken to ensure that the suspension is not settling or flocculating during the measurement. Finally, one variation of this technique is to use arbitrary



concentration of the calibration media and calibrate against another ‘standard’ precalibrated sensor. Great care should be applied when using this method. Standard sensors often already incorporate compensation schemes for linearizing the data. These schemes in turn are developed for use with a specific type of suspension. This can create dramatic and surprising results when using another suspension. While scattering sensors are predominantly used to determine relative concentrations of particulates, another very important set of applications involve characterization of the volume scattering function itself. One of the important goals in observational oceanography involves the use of remotely sensed data from satellites and other airborne platforms to rapidly characterize large areas of surface and near surface waters. Of particular interest are the emerging methodologies associated with using ocean color data captured from airborne and space-borne platforms to provide information about the biology and chemistry of waters. In the United States, NASA projects such as the Coastal Zone Color Scanner and the more recent SeaWiFs satellite program stimulated this interest, and in the case of SeaWiFs continue to contribute a growing body of information. The light that these platforms receive is a function of the sea surface state and the resultant reflections and the water-leaving radiance. This radiance in turn is defined by the absorption and scattering characteristics of the water. Scattering in the region of 90– 1801 is specifically important because it represents incoming light from the sun that is scattered back into the atmosphere. To quantify this, a class of sensors called optical backscattering sensors have been developed and calibrated specifically for this purpose. In many respects these sensors are very similar to other scattering sensors in that they use the same basic optical configurations and respond similarly to variations in the particle field. The major differences involve design constraints upon the wavelengths of the source emitters and the angles of interaction. Equally importantly, the calibration of these sensors involves tying the sensor response directly to the volume scattering function. Calibration of scattering sensors for radiometric measurements involves detailed knowledge of the sensor optics geometry and some known scattering agent. The prevalent method for single-angle measurements incorporates a sheet of highly reflective diffuse material and maps the sensor response as a function of the distance between the target and the sensor. This information is then applied to derive the angular weighting function of the interaction volume. Finally, this weighting function is applied to a typical ocean water VSF. More recently, researchers

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have begun to apply a calibration technique that incorporates known concentrations of scattering agents with well-defined VSFs. These two techniques address different elements of a sensor calibration and may well find optimum effectiveness when used in conjunction with one another (Figure 5).

Applications Domains of Use

The use of transmissometers and nephelometers falls broadly into two categories. We want to study the water’s optical properties and how they might relate to ongoing processes occurring in the water, and we want to determine how much foreign matter is in it. While, ultimately, both thrusts of study lead to measurement of the same media within a given body of water, the products that the instruments provide differ, and the requirements surrounding the given areas of study tend to drive the development of the different technologies. The factors ultimately determining the appropriateness of one sensor versus another do not always pertain to the data products provided. Size, cost, ease of deployment, ease of maintenance, and researcher’s experiences all contribute to decisions on which type of sensor is the best to use. Optical oceanographic research motivated much of the development of modern transmissometers. This arena also stimulated development of scattering sensors that are specifically designed and calibrated for providing coefficients related to the VSF. Much of this work in the United States revolves around Naval research needs, and primary development of sensors now available commercially was in large part funded through Naval research dollars. Naval applications include mine hunting, underwater tactical assessment for diving operations, and sea truthing for laser communications and imaging research. The US National Aeronautics and Space Administration (NASA) has also played a major role in developing underwater tools for optical characterization. These tools help calibrate the airborne sensors. Similarly, numerous other governments foster the development and use of these tools through their respective Naval, space and other scientific agencies. While not engaged in the study of ocean optical properties per se, many other ocean scientists working under aegis of funds supplied by these agencies use transmissometers and optical backscattering sensors in ongoing efforts to understand physical, biological, and chemical distributions and processes in the water. Scattering sensors remain the dominant optical tools used by environmental researchers. These


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sensors’ size and cost make them widely affordable and easily used, and the newer sensors incorporate fouling-retardant features such as shutters and biocidal exposed surfaces. As such they are becoming increasingly subscribed to as the sensor of choice in compliance-driven monitoring applications developed by various governmental agencies throughout the world. Naturally, the more attractive size and costs of scattering sensors also make them favorable choices in many larger-scale applications. It is likely that remote sensing will to some degree change preferences for sensors among fresh water researchers over the next ten years. Presently there is relatively little airborne color data available for fresh water bodies, and thus many limnology researchers have not yet been compelled to measure optical properties of lakes directly. With the next generation color airborne sensors and new governmental mandates driving more effective broader-scale sampling strategies, the need and desire for transmissometer measurements and scattering measurements for VSF determination will undoubtedly grow. How Sensors are Deployed

One major constraint in an underwater sampling is how to use the instrumentation effectively in the environment for which it is intended. Researchers often want to measure the water in places they cannot easily get to, or over timescales that make personal attendance of equipment an unappealing proposition. To these considerations must be added the requirement that the data gathered must truly reflect changes at the time and space scales of the governing processes within the water column, and the constraint imposed by doing this sampling at a reasonable cost. The sampling challenge becomes formidable. As a result, the development of effective sampling platforms has become as challenging and competitive a discipline of research as instrumentation design itself. Transmissometers and scattering sensors are typically integrated into multiparameter sampling packages for acquiring and storing data (CTDs, data sondes, loggers). The packages are then deployed from boats or other platforms and lowered through the water column, travel on or are towed by a vessel, or are placed on buoys or mooring lines in order to log measurements over an extended period. Many variations of these basic methods exist but virtually all entail these basic concepts. A new class of autonomous deployment platforms will serve to revolutionize underwater sampling. These range from miniature programmable underwater vehicles, to freely drifting ocean profilers that

can continuously move through the water column, and to rapidly deployable profiling moorings. Many flavors of these various platforms are now emerging. Some will find important niches for acquisition of data over space and time. Some Current Applications

There are many different applications engaging the use of transmissometers and scattering sensors. Table 1 represents only a sampling across numerous disciplines. Extending Capabilities

As mankind’s need to understand and monitor the Earth’s waters has increased, they have driven the development of more rugged, more reliable, smaller, and cost-effective technologies for transmissometry and nephelometry as measurement techniques. These resultant technologies have not only carved greater roles for optical measurement methods but have also proved seminal in the development of entirely new sensors. Recently, a new generation of IOP tools has been made available to the oceanographic community. They include sensors for the determination of the in-water absorption coefficient, multiangle scattering sensors, and a set of IOP tools with spectral capabilities. Transmissometers and simple scattering sensors have laid the foundation for the optical techniques and data methods of these new devices. In turn, these new sensors promise to significantly enhance the role of IOP measurements in modern observing platforms. One of the more significant recent breakthroughs in optical measurement techniques lies in the development of the absorption meter. This sensor uses a measurement method and optical geometry similar to a transmissometer except that it encompasses the sensor’s beam path with a reflective tube and incorporates a large-area detector at the receiver end of the path. The reflective tube and large-area detector combine to collect the bulk of the light scattered from the source beam. Thus the light not detected is primarily due to absorption by the water and its constituents. The wide-band spectral nature of sunlight coupled with the selective filtering capabilities of water and the absorption characteristics of phytoplankton and dissolved organic material make spectral optical characterization of the water highly desirable. Likewise, the spectral information from the scattering of particles provides more direct correlation with remote color data as well as a more complete description of the type of particles scattering. New tools encompassing spectral attenuation, absorption,



Table 1

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Applications of transmissometry and nephelometry

Application

Description

Monitoring terrestrial runoff and impact of industrial inflows on water quality

Scattering sensors stationed in rivers and streams allow researchers to determine impacts of inflows upon water quality. Inflows might be created by logging, agriculture, mining, land development, controlled and uncontrolled outflows from water treatment plants, natural runoff and other events that introduce new matter into the monitored bodies. Compliance monitoring United States’ compliance monitoring of fresh water bodies is soon likely to include turbidity as a required parameter for ongoing measurement. Determining biological distribution in the water Both transmissometers and scattering sensors are deployed in viewing the biological variability in space and time through the water column. Radiative transfer studies – optical closure In verifying the optical relationships between the inherent and apparent optical properties, researchers seek to test the relationships through direct measure and comparison of values from the disparate instrument types. Scientists also seek to reconcile measurements of the inherent properties among themselves in validation of IOP theory. Remote sensing validation Satellite and other airborne remote imaging systems require in-water transmissometry, scattering, and absorption measurements to calibrate these sensors to water-borne optical properties. Studying the benthic layer processes In understanding the processes effecting the settling and re-suspension of particles near the bottom of the water column both scattering sensors and transmissometers can provide relative indications of particle flux. Frazil ice formation Transmissometers have been shown to ‘see’ signal fluctuations associated with the formation of frazil or supercooled ice. These studies are imperative in understanding how polar ice sheets are formed. Diver visibility Navies require better tactical assessment of waters for determining operational risk for divers and other visibility-related operations. Small-scale structure in the water column In coastal regimes many physical and ecological processes take place on smaller time and space scales than previously thought. The speed of acquisition and sensitivity of modern scattering sensors and transmissometers allow accurate particulate mapping within the water column, which in turn serves as a tracer for these processes. Tracking particulate organic carbon Data from transmissometers has been shown to accurately reflect total particulate organic carbon within the water column. Understanding in-water carbon transport processes is, in turn, vital to understanding carbon flux between the water and atmosphere through the uptake and output of CO2. Tracking bloom cycles Transmissometers on moorings located both in open ocean and in coastal areas track seasonal bloom cycles as well as event-driven changes from major storms or other potential system disturbances. Monitoring activity around thermal vents and Scattering sensors on moorings and underwater vehicles track plumes from underwater volcanoes underwater vents and eruptions.

and scattering are now commercially available. These tools are playing increasingly important roles in various applications. Despite the plethora of scattering sensor data available, very little information exists concerning the range of variability of the VSF, and how it relates to different water masses and the processes within them. One of the chief constraints in fully characterizing the VSF is that it requires a multiangle scattering measurement encompassing in excess of 4 orders of magnitude of scattered light intensity. After some seminal work performed by researchers at the Scripps Institute of Oceanography during the late 1960s and early 1970s, very little has since been done to add to this body of data. In fact, VSF functions measured then remain de facto calibration

standards for instruments being built today. In recent years researchers in Europe and the United States have refocused attention upon this issue. As a result, a new set of multiangle scattering sensors is now coming into commercial availability. Other development efforts and new instrumentation incorporate scattering and transmittance measurements in unique ways to obtain specific underwater chemical and biological components. One example of these includes an underwater transmissometer that uses polarized light to determine concentrations of particulate inorganic carbon. These instruments promise to fill a vital niche in understanding the fate of carbon in the seas. Another example in development are underwater flow cytometers. While the prevalence of IOP measurements


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look at bulk phase phenomena, new instruments are now available as ship-board and dock mounted units that couple scattering and fluorescence measurements of individual cells and organisms to provide identifying signatures. Patterned after laboratory flow-cytometers, the in water devices will offer break-through capability in typing specific organisms in their natural environment. One of the most exciting aspects of the recent advancements in IOP-related technologies lies in the opportunities offered by their combined use. One marked example lies in the characterization of particle aggregations in the water. While the attenuation or scattering at one wavelength will provide data about relative concentrations of particles within the water column, spectral data from these sensors combined with absorption measurements can move us a long way toward characterizing the aggregation into various biological and inorganic components.

deflected. While these sensors play an increasing role in observing in water processes, they also provide a technological foundation for a new generation of sensors that extend IOP capabilities. These new sensors hold the ability to determine absorption coefficients, to determine coefficients as a function of wavelength, and to characterize the volume scattering function at more than one angle. These improvements not only allow more complete characterization of natural waters but also provide a tangible means of relating remotely sensed data from air and space to in-water processes.

See also Optical Particle Characterization. Radiative Transfer in the Ocean. Turbulence Sensors.

Further Reading

Summary Transmissometry and nephelometry provide increasingly valuable information relating to the lighttransmitting characteristics of water as well as an idea of the relative concentration of suspended material within lakes and oceans. While sometimes viewed as near-synonymous techniques, these methods use different measurement methods, provide different products, and have different strengths and weaknesses in considering the applications to which they are applied. Applications vary widely and across numerous disciplines, but tend to be divided into two major classes: those that attempt to characterize the fundamental optical properties of the water; and those that seek the relative concentrations of foreign particulate matter in the water. In general, nephelometry is the preferred technique in environmental and fresh water applications and transmissometry is more common in oceanographic research. Although transmissometry and nephelometry differ as measurement techniques, in their application domains, and in subsequent calibration and handling, all of these sensors are capable of providing outputs in terms of absolute coefficients that describe the fate of light passing through water. These coefficients of light transfer are collectively known as the inherent optical properties or IOPs. Their values are related through the volume scattering function that describes scattering as a function of angle into which light is

Bogucki DJ, Domaradzki JA, Stramski D, and Zaneveld JRV (1998) Comparison of near-forward light scattering on oceanic turbulence and particles. Applied Optics 37: 4669--4677. Bricaud A, Morel A, and Prieur L (1981) Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains. Limnology and Oceanography 26: 43--53. Greenberg AE, Clescerl LS, and Eaton AD (eds.) (1992) Standard Methods for the Examination of Water and Wastewater 18th edn. Washington, DC: American Public Health Association, AWWA, WEF. Jerlov NG (1976) Marine Optics. Amsterdam: Elsevier. Kirk JTO (1994) Light and Photosynthesis in Aquatic Ecosystems. Cambridge: Cambridge University Press. Mobley CD (1994) Light and Water: Radiative Transfer in Natural Waters. New York: Academic Press. Pegau WS, Paulson CA, and Zaneveld JRV (1996) Optical measurements of frazil concentration. Cold Regions Science and Technology 24: 341--353. Petzold TJ (1972) Volume Scattering Functions for Selected Ocean Waters. Reference Publication 72–28. La Jolla, CA: Scripps Institute of Oceanography. Tyler JE, Austin RW, and Petzold TJ (1974) Beam transmissometers for oceanographic measurements. In: Gibbs RJ (ed.) Suspended Solids in Water. New York: Plenum Press. Zaneveld JRV, Bartz R, and Kitchen JC (1990) A reflectivetube absorption meter. Ocean Optics X, Proceeding of the Society for Photo-Optical Instrumentation and Engineering 1302: 124--136.


TRITIUM–HELIUM DATING W. J. Jenkins, University of Southampton, Southampton, UK Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3048–3056, & 2001, Elsevier Ltd.

Introduction: Tritium in the Oceans Tritium (3H) is the heaviest isotope of hydrogen. Its nucleus consists of one proton (making it hydrogen) and two neutrons. Inasmuch as it is chemically hydrogen, tritium exists within the global environment primarily as part of the water molecule. Thus it is a potentially useful tracer of the hydrologic cycle, and an ideal tracer of water motions within the ocean. Tritium is radioactive, decaying with a halflife of 12.45 years to the stable, inert daughter isotope 3He. Because of its geologically short half-life, there is very little natural tritium in the environment. Small quantities are created by cosmic ray spallation (i.e. the smashing of atomic nuclei into small fragments by high-energy cosmic rays) in the upper atmosphere. The balance between production and radioactive decay leads to a global natural tritium inventory of approximately 4 kg. This natural inventory was dwarfed by the production of tritium by the atmospheric testing of nuclear fusion weapons during the 1950s and early 1960s. During this period, several hundred kilograms of tritium were released, largely late in the test series, and primarily in the Northern Hemisphere. The

detonations generally injected the tritium into the stratosphere, where it was quickly oxidized to form water vapor. Over a period of a few years, the tritiated water vapor was transferred, largely at mid-latitudes, to the troposphere, where it was rapidly ‘rained out’ to the earth’s surface. The delivery of bomb tritium to the earth’s surface was monitored by a number of WMO/IAEA (World Meteorological Organization (UN)/International Atomic Energy Authority) precipitation sampling stations. The pattern and timing of this delivery has been shown to consist of two primary components: a dominant northern, spike-like component, and a weaker southern component. Due to the geographic nature of the coupling between the stratosphere and the troposphere, tritium concentrations were elevated in both components toward higher latitudes, and weaker near the equator (Figure 1). Tritium levels in precipitation over land also tended to increase with altitude. The northern component reflects the more immediate injection of bomb tritium into the northern hemispheric hydrologic system because virtually all of the major detonations occurred in the Northern Hemisphere. Prior to the bomb tests, the concentration of natural tritium in rainfall was of the order of 5–10 tritium units (1 TU ¼ 1 tritium atom per 1018 normal hydrogen atoms). During the mid-1960s, tritium concentrations of more than several thousand TU were recorded in higher latitude, mid-continental locales such as Chicago, USA or Ottawa, Canada. The southern component, on the other hand, is much weaker in amplitude and more smeared out in time

Northern Factor

Southern Factor

_ 50 0 50 100 150 200 250 300 350 400 500

_ 200 0

5

10 15 20 25 30 35 40

Figure 1 Spatial pattern of the two dominant principal components of bomb tritium in precipitation. These were derived from a statistical analysis of the time variation of bomb tritium in precipitation by S. Doney.


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6 Northern Factor

Annual tritium flux

5

Score

4 3 2

Southern Factor

1 0 1960

1965

1970

1975 Year

1980

1985

Vapor exchange Precipitation Runoff Southern inflow Arctic flow

2 1.5 1 0.5

1990

0 1950

1960

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Year

Figure 2 Time history of the two dominant principal components of tritium in precipitation. Note that the northern component (red curve) is more spike-like and the southern component (blue curve) is more ‘smeared out’ in time.

Figure 3 The various modes of tritium deposition to the North Atlantic over time. Note that during the peak of bomb-testing, vapor deposition was dominant, but that after the early 1970s, the influx of fresh water from the Arctic plays a prominent role.

since it results primarily from the cross-equatorial leakage of northern hemispheric tritium with few local sources (Figure 2). Providing the production of bomb tritium is well known, the patterns and time variations of tritium concentrations in rain, environmental, and ocean waters provide useful insights into the hydrologic cycle and ocean circulation. Unfortunately, direct observation of environmental tritium levels was limited because the development of analytical techniques lagged events. Efforts are ongoing to reconstruct tritium records in precipitation by analysis of this isotope in tree rings. This has been made possible by the relatively recent development and improvement of high-sensitivity techniques of tritium measurement by 3He regrowth. The deposition of tritium to the oceans occurs both by direct precipitation and by vapor exchange. Vapor exchange is a two-way process, and in general dominates over the direct precipitation. There are relatively few direct measures of tritium concentration in atmospheric water vapor, but studies indicate that it is closely related to levels in precipitation. This linkage has been exploited in order to construct tritium depositional histories for ocean basins from tritium in precipitation records. Another pathway whereby tritium enters the ocean is through continental runoff and river flow. Tritium deposited to the continents ultimately flows to the oceans via lakes, rivers and groundwater flow, but is retained within the continental hydrosphere for time-scales of many years, thereby introducing a delayed input to the oceans. Further, when computing the time-evolving tritium inventory within an ocean basin, it is necessary to consider inflow and outflow across the basin’s boundaries. The relative importance of the various inputs to the ocean varied with time. An analysis of the tritium

budget for the North Atlantic Ocean, for example, shows that water vapor exchange (the magenta curve in Figure 3) and direct precipitation (the cyan curve in Figure 3) were the dominant inputs of tritium during the mid-1960s when the tritium ‘spike’ occurred. By the 1970s, however, the major input became the inflow of low salinity water from the Arctic (the dark blue curve in Figure 3). A substantial inventory of bomb tritium had been delivered to and held up within the Arctic fresh-water system, to be released more gradually to the subpolar oceans, and subsequently to the North Atlantic. This input can be seen in the distribution of tritium in surface waters as observed during the early 1980s (Figure 4). Figure 4 shows the intrusion of tritiumlabeled waters along the east coast of Greenland and the Labrador Sea (red areas). This is superimposed on a general southward-decreasing trend. In response to the deposition of tritium, North Atlantic surface water concentrations rose rapidly, reaching values approaching 18 TU, or about 40 times greater than natural, prebomb, surface ocean levels. After peaking in 1964, surface water concentrations decreased, in part due to radioactive decay of this isotope, but also due to the dilution of surface waters with older, lower tritium waters from below, and lower concentration Southern Hemisphere waters. Consequently, the surface water decrease observed is significantly faster than the radioactive decay timescale. The penetration of tritium into the oceans provides us with a direct visualization of the large-scale ventilation of the oceans. As a time-dependent dye, it stains water that has been in contact with the surface since the bomb tests in the 1960s. The time evolution of this picture highlights those processes that occur on decade time-scales that are important for climate change. Figure 5 is a north–south section taken


TRITIUM–HELIUM DATING

through the western North Atlantic in the early 1980s. The section shows how far the dye has penetrated along the pathway of the planetary-scale overturning circulation (‘the global conveyor’) and is

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Surface tritium (TU)

an important quantitative measure of the rate of this overturning on decade timescales. The boundary between high- and low-tritium waters at depths of 4–5 km corresponds to the transition region between the subtropical and subpolar 80W gyres. In the subpolar gyre, deep convection injected 0 40W tritium into deep and intermediate waters. In the subtropical gyre, subsurface penetration occurs from 60N the north, primarily along deep western boundary N 60 currents. Otherwise, bomb tritium is restricted to the upper 1 km, tracing the bowl-like structure of the main thermocline, which it penetrates by subduction 40N 40N of fluid by a combination of wind stress convergence (a process called ‘Ekman pumping’, i.e. convergence of surface waters due to wind forcing effectively 20N 20N pushes water downward) and southward penetration under lighter, warmer waters. A time series of tritium in the Sargasso Sea near 80W 0 Bermuda shows the penetration of this bomb tritium 60W 20W 40W into the subtropical North Atlantic (Figure 6). To TU (A) compensate for predictable radioactive decay, the 20 0 6 2 4 concentration of tritium has been decay-corrected to one point in time (arbitrarily chosen here to be 1981, 16 the approximate mid-point of the series). Two relatively sudden increases in tritium concentrations 12 occur in the deep waters. The first appears at a depth of about 1500 m in the late 1970s, whereas the 8 deeper one arrives in the late 1980s. These increases signal the arrival of waters that had been ‘ventilated’ 4 or exposed to the surface since the bomb tests. The delayed arrival provides a measure of the transit time 0 of properties southward from the outcropping re1990 1960 1970 1950 1980 gions, important knowledge for ocean climate (B) Year models. Figure 4 North Atlantic surface water tritium concentrations: The time series, however, is dominated in the (A) geographical distribution in the early 1980s; (B) variation with upper waters by the downward penetration of bomb time in the subtropics.

TU

0 1

12

4

Depth (km)

2 3 3 2 4 1

5 6

20

30

40

50

Latitude Figure 5 A North Atlantic tritium meridional section taken in the early 1980s.


60

0

122

TRITIUM–HELIUM DATING TU81n

0 3.5 3 2.5 1.5 2

4.5

4

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6.0

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_ 0.1 70

80

75

(B)

85

Year

Figure 6 A Bermuda time series of (A) tritium and (B) 3He. The tritium concentrations have been decay-corrected (that is, corrected for the effects of radioactive decay) to a fixed point in time (1981). This allows the effects of dilution and fluid motions to be seen.

tritium into the main thermocline. The tritium ‘spike’ first appears as a surface-intensified maximum at the beginning of the record, but then subsequently descends into the thermocline at a rate of about 20 m y1. As it descends, its intensity decreases due to dilution (the series has been corrected for radioactive decay). The rate at which this maximum descends into the thermocline is vital information for climate modeling; i.e., this information is important for predicting how the ocean will respond to changes on decade timescales.

Tritium–3He Dating in the Ocean !The penetration of tritium into the oceans, and its subsequent evolution, provides us with valuable information on ocean ventilation and large-scale circulation on multiyear and multidecade timescales.

However, it is possible to use this tracer in combination with its stable, inert daughter 3He to extend its utility to much shorter timescales, and provide a powerful measure of circulation and ventilation, as well as the rates of biological and chemical processing in the oceans. The manner in which this is accomplished can be seen in the following thought experiment. Imagine a parcel of water at the sea surface (Figure 7). Tritium within this fluid parcel is decaying, producing its daughter product 3He. (Half of the tritium decays to 3He in 12.45 years, while in 24.9 years, one-quarter would be left, and in 37.4 years, only one-eighth would remain, etc.) However, because it is at the sea surface, this 3He will be lost to the atmosphere via gas exchange. Thus no excess or ‘tritiugenic’ 3He would accumulate. However, should this water parcel sink below the surface and lose contact with the atmosphere, tritiugenic 3He would



123

Figure 7 The tritium–helium dating concept.

_ 100

Age (y)

_ 150

_ 150

35

_ 200

Lat

itud

e

30

h

_ 100

_ 200

Dept

Above 7 6 −7 5−6 4−5 3−4 2−3 1− 2 Below 1

_ 20

25

_ 25 20

_ 30 _ 35

de

gitu

n Lo

Figure 8 The distribution of tritium–helium age on a constant density surface (26.4 kg m3) in the subtropical North Atlantic.

accumulate at a predictable rate. By measuring both the tritium concentration and the accumulated 3He in the fluid parcel, the time that has elapsed since the fluid was last in contact with the surface can be determined according to the equation: He t ¼ 17:96ln 1 þ 3 ½ H

3

where t is the tritium–3He age in years, and [3He] and [3H] are the concentrations of 3He and tritium in the water, respectively. For typical surface water concentrations of a few tritium units, elapsed times as short as a month or two can be detected, and the upper limit to the dating technique is of the order of 10–20 years (see discussion below). This range of timescales is ideal for studying shallow-ocean circulation, ventilation, and biogeochemical processing.


124

TRITIUM–HELIUM DATING 0

10 15 20

1000

30 25

Depth (m)

2000 3000

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4000 5000 6000 (A)

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22.5

0

7.5

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4000

_ 2.5

5000 6000 160 (B)

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Longitude

Figure 9 Two deep Pacific zonal sections of 3He. Data are presented as isotope ratio anomaly (%), relative to atmospheric helium. Samples were processed during the WOCE Pacific hydrographic expeditions from 1989 to 1994.

In the subtropics, where wind-stress is convergent, water tends to be forced downward from the surface ocean into the thermocline. This downwelling is an important process for ventilation of the thermocline, and for driving the shallow gyre circulation. Figure 8 shows the measured tritium–helium age as measured on a constant density surface (1024 kg m3) in the eastern subtropical North Atlantic in the early 1990s. Water is youngest in the north-east, where the horizon rises toward the ocean surface. In fact, this horizon intercepts the base of the wintertime mixing layer, and the tritium–helium age of the water is less than one year, indicating that it was in active contact with the previous winter’s surface mixed layer. The age of the water increases monotonically as the layer deepens to the southwest, consistent with a south-

westward flow associated with the large-scale circulation of the gyre. The next logical step would be to use the observed age-gradients to compute fluid velocities. Before applying this technique quantitatively, however, there are two significant concerns that need to be considered. The first is the possible release of volcanic helium from submarine hydrothermal activity. This injection occurs at active volcanic centers, predominantly along midocean ridges, and to a lesser extent at near-axial seamounts. This helium is a mixture of primordial helium inherited during the earth’s formation from the presolar nebula and radiogenic helium produced by the decay of longlived radioactive U and Th isotopes in the deep earth. The injection of this helium is visible on a very large


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scale in the deep Pacific Ocean, where plumes of this helium extend across the basin (Figure 9). These plumes provide compelling evidence of ocean–crust interaction, terrestrial degassing, and trace deep ocean circulation (see Volcanic Helium). As this helium tends to be enriched in 3He compared to atmospheric helium, it may be confused with tritiugenic 3He. Such injections tend to occur in deeper waters, away from the surface where one would tend to use tritium–3He dating. Moreover, calculations indicate that despite the impressive signature in abyssal waters, the actual flux of volcanic 3 He is smaller than the tritiugenic production rate due to bomb tests. Clearly, however, caution should be exercised in areas where the two signals can interfere. The shallow North Atlantic, in particular, is well suited to tritium–3He dating, partly because water masses tend to be younger there, and partly because seafloor spreading rates (and hence the rate of injection of volcanic 3He into the deep water) are low. (One would expect, on average, that volcanic activity would be related to seafloor spreading rates.) A second concern arises from the behavior of the tritium–3He age in response to mixing. Returning to the model concept discussed earlier, it must be recognized that water does not circulate in discrete ‘parcels’ but is subjected to mixing. In general, this manifests itself in a ‘nonlinear’ response in the tritium–3He age. For example, consider two fluid parcels that undergo mixing in equal proportions (Figure 10). We consider, for simplicity, the case where the two are mixed in equal proportions, but the arguments apply equally well for an arbitrary mixture. In general, the tritium–3He age of the mixture would be calculated from its tritium and 3He concentrations, and will be different from the average of the component ages. That is, the age of the mixture is not equivalent to the mixture of the ages. The results for three example cases are shown in Table 1. In the first case, the average age of the two water masses should be slightly more than 22 years, but the tritium concentration of the mixture is dominated by water mass A, which is the younger water mass. In the second case, the mixture is significantly older than ‘average age’, again because it is dominated by the higher tritium component. Only when the two components are of equal tritium concentration (case 3) does the mixture age more closely match the average of the components. Even here, there is a deviation due to the logarithmic nature of the age dependence. Consideration of the scenarios presented in Table 1 reveals that when water masses mix, the tritium–3He age of the resultant mixture is weighted in favor of the water mass component with the

Water Mass A Tritium = TA Helium-3 = HA Age = 17.95 log(1 +

Water Mass B Tritium = TB Helium-3 = HB HA ) TA

Age = 17.95 log(1 +

HB ) TB

Water Mass C Tritium = TC = (TA + TB)/2 Helium-3 = HC = (HA + HB)/2 Age = 17.95 log(1 +

HC ) TC

Figure 10 The effect of mixing on the tritium–helium age.

Table 1 Examples of water mass mixing effects on the tritium– helium age

Case 1

Case 2

Case 3

Watermass A Watermass B 50 : 50 Mixture Watermass A Watermass B 50 : 50 Mixture Watermass A Watermass B 50 : 50 Mixture

[3H]

[3He]

Age (y)

10 1 5.5 10 1 5.5 10 10 5

1 10 5.5 100 0.1 50.05 10 1 5.5

1.71 43.04 12.45 43.04 1.71 41.51 17.95 1.71 13.32

greater tritium concentration. The implication of this is that a small admixture of a young, relatively tritium-rich water mass will depress the tritium–3He age disproportionately. Therefore, there will be a tendency for the tritium–3He age to be an underestimate of the true age in the presence of mixing. Although it seems a serious concern, consideration of real-world oceanographic situations indicates that this is not a significant problem for timescales of less than a decade. The effects of mixing on the tritium–3He age have been quantified by the development of an advection– diffusion equation for the age. This is accomplished by combining the definition of the tritium–3He age (t) with the advection–diffusion equations for tritium and 3He. 3 3 r He H @t ! þ 2 :rt þ u rt ¼ rðkrtÞ þ 1 k ½3 He ½3 H @t


126


where k is the turbulent diffusivity and u is the fluid velocity. The equation appears similar to that of an ideal age tracer (A), governed by ! u rA ¼ rðkrAÞ þ 1 except for the presence of the unsteady (time derivative) term and the last term on the right. The unsteady term arises from the fact that the parent distributions are changing with time, and the age distribution is adjusting accordingly. The last term appears more as a pseudovelocity that is a direct manifestation of the nonlinear mixing behavior exemplified in the two-water-mass thought experiment described earlier. Although the equation appears complex, the key point is that all the terms are observable. That is, given field observations of the tracers, the terms can be computed to within a value of k. The effects on the shallowest surfaces are small. Analysis of actual distributions within the shallow North Atlantic, for example, shows that deviations from ‘ideal’ behavior are negligibly small. Moreover, combining the age distributions with other tracers, for example salinity, and with geostrophic constraints, permits the determination of absolute velocities within the main thermocline to a resolution of order 0.1 cm s1.

See also Elemental Distribution: Overview. Ekman Transport and Pumping. Ocean Subduction. Ocean Circulation: Meridional Overturning Circulation. Water Types and Water Masses.

Further Reading Clarke WB, Jenkins WJ, and Top Z (1976) Determination of tritium by mass spectrometric measurement of 3He. International Journal of Applied Radioisotopes 27: 515. Doney SC, Glover DM, and Jenkins WJ (1992) A model function of the global bomb tritium distribution in precipitation, 1960–1986. Journal of Geophysical Research 97: 5481--5492. ¨ stlund HG (1993) A tritium Doney SC, Jenkins WJ, and O budget for the North Atlantic. Journal of Geophysical Research 98(C10): 18069--18081. Jenkins WJ (1978) Helium isotopes from the solid earth. Oceanus 21: 13. Jenkins WJ (1992) Tracers in oceanography. Oceanus 35: 47--56. Jenkins WJ (1998) Studying subtropical thermocline ventilation and circulation using tritium and 3He. Journal of Geophysical Research 103: 15817--15831. Jenkins WJ and Smethie WM (1996) Transient tracers track ocean climate signals. Oceanus 39: 29--32.


TSUNAMI P. L.-F. Liu, Cornell University, Ithaca, NY, USA & 2009 Elsevier Ltd. All rights reserved.

Introduction Tsunami is a Japanese word that is made of two characters: tsu and nami. The character tsu means harbor, while the character nami means wave. Therefore, the original word tsunami describes large wave oscillations inside a harbor during a ‘tsunami’ event. In the past, tsunami is often referred to as ‘tidal wave’, which is a misnomer. Tides, featuring the rising and falling of water level in the ocean in a daily, monthly, and yearly cycle, are caused by gravitational influences of the moon, sun, and planets. Tsunamis are not generated by this kind of gravitational forces and are unrelated to the tides, although the tidal level does influence a tsunami striking a coastal area. The phenomenon we call a tsunami is a series of water waves of extremely long wavelength and long period, generated in an ocean by a geophysical disturbance that displaces the water within a short period of time. Waves are formed as the displaced water mass, which acts under the influence of gravity, attempts to regain its equilibrium. Tsunamis are primarily associated with submarine earthquakes in oceanic and coastal regions. However, landslides, volcanic eruptions, and even impacts of objects from outer space (such as meteorites, asteroids, and comets) can also trigger tsunamis. Tsunamis are usually characterized as shallowwater waves or long waves, which are different from wind-generated waves, the waves many of us have observed on a beach. Wind waves of 5–20-s period (T ¼ time interval between two successive wave crests or troughs) have wavelengths (l ¼ T2(g/2p) distance between two successive wave crests or troughs) of c. 40–620 m. On the other hand, a tsunami can have a wave period in the range of 10 min to 1 h and a wavelength in excess of 200 km in a deep ocean basin. A wave is characterized as a shallowwater wave when the water depth is less than 5% of the wavelength. The forward and backward water motion under the shallow-water wave is felted throughout the entire water column. The shallow water wave is also sensitive to the change of water depth. For instance, the speed (celerity) of a shallowwater wave is equal to the square root of the product

of the gravitational acceleration (9.81 m s 2) and the water depth. Since the average water depth in the Pacific Ocean is 5 km, a tsunami can travel at a speed of about 800 km h 1 (500 mi h 1), which is almost the same as the speed of a jet airplane. A tsunami can move from the West Coast of South America to the East Coast of Japan in less than 1 day. The initial amplitude of a tsunami in the vicinity of a source region is usually quite small, typically only a meter or less, in comparison with the wavelength. In general, as the tsunami propagates into the open ocean, the amplitude of tsunami will decrease for the wave energy is spread over a much larger area. In the open ocean, it is very difficult to detect a tsunami from aboard a ship because the water level will rise only slightly over a period of 10 min to hours. Since the rate at which a wave loses its energy is inversely proportional to its wavelength, a tsunami will lose little energy as it propagates. Hence in the open ocean, a tsunami will travel at high speeds and over great transoceanic distances with little energy loss. As a tsunami propagates into shallower waters near the coast, it undergoes a rapid transformation. Because the energy loss remains insignificant, the total energy flux of the tsunami, which is proportional to the product of the square of the wave amplitude and the speed of the tsunami, remains constant. Therefore, the speed of the tsunami decreases as it enters shallower water and the height of the tsunami grows. Because of this ‘shoaling’ effect, a tsunami that was imperceptible in the open ocean may grow to be several meters or more in height. When a tsunami finally reaches the shore, it may appear as a rapid rising or falling water, a series of breaking waves, or even a bore. Reefs, bays, entrances to rivers, undersea features, including vegetations, and the slope of the beach all play a role modifying the tsunami as it approaches the shore. Tsunamis rarely become great, towering breaking waves. Sometimes the tsunami may break far offshore. Or it may form into a bore, which is a steplike wave with a steep breaking front, as the tsunami moves into a shallow bay or river. Figure 1 shows the incoming 1946 tsunami at Hilo, Hawaii. The water level on shore can rise by several meters. In extreme cases, water level can rise to more than 20 m for tsunamis of distant origin and over 30 m for tsunami close to the earthquake’s epicenter. The first wave may not always be the largest in the series of waves. In some cases, the water level will fall significantly first, exposing the bottom of a bay


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Figure 1 1946 tsunami at Hilo, Hawaii (Pacific Tsunami Museum). Wave height may be judged from the height of the trees.

or a beach, and then a large positive wave follows. The destructive pattern of a tsunami is also difficult to predict. One coastal area may see no damaging wave activity, while in a neighboring area destructive waves can be large and violent. The flooding of an area can extend inland by 500 m or more, covering large expanses of land with water and debris. Tsunamis may reach a maximum vertical height onshore above sea level, called a runup height, of 30 m. Since scientists still cannot predict accurately when earthquakes, landslides, or volcano eruptions will occur, they cannot determine exactly when a tsunami will be generated. But, with the aid of historical records of tsunamis and numerical models, scientists can get an idea as to where they are most likely to be generated. Past tsunami height measurements and computer modeling can also help to forecast future tsunami impact and flooding limits at specific coastal areas.

Historical and Recent Tsunamis Tsunamis have been observed and recorded since ancient times, especially in Japan and the Mediterranean areas. The earliest recorded tsunami occurred in 2000 BC off the coast of Syria. The oldest reference of tsunami record can be traced back to the sixteenth century in the United States. During the last century, more than 100 tsunamis have been observed in the United States alone. Among them, the 1946 Alaskan tsunami, the 1960 Chilean tsunami, and the 1964 Alaskan tsunami were the three most destructive tsunamis in the US history. The 1946 Aleutian earthquake (Mw ¼ 7.3) generated catastrophic tsunamis that attacked the Hawaiian Islands after traveling about 5 h and killed 159 people.

(The magnitude of an earthquake is defined by the seismic moment, M0 (dyn cm), which is determined from the seismic data recorded worldwide. Converting the seismic moment into a logarithmic scale, we define Mw ¼ (1/1.5)log10M0 10.7.) The reported property damage reached $26 million. The 1960 Chilean tsunami waves struck the Hawaiian Islands after 14 h, traveling across the Pacific Ocean from the Chilean coast. They caused devastating damage not only along the Chilean coast (more than 1000 people were killed and the total property damage from the combined effects of the earthquake and tsunami was estimated as $417 million) but also at Hilo, Hawaii, where 61 deaths and $23.5 million in property damage occurred (see Figure 2). The 1964 Alaskan tsunami triggered by the Prince William Sound earthquake (Mw ¼ 8.4), which was recorded as one of the largest earthquakes in the North American continent, caused the most destructive damage in Alaska’s history. The tsunami killed 106 people and the total damage amounted to $84 million in Alaska. Within less than a year between September 1992 and July 1993, three large undersea earthquakes strike the Pacific Ocean area, causing devastating tsunamis. On 2 September 1992, an earthquake of magnitude 7.0 occurred c. 100 km off the Nicaraguan coast. The maximum runup height was recorded as 10 m and 168 people died in this event. A few months later, another strong earthquake (Mw ¼ 7.5) attacked the Flores Island and surrounding area in Indonesia on 12 December 1992. It was reported that more than 1000 people were killed in the town of Maumere alone and two-thirds of the population of Babi Island were swept away by the tsunami. The maximum runup was estimated as 26 m. The final toll of this Flores earthquake stood at 1712 deaths and more than 2000 injures. Exactly


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Figure 2 The tsunami of 1960 killed 61 people in Hilo, destroyed 537 buildings, and damages totaled over $23 million.

7 months later, on 12 July 1993, the third strong earthquake (Mw ¼ 7.8) occurred near the Hokkaido Island in Japan (Hokkaido Tsunami Survey Group 1993). Within 3–5 min, a large tsunami engulfed the Okushiri coastline and the central west of Hokkaido, impinging extensive property damages, especially on the southern tip of Okushiri Island in the town of Aonae. The runup heights on the Okushiri Island were thoroughly surveyed and they varied between 15 and 30 m over a 20-km stretch of the southern part of the island, with several 10-m spots on the northern part of the island. It was also reported that although the runup heights on the west coast of Hokkaido are not large (less than 10 m), damage was extensive in several towns. The epicenters of these three earthquakes were all located near residential coastal areas. Therefore, the damage caused by subsequent tsunamis was unusually large. On 17 July 1998, an earthquake occurred in the Sandaun Province of northwestern Papua New Guinea, about 65 km northwest of the port city of Aitape. The earthquake magnitude was estimated as Mw ¼ 7.0. About 20 min after the first shock, Warapo and Arop villages were completely destroyed by tsunamis. The death toll was at over 2000 and many of them drowned in the Sissano Lagoon behind the Arop villages. The surveyed maximum runup height was 15 m, which is much higher than the predicted value based on the seismic information. It has been suggested that the Papua New Guinea tsunami could be caused by a submarine landslide. The most devastating tsunamis in recent history occurred in the Indian Ocean on 26 December 2004.

An earthquake of Mw ¼ 9.0 occurred off the west coast of northern Sumatra. Large tsunamis were generated, severely damaging coastal communities in countries around the Indian Ocean, including Indonesia, Thailand, Sri Lanka, and India. The estimated tsunami death toll ranged from 156 000 to 178 000 across 11 nations, with additional 26 500–142 000 missing, most of them presumed dead.

Tsunami Generation Mechanisms Tsunamigentic Earthquakes

Most tsunamis are the results of submarine earthquakes. The majority of earthquakes can be explained in terms of plate tectonics. The basic concept is that the outermost part the Earth consists of several large and fairly stable slabs of solid and relatively rigid rock, called plates (see Figure 3). These plates are constantly moving (very slowly), and rub against one another along the plate boundaries, which are also called faults. Consequently, stress and strain build up along these faults, and eventually they become too great to bear and the plates move abruptly so as to release the stress and strain, creating an earthquake. Most of tsunamigentic earthquakes occur in subduction zones around the Pacific Ocean rim, where the dense crust of the ocean floor dives beneath the edge of the lighter continental crust and sinks down into Earth’s mantle. These subduction zones include the west coasts of North and South America, the coasts of East Asia (especially Japan), and many Pacific island chains (Figure 3). There are


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different types of faults along subduction margins. The interplate fault usually accommodates a large relative motion between two tectonic plates and the overlying plate is typically pushed upward. This upward push is impulsive; it occurs very quickly, in a

Ridge axis divergent boundary

few seconds. The ocean water surface responds immediately to the upward movement of the seafloor and the ocean surface profile usually mimics the seafloor displacement (see Figure 4). The interplate fault in a subduction zone has been responsible for

Subduction zone Convergent boundary

Transform

Zone of extension with continents

Uncertain plate boundary

Figure 3 Major tectonic plates that make up the Earth’s crust.

(a)

(b)

Stuck S ubdu cting

(c)

Earthquake starts tsunami

Overriding plate

Slow distortion

p late

(d)

Tsunami waves spread

Stuck area ruptures, releasing energy in an earthquake Figure 4 Sketches of the tsunami generation mechanism caused by a submarine earthquake. An oceanic plate subducts under an overriding plate (a). The overriding plate deforms due to the relative motion and the friction between two tectonic plates (b). The stuck area ruptures, releasing energy in an earthquake (c). Tsunami waves are generated due to the vertical seafloor displacement (d).


TSUNAMI

most of the largest tsunamis in the twentieth century. For example, the 1952 Kamchatka, 1957 Aleutian, 1960 Chile, 1964 Alaska, and 2004 Sumatra earthquakes all generated damaging tsunamis not only in the region near the earthquake epicenter, but also on faraway shores. For most of the interplate fault ruptures, the resulting seafloor displacement can be estimated based on the dislocation theory. Using the linear elastic theory, analytical solutions can be derived from the mean dislocation field on the fault. Several parameters defining the geometry and strength of the fault rupture need to be specified. First of all, the mean fault slip, D, is calculated from the seismic moment M0 as follows: M0 ¼ mDS

estimation, the fault plane can be approximated as a rectangle with length L and width W. The aspect ratio L/W could vary from 2 to 8. To find the static displacement of the seafloor, we need to assign the focal depth d, measuring the depth of the upper rim of the fault plane, the dip angle d, and the slip angle l of the dislocation on the fault plane measured from the horizontal axis (see Figure 5). For an oblique slip on a dipping fault, the slip vector can be decomposed into dip-slip and strike-slip components. In general, the magnitude of the vertical displacement is less for the strike-slip component than for the dip-slip component. The closed form expressions for vertical seafloor displacement caused by a slip along a rectangular fault are given by Mansinha and Smylie. For more realistic fault models, nonuniform stressstrength fields (i.e., faults with various kinds of barriers, asperities, etc.) are expected, so that the actual seafloor displacement may be very complicated compared with the smooth seafloor displacement computed from the mean dislocation field on the fault. As an example, the vertical seafloor displacement caused by the 1964 Alaska earthquake is sketched in Figure 6. Although several numerical models have considered geometrically complex faults, complex slip distributions, and elastic layers of variable thickness, they are not yet disseminated in

½1

din

al)

where S is the rupture area and m is the rigidity of the Earth at the source, which has a range of 6–7 1011 dyn cm 2 for interplate earthquakes. The seismic moment, M0, is determined from the seismic data recorded worldwide and is usually reported as the Harvard Centroid-Moment-Tensor (CMT) solution within a few minutes of the first earthquake tremor. The rupture area is usually estimated from the aftershock data. However, for a rough

(la

titu

X

rth no To

Y

O

lt au

e

lin

Overriding block

F

Tectonic motion

Foot block

Z

Symbols

X

Fa

Foot block

W

W Width of fault plane

Slip direct ion

O

Y

L Length of fault plane

ine

l ult

131

Strike angle Dip angle

Slip angle L

X OY parrallel to the horizontal Earth surface; OZ pointing upward; is the azimuth of OX measuring clockwise from the latitudinal Figure 5 A sketch of fault plane parameters.


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NW

19.5 m

0

5.2 m

Horizontal SW displacements

SE

3.5+ m?

m

11.3 m

12 8

5.0 m

Vertical displacements

4

3.5 m

0 −2

Volcanic arc

Patton Bay Fault

Anchorage North American Plate

Aleutian

Middleton Island

400

300

km 0

Megathrust

200

50

Pacific Plate and mantle

H/V = 1 475

Aleutian Trench

100

0 km

100

Figure 6 A sketch of 1964 Alaska earthquake generated vertical seafloor displacement (G. Plafker, 2006).

tsunami research. One of the reasons is that our knowledge in source parameters, inhomogeneity, and nonuniform slip distribution is too incomplete to justify using such a complex model. Certain earthquakes referred to as tsunami earthquakes have slow faulting motion and very long rupture duration (more than several minutes). These earthquakes occur along the shallow part of the interplate thrust or dećollement near the trench (the wedge portion of the thin crust above the interface of the continental crust and the ocean plate). The wedge portion consisting of thick deformable sediments with low rigidity, and the steepening of rupture surface in shallow depth all favor the large displacement of the crust and possibility of generating a large tsunami. Because of the extreme heterogeneity, accurate modeling is difficult, resulting in large uncertainty in estimated seafloor displacement. Landslides and Other Generation Mechanisms

There are occasions when the secondary effects of earthquakes, such as landslide and submarine slump, may be responsible for the generation of tsunamis. These tsunamis are sometimes disastrous and have gained increasing attentions in recent years. Landslides are generated when slopes or sediment deposits become too steep and they fail to remain in equilibrium and motionless. Once the unstable conditions are present, slope failure can be triggered by storm, earthquakes, rains, or merely continued

deposition of materials on the slope. Alternative mechanisms of sediment instability range between soft sediment deformations in turbidities, to rotational slumps in cohesive sediments. Certain environments are particularly susceptible to the production of landslides. River delta and steep underwater slopes above submarine canyons are likely sites for landslide-generated tsunamis. At the time of the 1964 Alaska earthquake, numerous locally landslide-generated tsunamis with devastating effects were observed. On 29 November 1975, a landslide was triggered by a 7.2 magnitude earthquake along the southeast coast of Hawaii. A 60-km stretch of Kilauea’s south coast subsided 3.5 m and moved seaward 8 m. This landslide generated a local tsunami with a maximum runup height of 16 m at Keauhou. Historically, there have been several tsunamis whose magnitudes were simply too large to be attributed to the coseismic seafloor movement and landslides have been suggested as an alternative cause. The 1946 Aleutian tsunami and the 1998 Papua New Guinea tsunami are two significant examples. In terms of tsunami generation mechanisms, two significant differences exist between submarine landslide and coseismic seafloor deformation. First, the duration of a landslide is much longer and is in the order of magnitude of several minutes or longer. Hence the time history of the seafloor movement will affect the characteristics of the generated wave and needs to be included in the model. Second, the


TSUNAMI

effective size of the landslide region is usually much smaller than the coseismic seafloor deformation zone. Consequently, the typical wavelength of the tsunamis generated by a submarine landslide is also shorter, that is, c. 1–10 km. Therefore, in some cases, the shallow-water (long-wave) assumption might not be valid for landslide-generated tsunamis. Although they are rare, the violent geological activities associated with volcanic eruptions can also generate tsunamis. There are three types of tsunamigeneration mechanism associated with a volcanic eruption. First, the pyroclastic flows, which are mixtures of gas, rocks, and lava, can move rapidly off an island and into an ocean, their impact displacing seawater and producing a tsunami. The second mechanism is the submarine volcanic explosion, which occurs when cool seawater encounters hot volcanic magma. The third mechanism is due to the collapse of a submarine volcanic caldera. The collapse may happen when the magma beneath a volcano is withdrawn back deeper into the Earth, and the sudden subsidence of the volcanic edifice displaces water and produces a tsunami. Furthermore, the large masses of rock that accumulate on the sides of volcanoes may suddenly slide down the slope into the sea, producing tsunamis. For example, in 1792, a large mass of the mountain slided into Ariake Bay in Shimabara on Kyushu Island, Japan, and generated tsunamis that reached a height of 10 m in some places, killing a large number of people. In the following sections, our discussions will focus on submarine earthquake-generated tsunamis and their coastal effects.

Modeling of Tsunami Generation, Propagation, and Coastal Inundation To mitigate tsunami hazards, the highest priority is to identify the high-tsunami-risk zone and to educate the citizen, living in and near the risk zone, about the proper behaviors in the event of an earthquake and tsunami attack. For a distant tsunami, a reliable warning system, which predicts the arrival time as well as the inundation area accurately, can save many lives. On the other hand, in the event of a nearfield tsunami, the emergency evacuation plan must be activated as soon as the earth shaking is felt. This is only possible, if a predetermined evacuation/ inundation map is available. These maps should be produced based on the historical tsunami events and the estimated ‘worst scenarios’ or the ‘design tsunamis’. To produce realistic and reliable inundation maps, it is essential to use a numerical model that calculates accurately tsunami propagation from

133

a source region to the coastal areas of concern and the subsequent tsunami runup and inundation. Numerical simulations of tsunami have made great progress in the last 50 years. This progress is made possible by the advancement of seismology and by the development of the high-speed computer. Several tsunami models are being used in the National Tsunami Hazard Mitigation Program, sponsored by the National Oceanic and Atmospheric Administration (NOAA), in partnership with the US Geological Survey (USGS), the Federal Emergency Management Agency (FEMA), to produce tsunami inundation and evacuation maps for the states of Alaska, California, Hawaii, Oregon, and Washington. Tsunami Generation and Propagation in an Open Ocean

The rupture speed of fault plane during earthquake is usually much faster than that of the tsunami. For instance, the fault line of the 2004 Sumatra earthquake was estimated as 1200-km long and the rupture process lasted for about 10 min. Therefore, the rupture speed was c. 2–3 km s 1, which is considered as a relatively slow rupture speed and is still about 1 order of magnitude faster than the speed of tsunami (0.17 km s 1 in a typical water depth of 3 km). Since the compressibility of water is negligible, the initial free surface response to the seafloor deformation due to fault plane rupture is instantaneous. In other words, in terms of the tsunami propagation timescale, the initial free surface profile can be approximated as having the same shape as the seafloor deformation at the end of rupture, which can be obtained by the methods described in the previous section. As illustrated in Figure 6, the typical cross-sectional free surface profile, perpendicular to the fault line, has an N shape with a depression on the landward side and an elevation on the ocean side. If the fault plane is elongated, that is, L4 4W, the free surface profile is almost uniform in the longitudinal (fault line) direction and the generated tsunamis will propagate primarily in the direction perpendicular to the fault line. The wavelength is generally characterized by the width of the fault plane, W. The measure of tsunami wave dispersion is represented by the depth-to-wavelength ratio, that is, m2 ¼ h/l, while the nonlinearity is characterized by the amplitude-to-depth ratio, that is, e ¼ A/h. A tsunami generated in an open ocean or on a continental shelf could have an initial wavelength of several tens to hundreds of kilometers. The initial tsunami wave height may be on the order of magnitude of several meters. For example, the 2004 Indian Ocean tsunami


134

TSUNAMI

had a typical wavelength of 200 km in the Indian Ocean basin with an amplitude of 1 m. The water depth varies from several hundreds of meters on the continental shelf to several kilometers in the open ocean. It is quite obvious that during the early stage of tsunami propagation both the nonlinear and frequency dispersion effects are small and can be ignored. This is particularly true for the 2004 Indian tsunami. The bottom frictional force and Coriolis force have even smaller effects and can be also neglected in the generation area. Therefore, the linear shallow water (LSW) equations are adequate equations describing the initial stage of tsunami generation and propagation. As a tsunami propagates over an open ocean, wave energy is spread out into a larger area. In general, the tsunami wave height decreases and the nonlinearity remains weak. However, the importance of the frequency dispersion begins to accumulate as the tsunami travels a long distance. Theoretically, one can estimate that the frequency dispersion becomes important when a tsunami propagates for a long time: sffiffiffi h l 3 ½2 t4 4td ¼ g h or over a long distance: x4 4xd ¼ td

pffiffiffiffiffiffi l3 gh ¼ 2 h

½3

In the case of the 2004 Indian Ocean tsunami, tdE700 h and xdE5 105 km. In other words, the frequency dispersion effect will only become important when tsunamis have gone around the Earth several times. Obviously, for a tsunami with much shorter wavelength, for example, lE20 km, this distance becomes relatively short, that is, xdE5 102 km, and can be reached quite easily. Therefore, in modeling transoceanic tsunami propagation, frequency dispersion might need to be considered if the initial wavelength is short. However, nonlinearity is seldom a factor in the deep ocean and only becomes significant when the tsunami enters coastal region. The LSW equations can be written in terms of a spherical coordinate system as: @z 1 @P @ @h þ þ ðcosjQÞ ¼ ½4 @t Rcosj @c @j @t @P gh @z þ ¼0 @t Rcosj @c

½5

@Q gh @z þ ¼0 @t R @j

½6

where (c,j) denote the longitude and latitude of the Earth, R is the Earth’s radius, z is free surface elevation, P and Q the volume fluxes (P ¼ hu and Q ¼ hv, with u and v being the depth-averaged velocities in longitude and latitude direction, respectively), and h the water depth. Equation [4] represents the depth-integrated continuity equation, and the time rate of change of water depth has been included. When the fault plane rupture is approximated as an instantaneous process and the initial free surface profile is prescribed, the water depth remains timeinvariant during tsunami propagation and the righthand side becomes zero in eqn [4]. The 2004 Indian Ocean tsunami provided an opportunity to verify the validity of LSW equations for modeling tsunami propagation in an open ocean. For the first time in history, satellite altimetry measurements of sea surface elevation captured the Indian Ocean tsunami. About 2 h after the earthquake occurred, two NASA/French Space Agency joint mission satellites, Jason-1 and TOPEX/Poseidon, passed over the Indian Ocean from southwest to northeast ( Jason-1 passed the equator at 02:55:24UTC on 26 December 2004 and TOPEX/ Poseidon passed the equator at 03:01:57UTC on 26 December 2004) (see Figure 7). These two altimetry satellites measured sea surface elevation with accuracy better than 4.2 cm. Using the numerical model COMCOT (Cornell Multi-grid Coupled Tsunami Model), numerical simulations of tsunami propagation over the Indian Ocean with various fault plane models, including a transient seafloor movement model, have been carried out. The LSW equation model predicts accurately the arrival time of the leading wave and is insensitive of the fault plane models used. However, to predict the trailing waves, the spatial variation of seafloor deformation needs to be taken into consideration. In Figure 8, comparisons between LSW results with an optimized fault plane model and Jason-1/TOPEX measurements are shown. The excellent agreement between the numerical results and satellite data provides a direct evidence for the validity of the LSW modeling of tsunami propagation in deep ocean.

Coastal Effects – Inundation and Tsunami Forces

Nonlinearity and bottom friction become significant as a tsunami enters the coastal zone, especially during the runup phase. The nonlinear shallow water (NLSW) equations can be used to model certain aspects of coastal effects of a tsunami attack. Using the same notations as those in eqns [4]–[6], the NLSW


TSUNAMI

135

20

15

0.8

10

0.4 0

0.2

TOP

−10

EX

−0.2 −0.4

−1

0

−5

Jason

Latitude (deg)

0.6 5

−0.6

−15

−0.8 70

75

80

85 90 Longitude (deg)

95

100

105

Figure 7 Satellite tracks for TOPEX and Jason-1. The colors indicate the numerically simulated free surface elevation in meter at 2 h after the earthquake struck.

equations in the Cartesian coordinates are @z @P @Q þ þ ¼0 @t @x @y

½7

@P @ P2 @ PQ @z þ þ þ gH þ tx H ¼ 0 @t @x H @y H @x

½8

@Q @ PQ @ Q2 @z þ gH þ ty H ¼ 0 þ þ @t @x H @y H @y

½9

The bottom frictional stresses are expressed as tx ¼

gn2 PðP2 þ Q2 Þ1=2 H 10=3

½10

ty ¼

gn2 QðP2 þ Q2 Þ1=2 H 10=3

½11

where n is the Manning’s relative roughness coefficient. For flows over a sandy beach, the typical value for the Manning’s n is 0.02. Using a modified leapfrog finite difference scheme in a nested grid system, COMCOT is capable of solving both LSW and NLSW equations simultaneously in different regions. For the nested grid system, the inner (finer) grid adopts a smaller grid size and time step compared to its adjacent outer (larger) grid. At the beginning of a time step, along the interface of two different grids, the volume flux, P and Q, which is product of water depth and depthaveraged velocity, is interpolated from the outer (larger) grids into its inner (finer) grids. And at the end of this time step, the calculated water surface elevations, z, at the inner finer grids are averaged to update those values of the larger grids overlapping the finer grids, which are used to compute the volume fluxes at next time step in the outer grids. With this procedure, COMCOT can capture near-shore


TSUNAMI

Water surface elevation (m)

(a)

(b)

1

Jason-1 Model

0.8 0.6 0.4 0.2 0 −0.2 −0.4 −0.6 −0.8 −1

−5

0

5 10 Latitude (deg)

Water surface elevation (m)

136

Model TOPEX

0.8 0.6 0.4 0.2 0 −0.2 −0.4 −0.6 −0.8 −1

15

Model vs. TOPEX

1

−5

0

5 10 Latitude (deg)

15

Figure 8 Comparisons between optimized fault model results and Jason-1 measurements (a)/TOPEX measurements (b).

(a)

Grids with dx = 36.7 m

(b)

5.6

5.6

5.5

Latitude (deg)

Ulee Lheue BANDA ACEH

5.55

Lampuuk Lhoknga

5.45

5.55 5.5 5.45

Leupung

5.4

5.4

Inundated area Dry land Ocean

95.15

95.2

95.25

95.3

95.35

95.4

95.15

95.2

95.25 95.3 Longitude (deg)

95.35

95.4

Figure 9 Calculated inundation areas (a) and overlaid with QUICKBIRD image (b) in Banda Aceh, Indonesia.

features of a tsunami with a higher spatial and temporal resolution and at the same time can still keep a high computational efficiency. To estimate the inundation area caused by a tsunami, COMCOT adopts a simple moving boundary scheme. The shoreline is defined as the interface between a wet grid and its adjacent dry grids. Along the shoreline, the volume flux is assigned to be zero. Once the water surface elevation at the wet grid is higher than the land elevation in its adjacent dry grid, the shoreline is moved by one grid toward the dry grid and the volume flux is no longer zero and need to be calculated by the governing equations. COMCOT, coupled up to three levels of grids, has been used to calculate the runup and inundation areas at Trincomalee Bay (Sri Lanka) and Banda Aceh (Indonesia). Some of the numerical results for Banda Aceh are shown here.

The calculated inundation area in Banda Aceh is shown in Figure 9. The flooded area is marked in blue, the dry land region is rendered in green, and the white area is ocean region. The calculated inundation area is also overlaid with a satellite image taken by QUICKBIRD in Figure 9(b). In the overlaid image, the thick red line indicates the inundation line based on the numerical simulation. In the satellite image, the dark green color (vegetation) indicates areas not affected by the tsunami and the area shaded by semitransparent red color shows flooded regions by this tsunami. Obviously, the calculated inundation area matches reasonably well with the satellite image in the neighborhood of Lhoknga and the western part of Banda Aceh. However, in the region of eastern Banda Aceh, the simulations significantly underestimate the inundation area. However, in general, the agreement


137

TSUNAMI

5.52

5.5

5.5

5.48

5.48

Survey by Tsuji et al.

Survey by Shibayama et al.

Latitude (deg)

Latitude (deg)

Lampuuk

5.46 5.44 5.42

5.36 40

5.42

30 20 10 Tsunami heights (m)

5.38

0

5.36 95.2

10 5 0 95.24

95.26

95.28


95.34

95.36

95.38

95.36

95.38

5.62

5.4 Survey by Tsuji et al. Survey by Shibayama et al. Numerical result Nearest numerical result

Survey by Tsuji et al. Survey by Shibayama et al. Numerical result Nearest numerical result

Lhoknga

5.44

Leupung

5.4 5.38

5.46

15

Flooded area Dry land Ocean

95.22 95.24 95.26 95.28 Longitude (deg)

Latitude (deg)

5.52

is measured more than 30 m, the numerical results match very well with the field measurements. However, beyond Lhoknga to the north, the numerical results, in general, are only half of the measurements, except in middle regions between Lhoknga and Lampuuk.

Tsunami heights (m)

between the numerical simulation and the satellite observation is surprisingly good. In Figure 10, the tsunami wave heights in Banda Aceh are also compared with the field measurements by two Japan survey teams. On the coast between Lhoknga and Leupung, where the maximum height

5.6 5.58

Survey by Tsuji et al.

Survey by Shibayama et al.

Flooded area

Dry land Ocean

5.56

Ulee Lheue

5.54 95.24

BANDA ACEH

95.26

95.28


95.34

Figure 10 Tsunami heights on eastern and northern coast of Banda Aceh, Indonesia. The field survey measurements are from Tsuji et al. (2005) and Shibayama et al. (2005).

120.0° E 135.0° E

150.0° E 165.0° E

180.0° E

165.0° W 150.0° W 135.0° W 120.0° W 105.0° W 90.0° W

75.0° W

60.0° W

45.0° W

60.0° N

45.0° N

30.0° N

15.0° N

15.0° S

30.0° S

45.0° S

Figure 11 The locations of the existing and planned Deep-Ocean Assessment and Reporting of Tsunamis (DART) system in the Pacific Ocean (NOAA magazine, 17 Apr. 2006).


138

TSUNAMI

Tsunami Hazard Mitigation The ultimate goal of the tsunami hazard mitigation effort is to minimize casualties and property damages. This goal can be met, only if an effective tsunami early warning system is established and a proper coastal management policy is practiced. Tsunami Early Warning System

The great historical tsunamis, such as the 1960 Chilean tsunami and the 1964 tsunami generated near Prince William Sound in Alaska, prompted the US government to develop an early warning system in the Pacific Ocean. The Japanese government has

Bidirectional communication and control

Iridium satellite

also developed a tsunami early warning system for the entire coastal community around Japan. The essential information needed for an effective early warning system is the accurate prediction of arrival time and wave height of a forecasted tsunami at a specific location. Obviously, the accuracy of these predictions relies on the information of the initial water surface displacement near the source region, which is primarily determined by the seismic data. In many historical events, including the 2004 Indian Ocean tsunami, evidences have shown that accurate seismic data could not be verified until those events were over. To delineate the source region problem, in the United States, several federal agencies and states

DART II System Optional met sensors

Iridium and GPS antennas Electronic systems and batteries

Tsunami warning center

Optional sensor mast

Wind Barometric pressure Sea surface temperature and conductivity Air temperature/ relative humidity

Lifting handle 2.0 m

Surface buoy 2.5-m diameter 4000-kg displacement Swivel Acoustic transducers (two each) Tsunameter

25-mm chain (3.5m)

Signal flag

Glass ball flotation

1.8 m

Bidirectional acoustic telemetry

13-mm polyester

~75 m

25-mm nylon 22-mm nylon

19-mm nylon Acoustic transducer Acoustic release CPU Batteries Sensor Anchor 325 kg

13-mm chain (5 m) Anchor 3100 kg

Figure 12 A sketch of the second-generation DART (II) system.


1000 − 6000 m

TSUNAMI

Newport, Oregon Highway 101 This map is intended for emergency planning purposes only

Yaquina Bay

139

These models can simulate a ‘design tsunami’ approaching a coastline, and they can predict which areas are most at risk to being flooded. The tsunami inundation maps are an integral part of the overall strategy to reduce future loss of life and property. Emergency managers and local governments of the threatened communities use these and similar maps to guide evacuation planning. As an example, the tsunami inundation map (Figure 13) for the coastal city of Newport (Oregon) was created using the results from a numerical simulation using a design tsunami. The areas shown in orange are locations that were flooded in the numerical simulation

Acknowledgment Highway 101

The work reported here has been supported by National Science Foundation with grants to Cornell University.

Figure 13 Tsunami inundation map for the coastal city of Newport, Oregon.

have joined together to create a warning system that involves the use of deep-ocean tsunami sensors to detect the presence of a tsunami. These deep-ocean sensors have been deployed at different locations in the Pacific Ocean before the 2004 Indian Ocean tsunami. After the 2004 Indian Ocean tsunami, several additional sensors have been installed and many more are being planned (see Figure 11). The sensor system includes a pressure gauge that records and transmits the surface wave signals instantaneously to the surface buoy, which sends the information to a warning center via Iridium satellite (Figure 12). In the event of a tsunami, the information obtained by the pressure gauge array can be used as input data for modeling the propagation and evolution of a tsunami. Although there have been no large Pacific-wide tsunamis since the inception of the warning system, warnings have been issued for smaller tsunamis, a few of which were hardly noticeable. This tends to give citizens a lazy attitude toward a tsunami warning, which would be fatal if the wave was large. Therefore, it is very important to keep people in a danger areas educated of tsunami hazards. Coastal Inundation Map

Using numerical modeling, hazards in areas vulnerable to tsunamis can be assessed, without the area ever having experienced a devastating tsunami.

See also Glacial Crustal Rebound, Sea Levels, and Shorelines. Heat and Momentum Fluxes at the Sea Surface. Land–Sea Global Transfers. Sea Level Variations Over Geologic Time. Seismology Sensors. Sensors for Mean Meteorology. Sensors for Micrometeorological and Flux Measurements. Turbulence Sensors. Wave Generation by Wind. Waves on Beaches.

Further Reading Geist EL (1998) Local tsunami and earthquake source parameters. Advances in Geophysics 39: 117--209. Hokkaido Tsunami Survey Group (1993) Tsunami devastates Japanese coastal region. EOS Transactions of the American Geophysical Union 74: 417--432. Kajiura K (1981) Tsunami energy in relation to parameters of the earthquake fault model. Bulletin of the Earthquake Research Institute, University of Tokyo 56: 415--440. Kajiura K and Shuto N (1990) Tsunamis. In: Le Me´haute´ B and Hanes DM (eds.) The Sea: Ocean Engineering Science, pp. 395--420. New York: Wiley. Kanamori H (1972) Mechanism of tsunami earthquakes. Physics and Earth Planetary Interactions 6: 346--359. Kawata Y, Benson BC, Borrero J, et al. (1999) Tsunami in Papua New Guinea was as intense as first thought. EOS Transactions of the American Geophysical Union 80: 101, 104--105. Keating BH and Mcguire WJ (2000) Island edifice failures and associated hazards. Special Issue: Landslides and Tsunamis. Pure and Applied Geophysics 157: 899--955.


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Liu PL-F, Lynett P, Fernando H, et al. (2005) Observations by the International Tsunami Survey Team in Sri Lanka. Science 308: 1595. Lynett PJ, Borrero J, Liu PL-F, and Synolakis CE (2003) Field survey and numerical simulations: A review of the 1998 Papua New Guinea tsunami. Pure and Applied Geophysics 160: 2119--2146. Mansinha L and Smylie DE (1971) The displacement fields of inclined faults. Bulletin of Seismological Society of America 61: 1433--1440. Satake K, Bourgeois J, Abe K, et al. (1993) Tsunami field survey of the 1992 Nicaragua earthquake. EOS Transactions of the American Geophysical Union 74: 156--157. Shibayama T, Okayasu A, Sasaki J, et al. (2005) The December 26, 2004 Sumatra Earthquake Tsunami, Tsunami Field Survey in Banda Aceh of Indonesia. http://www.drs.dpri.kyoto-u.ac.jp/sumatra/indonesia-ynu/ indonesia_survey_ynu_e.html (accessed Feb. 2008). Synolakis CE, Bardet J-P, Borrero JC, et al. (2002) The slump origin of the 1998 Papua New Guinea tsunami.

Proceedings of Royal Society of London, Series A 458: 763--789. Tsuji Y, Matsutomi H, Tanioka Y, et al. (2005) Distribution of the Tsunami Heights of the 2004 Sumatra Tsunami in Banda Aceh measured by the Tsunami Survey Team. http://www.eri.u-tokyo.ac.jp/namegaya/sumatera/ surveylog/eindex.htm (accessed Feb. 2008). von Huene R, Bourgois J, Miller J, and Pautot G (1989) A large tsunamigetic landslide and debris flow along the Peru trench. Journal of Geophysical Research 94: 1703--1714. Wang X and Liu PL-F (2006) An analysis of 2004 Sumatra earthquake fault plane mechanisms and Indian Ocean tsunami. Journal of Hydraulics Research 44(2): 147--154. Yeh HH, Imamura F, Synolakis CE, Tsuji Y, Liu PL-F, and Shi S (1993) The Flores Island tsunamis. EOS Transactions of the American Geophysical Union 74: 369--373.


TURBULENCE IN THE BENTHIC BOUNDARY LAYER

& 2008 Elsevier Ltd. All rights reserved.

Introduction Fluids do not slip at solid boundaries. The fluid velocity changes from 0 to a speed that matches the ‘far field’ in a transition, or boundary layer, where friction and shear (the rate of change of velocity with distance from the boundary) are strong. The thickness of the ocean bottom (benthic) boundary layer is determined by the bottom stress and the rate of rotation of the Earth. The benthic boundary layer is usually thin (O(10 m)) compared to typical ocean depths of B4000 m. However, in coastal regions which are shallow, and where currents and thus friction are relatively strong compared to the deep ocean, the benthic boundary layer may span most of the water column. The boundary layer can be separated into several layers within which some forces are much stronger than others. Neglect of the weaker forces leads to scaling and parametrization of the flow within each layer. The benthic boundary layer is usually considered to consist of (1) an outer or Ekman layer in which rotation and turbulent friction (Reynolds stress) are important; (2) a very thin (O(10 3 m)) viscous layer right next to the boundary where molecular friction is important; and (3) a transitional layer between these, usually called the logarithmic layer, in which turbulent friction is important (Figure 1). The pressure gradient is an important force in all the three layers. Because the velocity profile within the logarithmic layer must match smoothly with both the Ekman layer above and the viscous layer below, it will be considered last. This discussion is framed in the context of a neutrally-stratified ocean remote from the free surface. Additional constraints due to stratification and proximity to the free surface are noted later.

The Ekman Layer Most of the open ocean is essentially frictionless and in geostrophic balance, being well described by a

balance between the Coriolis force which pushes the flow to the right (in the Northern Hemisphere) and the pressure gradient which keeps it from veering (Figure 2(a)).This picture changes as we approach the bottom. Friction acts against the flow and decreases the velocity U. However, the pressure gradient remains and is not completely balanced by the Coriolis force fU. The current backs leftward so that friction, which is directed against the current, establishes a balance of forces in the horizontal plane (Figure 2(b)). Progressively closer to the bottom, the increasing friction slows the flow and brings it to a complete halt right at the bottom while also further backing the flow direction. A vertical profile of the two components of the horizontal velocity might look like those depicted in Figure 3. The equations of motion and their boundary conditions are 1 @P 1 @tx 1 @P 1 @ty ; fU ¼ þ þ fV ¼ r @x r @z r @y r @z U ¼ Ug ; V ¼ Vg ; tx ¼ ty ¼ 0 as z-N U ¼ V ¼ 0 at z ¼ 0

H =u* /f

½1

Ekman height

Z

R. Lueck and L. St. Laurrent, University of Victoria, Victoria, BC, Canada J. N. Moum, Oregon State University, Corvallis, OR, USA

»0.03H Log layer

Linear viscous layer

»0.001m U Figure 1 A conceptual sketch of the three sublayers forming the bottom boundary layer. The pressure-gradient, friction, and Coriolis forces are in balance in the Ekman layer while only friction and pressure-gradient forces are significant in the logarithmic and viscous layers. In the logarithmic layer, friction stems predominantly from the Reynolds stress of turbulence, whereas in the viscous layer it comes mainly from molecular effects.


141

142 (a)

TURBULENCE IN THE BENTHIC BOUNDARY LAYER No friction

(b)

−∂P/∂y

Friction −∂P/∂y

U

U

y

the bottom, that is fUg ¼

1 @P ; r @y

fVg ¼

1 @P r @x

½2

and if the density is homogeneous within the boundary layer, the pressure gradient is independent of height within this layer. Substituting [2] into [1] gives the so-called Ekman equation for the boundary layer, namely

fU fU

f ðV Vg Þ ¼

x Figure 2 Plan view of the balance of forces in the geostrophic flow far above the bottom (a) and in the Ekman layer (b). The current, U, is directed to the right in the positive x-direction. Far above the bottom, the pressure gradient in the y-direction is balanced by Coriolis force in the opposite direction and this force is always directed to the right of the current (in the Northern Hemisphere). Within the Ekman layer, friction, t, acts against the current. A balance of forces in ‘both’ the x- and y-directions is only possible if the current backs anticlockwise when viewed from above.

1 @tx 1 @ty ; f ðU Ug Þ ¼ r @z r @z

½3

It is convenient to assume that the bottom stress has no y-component so that the bottom stress t0 ¼ tx ð0Þ is directed entirely in the x-direction, that is, ty ð0Þ ¼ 0. Solving [3] for the velocity profile requires the relationship between stress and velocity, which is a major focus of boundary layer research. Fortunately, the height above the bottom over which friction is important can be determined using only dimensional analysis. For example, the x-component of velocity must be some function, F, of the parameters and variables in [3] and its boundary condition, tx ð0Þ ¼ t0 , so that U ¼ Fðr; t0 ; f ; zÞ

½4

Height

All the four variables in [4] cannot be independent. For example, r and t0 must always appear as a ratio because they are the only ones with the dimension of mass. The root of this ratio, u V U

has dimensions of velocity velocity’. It represents a velocity fluctuations in the other independent variable

Current

Figure 3 A conceptual velocity profile that may result from the effect of friction as depicted in Figure 2. A positive current component, V, is directed to the left of the geostrophic current.

where we have assumed that the vertical velocity, W, is zero (flat bottom), taken the bottom at z ¼ 0, assumed that both components of the stress ðtx ; ty Þ vanish far above the bottom, and assigned the x- and y-components of the geostrophic velocity to Ug and Vg ; respectively. The flow is geostrophic far above

H

rffiffiffiffiffi t0 r

½5

and is called the ‘friction scale for the turbulent boundary layer. The only is

rffiffiffiffiffi 1 t0 u ¼ f f r

½6

and this is the only dimensional group that can be used to nondimensionalize z, the height above the bottom. Thus, the velocity profile must be U Ug =u ¼ Fu ðz=HÞ ðV Vg Þ=u ¼ Fv ðz=HÞ

½7

where Fu and Fv are universal functions. Equation [7] is usually called the ‘velocity defect law’. The order of



magnitude of the height H of the boundary layer, that is, the scale over which friction is important, is usually called the Ekman height. The actual height to which friction is important is within a factor of order unity of H. The Ekman height can also be considered the transition height; for z{H, friction dominates over the Coriolis force while above this level, the reverse holds. An important effect of rotation is that the thickness of the BBL does not grow in the downstream direction (for uniform bottom conditions) whereas the boundary layer over a nonrotating and flat surface grows downstream. Numerical values for the Ekman height can be derived from a traditional formulation of the bottom stress in terms of a drag coefficient, such as t0 ¼ rCD Ug2

½8

where the drag coefficient, CD, must depend on the bottom characteristics, such as roughness. Typical values are CDE0.002. Using a geostrophic flow of UgE0.1 m s 1 commonly found in the open ocean and f ¼ 1 10 4 s 1 gives a friction velocity of u ¼ 4:5 103 m s1 and an Ekman height of H ¼ 45 m which is 100 times smaller than the average ocean depth. The friction layer is thus thin compared to the ocean depth, as assumed. Ekman solved [3] almost a century ago for the special case of a stress proportional to the shear. That is, tx ¼ rKV

@U ; @z

ty ¼ rKV

@V @z

½9

where KV is the eddy viscosity. The mathematically elegant spiral predicted by [3] and [9] is presented in standard textbooks on fluid mechanics. However, the predicted profile is not directly observed due to a number of complicating factors, such as complex boundary geometries, temporal variability in stress acting in the boundary layer, nonconstant eddy viscosity, and nonlinear dynamics. Despite these difficulties, very nice Ekman spirals have been documented when data of sufficient quantity and quality have been carefully analyzed. Trowbridge and Lentz provide an excellent contemporary example of bottom boundary layer observations and analysis (see Further Reading). They show that Ekman balance dynamics are recovered with adequate time averaging. They also show how the traditional Ekman equations presented above must be modified to include important buoyancy effects occurring in a stratified boundary layer. An additional review of interest, focused more on the Ekman spiral extending from the ocean surface boundary layer, is given by Rudnick.

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Viscous Sublayer Very near to a smooth bottom, z{H, a layer forms in which momentum is transferred only by molecular diffusion. In general, the stress tx ¼ r

dU ru0 w0 dz

½10

is the sum of the shear stress due to molecular friction (first term on the right-hand side of [10]) and the Reynolds stress ru0 w0 ; where ¼ m=r is the kinematic molecular viscosity (E1 10 6 m2 s 1). The covariance u0 w0 of horizontal, u0; and vertical, w0; velocity fluctuations leads to a transfer of momentum from the fluid toward the wall. Very near the wall, vertical velocity fluctuations are strongly suppressed (no normal-flow boundary condition) and the Reynolds stress is negligible compared to molecular friction. The Ekman height, H, is not an appropriate parameter for nondimensionalizing the height above the bottom in the thin viscous layer. Rather, the viscous scale is used: d ¼ =u

½11

Using [3] the nondimensionalized momentum balance is @ tx =u2 d V Vg ¼ Hu @ ðz=dÞ @ ty =u2 d U Ug ¼ Hu @ ðz=dÞ

½12

To estimate the magnitude of the terms on the righthand side of [12], we note the following. From [8], the ratio of the geostrophic speed to friction velocity 1=2 is related to the drag coefficient by Ug =u ¼ CD ; and this equals approximately 25. The velocity is at most comparable to the geostrophic velocity, so the factor ðU Ug Þ=u is no more than about 25. Even for very weak flows, the terms in [12] are smaller than O(10 3). Thus, the vertical divergence of the stress is zero and the stress itself is constant. When the stress stems entirely from molecular friction, the only possible velocity profile is a linear one that has a shear which is commensurate with the bottom stress, that is, U zu ¼ zþ ¼ u

½13

Laboratory observations of flow over smooth surfaces show that [13] holds to about z þ E5 and this innermost region is called the ‘viscous sublayer’. A


144


typical dimensional thickness for the viscous sublayer is 5=u E0:001 m. Thus, this layer never extends more than a few millimeters above the bottom. Most of the ocean bottom is not ‘smooth’ compared to this scale.

The Wall Layer Further above the bottom but still well within the extent of the Ekman layer, for =u {z{H ¼ u =f, neither the Ekman height, H, nor the molecular viscosity, n, can be relevant parameters controlling the velocity profile. The only parameter that can nondimensionalize the vertical height is either the thickness of the viscous sublayer or the characteristic height of bottom roughness features, z0. Equation [12] is still the appropriate nondimensional momentum balance if we substitute z0 for d. The lefthand side of [12] is no longer as small as for the viscous sublayer but it is still small compared to unity, and the stress can be taken as constant. Thus, the wall layer and the viscous sublayer are usually called the constant-stress layer. The stress [10], however, is now entirely due to the Reynolds stress. Because the bottom stress has no component in the y-direction, there is also no bottom velocity in this direction. The only parameters that control the velocity profile are the bottom stress and the roughness height. On purely dimensional grounds, we have near the wall: V=u ¼ 0 U=u ¼ g2 ðz=z0 Þ

½14

where g2 is a yet to be determined universal function. Equation [14] is the ‘law of the wall’ for rough bottoms. The law of the wall must be matched to the velocity-defect law [7] and this is usually done by matching the shear rather than the velocity itself. The result is that Vg ¼ Fv ð0Þ ¼ A u U 1 z ¼ ln u k z0 Ug 1 H C ¼ ln u k z0

½15

where k ¼ 0.4 is von Karman’s constant and atmospheric observations indicate that AE12 and CE4. These equations are valid for z/z0c1 and z/H{1 ‘simultaneously’. Thus, the velocity increases logarithmically with increasing height and this profile

ultimately turns into an ‘Ekman’-like spiral that matches the geostrophic flow at z ¼ O(H). A thin viscous sublayer may underlie this profile if the bottom is very smooth, in which case z0 is chosen to match the profile given by [13] for the same bottom stress. It is frequently convenient to express the stress in terms of an eddy viscosity and the shear such as in [9]. However, a constant stress and a logarithmic velocity profile make the eddy viscosity proportional to height, namely K ¼ u kz

½16

Thus, a constant eddy diffusivity is not a good model for the wall layer and may well be inappropriate in much of the Ekman layer. The Reynolds stress in the presence of a shear leads to the production of turbulent kinetic energy (TKE) within the wall layer. It is thought that almost all of the TKE is dissipated locally and that the rate of dissipation is given by e ¼ u0 w0

@U u3 ¼ kz @z

½17

Thus, profiles of the rate of dissipation of kinetic energy provide an alternate measure of the bottom stress to that which can be derived from the velocity profile.

Observations Values of the bottom stress are required for two major purposes: as a boundary condition for flows above the bottom and for the prediction of sediment motions. The near-bottom velocity profile [15] provides a convenient method for estimating the bottom stress through a fitting of U against the logarithm of z. This profile method is the one most frequently used to estimate the bottom stress. Point current meters have been placed within a few meters of the bottom and, under the assumption that they are within the logarithmic region, the bottom stress was estimated from as few as a pair of current meters. Some bottom velocity ‘profile’ measurements were accompanied by concurrent measurements of the turbulent fluctuations of along-flow and vertical velocity components. The covariance of these fluctuations, ru0 w0 ; is an unambiguous measure of the Reynolds stress and, when this stress is extrapolated to the bottom, it usually agrees closely with the stress (ru2 ) inferred from the slope of the logarithmic velocity profile. (Readers are referred to the article by Trowbridge and Lentz in ‘Further Reading’, an excellent source of citations to past observational studies.)



Taking profiles of velocity within the BBL is very difficult. Consequently, there is very little observational evidence on the form of the velocity profile. One of the best deep-ocean velocity profiles was taken in the North Atlantic Western Boundary Current over the Blake Outer Ridge and reached to within 5 m of the bottom (Figure 4). The potential density was homogeneous within 250 m of the bottom and so the pressure gradient was independent of height as assumed in [3]. The current in the upper parts of the homogeneous layer was 0.22 m s 1 and directed along the isobaths (approximately southward). The along-slope current had a very slight maximum at 40 m, decreased sharply below 15 m, and dropped to 0.18 m s 1 at 5 m. The full decay to zero current at the bottom was not resolved for instrumental reasons. The cross-slope current was negligible further than 50 m above the bottom. It increased to 0.025 m s 1 at 5 m and was consistently directed to the left of the along-slope current (approximately eastward). The veering of the velocity vector with height above the bottom was like that depicted in Figures 2 and 3 and reached a maximum of 81 at the lowest observation located at 5 m. Simultaneous measurements of the rate of dissipation of TKE indicate that the turbulence was negligible for heights greater than 50 m above the bottom. The dissipation rate decreased monotonically with increasing height up to 50 m. Above this height, it was small and fairly uniform. Thus, the frictional layer was 50-m thick and 5 times thinner than the homogeneous layer. It is common to find different heights for the homogeneous (‘mixed’) and the turbulent (‘mixing’) layers. The height of the Ekman

layer, H, predicted by [6] was 120 m and the actual height to which friction was important was close to the expected value of kH ¼ 50 m, where k ¼ 0.41 is the von Karman constant. The height of the logarithmic layer [15] has not been extensively surveyed and based on the scaling arguments it must be small compared to the Ekman height. Measurements in a tidal channel indicate that profiles depart from a logarithmic form at about 3–4% of the Ekman height. The height of the constant stress layer cannot be greater than the logarithmic profile height. For horizontally homogeneous bottom roughness, such as flat sand and fine gravel, the roughness height, z0, is c. 30 times smaller than the actual roughness. The notion is that the velocity profile reaches zero somewhere below the highest bottom features. Thus, there must be considerable local variations of the velocity profile for heights less than zE30z0 and [15] represents a horizontally averaged velocity profile. The constancy of z0 is not well established for any particular site nor does it increase consistently with increasing bottom roughness. Cheng et al. found a systematic decrease in z0 with increasing speed above 0.2 m s1 and attributed this to the onset of sediment motions and its smoothing effect upon the bottom. The bottom roughness is seldom horizontally homogeneous and the major contribution to roughness comes from bedforms (e.g., ripples and sand waves) and other features with horizontal scales far greater than the largest pieces of bottom material. Thus, bottom profiles well above zE30z0 should show horizontal variations (Figure 5). For example, a

U

60

z (m)

145

U

V

40

20

0

0

0.1

0.2 −1

(ms )

Figure 4 A sketch of the along- , U, and across-isobath, V, flow over the Blake Outer Ridge in the North Atlantic Western Boundary Current as reported by Stahr and Sanford (1999). Dashed lines within 5 m of the bottom are hypothetical extensions.

Figure 5 Conceptual sketch of spatial variations in the vertical profile of velocity over bedforms with long horizontal scales, such as sand waves. The vertical and dashed lines give a zero-velocity reference. The flow accelerates and stream-lines compress on the ‘upwind’ side of crests and the flow decelerates and its streamlines dilate on the lee side. This causes a pressure drop in the flow direction. If slopes are steep, flow separation and back flow may occur in the troughs and over the leesides as depicted for the right profile.


146


wavy bottom may appear locally to have a roughness scale commensurate with the bottom material (such as sand) but at a height comparable to the amplitude of sand waves, the bottom turns ‘rough’ as the turbulent eddies respond to the larger horizontal-scale structures on the bottom and not just the local features. Additional drag will be exerted on the flow by the pressure differences across sand waves (or other obstacles) due to stream-line asymmetry and outright flow separation when the slope on the lee side of objects is very steep. This is usually labeled ‘form drag’ due to its similarity to the drag on bluff bodies. This feature was first observed by Chriss and Caldwell in 1982 in profiles taken over the continental shelf off Oregon. They found two logarithmic layers with differing slopes (Figure 6a). The lower layer extended to 0.1 m, and its logarithmic slope implies a friction velocity of u ¼ 0:004 m s1 : This layer appears to be associated with skin friction over a fairly smooth surface. The upper layer reached to at least 0.2 m and its much greater slope is indicative of stress due to form drag. Form drag can also result when boundary layer turbulence is produced by wave-like variations in the seabed. Sanford and Lien

report on measurements from a tidal channel, where sand ripples of 0.3-m amplitude and 16-m wavelength were present and oriented span-wise to the flow direction. They find a double logarithmic velocity profile (Figure 6b) similar to that observed by Chriss and Caldwell in 1982. The slope of the velocity–log z relation increased by a factor of 2 near z ¼ 4 m, even though the seabed amplitude variations were much smaller than this height. The effect of long horizontal-scale features on the flow over the bottom is still being investigated. An alternate method of estimating the bottom stress is provided by the dissipation profile technique. Profiles of the rate of dissipation have verified the inverse height dependence predicted by [17] for heights of up to 10 m. However, when the estimates of bottom stress derived from dissipation profiles are compared to the stress estimated from a fit of the velocity profile to a logarithmic form, the dissipation-based estimates are typically 3 times smaller. Momentum budgets for bottom streams such as the Mediterranean outflow are consistent with the drag determined from the velocity profile but not with the drag inferred from dissipation profiles. There is still Tidal channel

Oregon shelf s −1

20 m

m

5

1

3

2

2

u * = 0.0 24 m s−

3

z (m)

5

43

7

u * =0 .0

7

m s −1

10

u = * 0.00 4

z (cm)

u * =

0.

01

10

s −1

15

1

1 (a)

0.08

0.1 U (m s −1)

0.12 (b)

0.5

0.6 U (m s −1)

0.7

Figure 6 A sketch of velocity profiles plotted against the logarithm of height above the bottom based on data reported by Chriss and Caldwell (1982) and Sanford and Lien (1999). Approximately 10 data points were available for each regression (a) whereas data from about 100 different depths were used for (b). Both profiles imply a jump by a factor of 2 in friction velocity and an increase by a factor of 4 in stress for the upper logarithmic layer compared to the lower layer. The grey-shaded line is the law-of-the-wall scaling modified by proximity to the free surface.



no satisfactory explanation for such discrepancies. Recent observations on the Oregon shelf include those of Nash and Moum, who document the hydraulic production of bottom boundary layer turbulence at a topographic bump in the presence of a coastal jet. Their measurements appear to be dominated by a form drag stress-layer, with u C0:00520:01 m s1 estimated using the dissipation rate method. They find that the resulting form drag can be of sufficient magnitude to break the geostrophic balance of flow near the bump. Nash and Moum also document that the boundary layer properties can change significantly due variations in forcing and stratification over both short (O(1 day)) and long (O(1 year)) timescales.

Modifications due to Stratification and Proximity of the Free Surface In the absence of stratification and a close upper boundary, turbulence length scales increase linearly from z ¼ 0. However, turbulence scales are attenuated by stratification and by boundaries. Most commonly, unstratified near-bottom layers are capped by stratified layers. In these cases, the length scales of the turbulence cannot increase unbounded and are attenuated throughout the boundary layer (Perlin et al., 2005). In shallow tidal channels (such as is shown in Figure 6b), a similar effect ensues. This offers an alternate explanation to the two logarithmic layer model, in which a single velocity scale (u ) describe the full velocity profile (shown in grey in Figure 6b).

See also Benthic Boundary Layer Effects. Ekman Transport and Pumping. Fluid Dynamics, Introduction, and Laboratory Experiments. Grabs for Shelf Benthic Sampling. Non-Rotating Gravity Currents. Overflows and Cascades. Rotating Gravity Currents. Sub-sea Permafrost. Turbulence Sensors. UnderIce Boundary Layer.

147

Further Reading Cheng RT, Ling C-H, and Gartner JW (1999) Estimates of bottom roughness length and bottom shear stress in South San Francisco Bay, California. Journal of Geophysical Research 104: 7715--7728. Chriss TM and Caldwell DR (1982) Evidence for the influence of form drag on bottom boundary layer flow. Journal of Geophysical Research 87: 4148--4154. Dewey RK and Crawford WR (1988) Bottom stress estimates from vertical dissipation rate profiles on the continental shelf. Journal of Physical Oceanography 18: 1167--1177. Johnson GC, Lueck RG, and Sanford TB (1995) Stress on the Mediterranean outflow plume. Part 2: Turbulent dissipation and shear measurements. Journal of Physical Oceanography 24: 2072--2083. Lueck RG and Huang D (1999) Dissipation measurement with a moored instrument in a swift tidal channel. Journal of Atmospheric and Oceanic Technology 16: 1499--1505. Nash JD and Moum JN (2001) Internal hydraulic flows on the continental shelf: High drag states over a small bank. Journal of Geophysical Research 106: 4593--4612. Perlin A, Moum JN, Klymak JM, Levine MD, Boyd T, and Kosro PM (2005) A modified law-of-the-wall applied to oceanic bottom boundary layers. J. Geophysics Research 110: doi:10.1029/2004JC002310. Rudnick D (2003) Observations of momentum transfer in the upper ocean: Did Ekman get it right? In: Muller P and Garrett C (eds.) Proceedings of the ‘Aha Huliko’a Hawaiian Winter Workshop, pp. 163--170. Honolulu, HI: University of Hawaii. Sanford TB and Lien R-C (1999) Turbulent properties in a homogeneous tidal bottom boundary layer. Journal of Geophysical Research 104: 1245--1257. Stahr FR and Sanford TB (1999) Transport and bottom boundary layer observations of the North Atlantic deep western boundary current at the Blake outer ridge. Deep-Sea Research 46: 205--243. Trowbridge JH and Lentz SJ (1998) Dynamics of the bottom boundary layer on the North California Shelf. Journal of Physical Oceanography 28: 2075--2093.


TURBULENCE SENSORS N. S. Oakey, Bedford Institute of Oceanography, Dartmouth, NS, Canada Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3063–3069, & 2001, Elsevier Ltd.

Introduction This article describes sensors and techniques used to measure turbulent kinetic energy dissipation in the ocean. Dissipation may be thought of simply as the rate at which turbulent mechanical energy is converted into heat by viscous friction at small scales. This is a complicated indirect measurement requiring mathematical models to allow us to envisage and understand turbulent fields. It will require using this theory to understand how sensors might be developed using basic principles of physics to measure properties of a turbulent field to centimeter scales. Instruments must be used to carry these sensors into the ocean so that the researcher can measure its turbulent characteristics in space and time. It is also this sensor–instrument combination that converts the sensor output into a quantity, normally a voltage varying in time, that is used by the experimenter to calculate turbulent intensity. Thus, both the characteristics of sensors and the way in which the sensor– instrument combination samples the environment must be understood and will be discussed below.

Understanding Turbulence in the Ocean There is no universally accepted definition of turbulence. Suppose that one stirs a bowl of clear water and injects some colored dye into it. One sees that filaments of dye become stretched, twisted and contorted into smaller and smaller eddies and eventually the bowl becomes a uniform color. This experiment leads to one definition of turbulence. It includes the concept that eddies in the water are distributed randomly everywhere in space and time, that energy is transferred from larger to smaller eddies, and that over time the mean separation of the dyed particles increases. In contrast, the ocean is typically stratified through a density that is determined by the temperature and salt in the water as well as the pressure. In this environment, a vertical shear in the velocity in the water column can be large enough to overcome the stability. Energy from the mean flow is converted

148

into large-scale eddies determined by flow boundary conditions that characterize turbulent kinetic energy at its maximum scales. Further vortex stretching creates smaller and smaller eddies resulting in a turbulent cascade of energy (velocity fluctuations) to smaller scales until viscous forces begin to dominate where the energy is eventually dissipated as heat. This article focuses on sensors to measure this dissipation process directly by measuring the effect of viscosity on the turbulent cascade. The irregular and aperiodic velocity fluctuations in space and time characteristic of turbulence, accompanied by energy transfer between scales and associated fluid mixing, may be described mathematically through nonlinear terms in the Navier–Stokes equation. Nevertheless, it is difficult to solve numerically in oceanographic applications. At the dissipation scales, typically a few meters and smaller, we normally assume that the turbulent field is homogenous and that it has definable statistical averages in all parts of the field. We further assume that direction is unimportant (isotropy) and statistical distributions depend only on separation distances between points. With the turbulence controlled only by internal parameters, we assume the nature of the nonlinear cascade of energy from large to small scales generates a universal velocity spectrum. An example of this spectrum is shown schematically in Figure 1A. At low wavenumbers, k, no energy is taken out by viscous dissipation, so the energy flux, e, across each wave number, or down the cascade, is constant. Through dimensional arguments, the three-dimensional turbulent energy spectrum, EðkÞ in this region (called the inertial subrange) as a function of wavenumber k is given by EðkÞ ¼ ae2=3 k5=3

½1

where a is a constant determined experimentally to be approximately 1.5. In practice the three-dimensional spectrum given in eqn [1] cannot easily be measured and one must use the one-dimensional analogy where k is replaced by a component ki . At higher wavenumbers or smaller scales the velocity gradient spectrum (obtained by multiplying the spectrum in eqn [1] by the square of the wave number k2 ) shows more clearly where dissipation occurs. Figure 1B shows the spectra of velocity shear for velocity fluctuations for one component of k for values of e most typically found in the ocean. In this case, the spectra of fluctuations transverse to the


TURBULENCE SENSORS

149

The factor 2p gives a length scale from the radian wave number. This is an important scale for the design of instruments and sensors because it defines the smallest diameter eddies that must be measured. The dissipation e is given by integrating the spectrum shown in Figure 1B.

e ¼ 15v

N ð

k21 E1 ðk1 Þdk1

0

¼ 7:5v

N ð

k21 E2 ðk1 Þdk1

½3

0

E1 ðk1 Þ is the one-dimensional wavenumber spectrum of longitudinal velocity, and E2 ðk1 Þ is the onedimensional spectrum of transverse velocity and one assumes isotropy to estimate the factors 15 and 7.5, respectively. In practice, the upper integration limit may be replaced with the viscous cutoff scale. For the transverse turbulent velocity u, the shear variance in the z direction, ðdu=dzÞ2 is equivalent to the integral of equation [3] and e is given by 15 du 2 e¼ v 2 dz

Figure 1 (A) The universal, velocity spectra for dissipation rates that typically occur in the ocean. Power density in velocity is plotted as a function of wavenumber. The shape of the spectrum remains the same but, as the energy in the turbulent field increases, the spectrum moves to higher wavenumbers and to higher intensities. (B) The equivalent universal, velocity shear spectra. (A) and (B) both show the inertial subrange and dissipation region, but in (B), the dissipation portion is more strongly emphasized.

measurement direction are shown but the picture for along-axis fluctuations would look almost identical. At the highest wavenumbers (smallest scales), viscous dissipation reduces the energy per unit wavenumber to zero. At small scale, it is assumed that turbulent motion is determined only by kinematic viscosity, v( ¼ 1.3 106 m2 s1 at 101C), and the rate, e, at which energy passed down from larger eddies, must be dissipated. By dimensional arguments the length scale at which viscous forces equal inertial forces, and viscosity dissipates the turbulent energy as heat, is given by viscous cutoff scale 1=4 ½2 Lv ¼ 2p v3 =e

½4

These assumptions are important to the way in which sensors are designed. A common way to observe turbulent fields is by making measurements of velocity and other mixing quantities along a trajectory through a turbulent field assuming that it is frozen in space and time. Measurement along a line, recorded as a time-series (Figure 2), is interpreted as spatial variability by assuming stationarity and using the known sensor velocity to convert into distance. Standard Fourier transform techniques allow one to generate spectra similar to those in Figure 1 from which dissipation, e, may be estimated. If there is a temperature gradient in the water column when turbulence is generated, the velocity field strains the temperature field, creating strongly interleaved temperature filaments over the vertical range of the overturn. The temperature microstructure intensity depends not only on the mean gradient but also on the energy in the turbulent field, in particular dissipation, e. Temperature fluctuations recorded as a time-series (Figure 2) can be represented by spectra similar to those shown in Figure 1. As with the velocity fluctuations, there is a subrange where diffusive and viscous effects are unimportant where temperature fluctuations are transferred towards higher wave numbers. Temperature spectra persist to length scales smaller than the viscous cutoff scale. In this range, not only kinematic viscosity, v, and dissipation, e, are important but also, thermal diffusion, kT (E1.4 107 ms s1). The cutoff


150

TURBULENCE SENSORS 0

layers on continental shelves. (To put these numbers into a simple perspective, energy dissipated in the ocean may range from the almost insignificant rate of 100 W km3 to the very large rate of 100 MW km3.) Present sensors and instruments are capable of measuring over this range of dissipation. Regions of higher dissipation such as river outflows and tidal channels are not normally measurable with sensors and instruments described here.

10

uz

Depth (m)

20

Tz

30

Measuring Dissipation in the Ocean

40

50

60

70

8

10

12 14 16 Temperature (°C)

18

20

Figure 2 A representative vertical profile of temperature is shown from the surface to bottom obtained with a vertical falling instrument. In panels at the right are shown expanded portions of the velocity shear (uz ) and the gradients in temperature (Tz ). The panels represent small sections of the vertical record that are treated as time-series to calculate spectra similar to those in Figure 1. The upper panel of Uz at mid-depth is a region of low dissipation and the one below represents higher dissipation.

wavelength for temperature fluctuations is given by 1=4 LT ¼ 2p vk2T =e

½5

Under restricted circumstances, the temperature gradients or temperature microstructure can be measured in the ocean to this scale. Under these circumstances one can determine LT and hence estimate dissipation e. The sensors used most commonly in oceanography to measure dissipation make use of the above ideas. Velocity fluctuations may be used to determine dissipation e directly using eqns [3] or [4]. Measuring temperature fluctuations allows dissipation to be calculated indirectly from eqn [5]. The units used to express dissipation in the ocean are W kg1 (watts of mechanical energy converted into heat per kilogram of sea water). Typical values range from 1010 W kg1 in the deep ocean to 104 W kg1 in active boundary

The most common technique of estimating dissipation in the ocean involves measuring small-scale velocity and temperature fluctuations. This may be accomplished by dropping a profiler vertically, towing one horizontally or setting it at a fixed position and measuring the fluctuations in velocity and temperature as the water moves past the sensors. This allows a time-series of turbulent velocity fluctuations to be recorded. A typical platform used to measure dissipation in the ocean is a vertical profiler that falls typically at a speed of 0.5–1.0 ms1. There have been many such instruments built and each one typically carries a number of sensors to measure some components of the turbulent velocity as well as temperature microstructure. A sample time-series for a vertical profiler is shown in Figure 2. Assuming that the turbulent field is isotropic, homogeneous and stationary one can use the mean flow velocity to determine the wavenumber scale and calculate the one-dimensional turbulence spectrum, E1 ðk1 Þ or E2 ðk1 Þ, as defined above and from this determine the dissipation, e, using eqns [3] and [4]. As the turbulent dissipation gets larger, the wavenumber at cut-off gets larger. Alternatively, Lv and LT become smaller as shown in Figure 3(A). As one tries to measure higher dissipation one must have a sensor with better spatial resolution and higher frequency response. We convert from wavenumber, k (cycles m1), to frequency using the relationship f ¼ kV (Hz) where V (m s1) is the flow speed past the sensor. The cutoff frequencies corresponding to Lv and LT are given by fci ¼ V=Li. In practice, one does not have to measure the microstructure variance to the cutoff frequency because of the universal characteristic of the dissipation curves. A usual compromise is to consider that if 90% of the dissipation curve is measured then a satisfactory measure of dissipation can be achieved. This is summarized in Figure 3A which shows the sampling frequency that must be achieved to resolve a particular dissipation. (It must be remembered that to resolve the energy at any frequency one must sample at least twice that frequency.)


LT and LV (cm)

TURBULENCE SENSORS

Sampling freq. (Hz)

(A)

(B)

10 5

LV LT

0 _ 10 10

_8

10

_6

_4

10

10

2000

LT

1000

LV 0 _ 10 10

_8

_6

10 10 _ Dissipation (W kg 1)

_4

10

Figure 3 (A) The decrease in the cutoff scale with increasing dissipation for viscous dissipation, Lv (eqn [2]) and thermal dissipation, LT (eqn [5]). (B) The sampling frequency that is required for a particular e for viscous dissipation, Lv and for thermal cutoff, LT. The upper and lower boundaries of the shaded bands correspond to measurement at flow speeds of 1.0 and 0.5 m s1, respectively.

Turbulence Dissipation Sensors Airfoil Probes

One of the most commonly used turbulence sensors to measure turbulent velocity fluctuations is called an airfoil probe. This sensor is an axially symmetrical airfoil made of flexible rubber surrounding a sensitive piezoelectric crystal. The sensitive tip of the probe approximates a parabola of revolution, several millimeters in diameter and about 1 cm long. The crystal generates a voltage proportional to the magnitude of a force applied perpendicular to its axis. The crystal is rigid in one transverse direction so responds to a cross force only in one direction. Thus, two sensors are required to measure the two transverse components of turbulent velocity fluctuations. The sensor is placed on the leading end of an instrument that is moving relative to the water at a mean speed V. In a mean flow along the axis of the shear probe, no lift will be generated and no force applied to the crystal. If there is an off-axis turbulent velocity, a lift will be generated which will apply a force to the piezoelectric crystal through the flexible rubber tip. Thus, the sensor will provide a voltage that is linearly proportional to the turbulent velocity. The effective resolution of the sensor is of order 1 cm, the smallest scale of turbulence that can be effectively measured by this sensor. From Figure 3, it can be seen that for values above 105 W kg1 this type of sensor will begin to underestimate dissipation. Normally the signal from the sensor is

151

differentiated to emphasize the high frequency part of the turbulence spectrum. This gives the velocity shear, and analysis of this signal allows direct generation of spectra similar to the theoretical ones shown in Figure 1B. For this reason, airfoil probes are often called shear probes. These sensors measure the component of turbulence perpendicular to the drop direction of the instrument. As such, it is eqn [4]. Which is most relevant to calculating dissipation, e. Figure 3B, shows that to measure dissipation to 105 W kg1 in a flow speed of 1 m s1 (along the axis of the sensor) the output must be sampled to at least as rapidly as 200 Hz. Of the many instruments that use this sensor to measure dissipation, the most common are vertical profilers. Those used near the surface are often called tethered free-fall profilers because they have a light, loose line attached to the instrument for quick recovery and redeployment. The line is usually a data link to the ship where data are recorded on computers for analysis. Because of the intermittent nature of turbulence, it is important to have many profiles (or independent samples) in measuring dissipation to be able to obtain a statistically robust average value. For deeper measurements of dissipation, free-fall profilers are used that have no tether line. They are deployed to a predetermined depth in the ocean where their buoyancy is changed to allow them to return to the surface. These instruments record internally and can be inherently quieter than tethered free-fall instruments but are slower to recover and redeploy. In practice, both types of profilers can measure dissipation as low as 1010 W kg1. In shallow regions of high dissipation such as the bottom boundary layer of tidally generated flow over banks, in bottom river channels or in active regions such as the Mediterranean outflow tethered free-fall instruments have been most successful. Where the dissipation exceeds 105 W kg1, these profilers and the shear probe sensor give limited results. The airfoil probe has also been used successfully to obtain dissipation measurements horizontally. It has been used as a sensor on a towed fish pulled horizontally at speeds of order 1 m s1. The results look similar to those in Figure 2 where the depth axis is replaced by a horizontal axis and similar techniques to those described above are used to extract dissipation. Because of towline vibration, a towed instrument is generally noisier than a free-fall profiler. If the vibration noise of the platform is transferred to the airfoil sensor, it will generate velocity signals relative to the sensor indistinguishable from turbulence in the water with the sensor not vibrating. Generally, a measurable dissipation lower limit for these instruments of 109 W kg1 would be


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TURBULENCE SENSORS

considered good. These shear probes have also been mounted on submarines for horizontal measurements. Nevertheless, this platform has had only limited use because of vehicle noise and expensive operating costs. More recently, unmanned submarines called autonomous underwater vehicles have been used as suitable platforms for turbulent kinetic energy measurements. They are expected to have similar noise characteristics to towed instruments. Another interesting way of obtaining horizontal measurements is to place shear probes on a moored instrument. The turbulence in the water is measured as it flows past the sensor at a speed V m s1. In this case, the water velocity must typically be faster than 0.1 m s1 for the measurements to be within the sensor capabilities and mooring vibrations generate similar problems to towed instruments. Thin Film Sensors

One of the original sensors used to measure turbulence and dissipation is called a hot film sensor. In these sensors, a platinum or nickel film is deposited on the surface near the conical tip of a glass rod of order 1 mm diameter and covered with a thin film of quartz to insulate it from the water. The film is heated to several degrees centigrade above the ambient temperature and special electronics are used to maintain a constant thin film temperature. Water flowing across the probe cools the platinum. Fluctuations in the current, required to keep the sensor at a constant temperature, are a measure of the turbulent velocity fluctuations along the axis of the sensor. This sensor measures the E1 ðk1 Þ component of the turbulent field as opposed to the E2 ðk1 Þ component measured by the shear probe. Therefore, the first part of the eqn [3] is relevant to estimating dissipation. The primary advantage of this sensor over the shear probe is that it has much smaller spatial resolution and a much higher frequency response. As one can see from Figure 3, this allows one to measure to higher dissipation rates. The disadvantages of this probe are that the electronics to run it are much more complicated than for shear probes and the sensors are more difficult to fabricate and quite expensive. They also require a lot of power to heat since they are very low in resistance (of order 5–10 O). Because the quartz insulation must be extremely thin to provide good heat transfer, thin films are also very fragile and easily damaged by impact with particles in the water. These probes do not provide an output voltage that is linear with turbulent velocity fluctuations. They also tend to be noisy and subject to fouling. They are seldom used today in ocean measurements.

Pitot Tubes

Another recently developed sensor used to measure dissipation makes use of a Pitot tube. If a Pitot tube is placed in water flowing at a speed W along its axis, the pressure generated by the flow is proportional to W 2 . This technique has been applied to turbulence measurements by carefully designing an axisymmetric port a few millimeters in diameter on the tip of a sensor of order 1 cm in diameter. By connecting the port to a very sensitive differential pressure sensor, fluctuations in pressure along the axis of the probe can be measured. Using suitable electronic circuits, a signal is produced that is linearly proportional to along-axis fluctuations in turbulent velocity. In this sense, it is similar to the heated-film sensor and different from the shear probe which measures fluctuations perpendicular to the mean flow. This sensor has been used in conjunction with a pair of shear probes to simultaneously measure all three components of turbulent velocity fluctuations. Temperature Microstructure Sensors

As outlined above, if there is turbulent mixing occurring in a region where there is a temperature gradient, the turbulent velocity will cause the temperature to be mixed. If, for example, warmer fluid overlays colder fluid, turbulence will move parcels of warm fluid down and cold fluid up. A temperature sensor that traverses a patch of fluid such as this will measure fluctuations in temperature as shown in Figure 2. The spectrum of these fluctuations can be used to determine the dissipation using eqn [5]. Because the molecular diffusivity of heat for water is much smaller than the molecular viscosity, the scale at which temperature fluctuations cease is about a factor of three smaller than the scale at which velocity fluctuations cease. This is shown clearly in Figure 3A that compares LT and Lv . These facts place a severe restriction on the speed and size of a temperature sensor compared to a shear probe, or alternatively limits the speed that an instrument may fall. For the same fall speed, a temperature sensor must be sampled at a much higher rate than a shear probe. The simplest temperature sensor with the precision and noise level to measure temperature microstructure in the ocean is the thermistor. The smallest thermistors that are used in sea water are a fraction of millimeter in diameter and have a frequency response of order 10 ms. At a flow speed of 1 m s1, one is able to delimit the spectrum of temperature for dissipations up to about 107 W kg1. Some success has been obtained by using very slow moving profilers that fall or rise at about 0.1 m s1. An alternative to the thermistor is a thin film


TURBULENCE SENSORS

153

thermometer. It is similar to the hot film velocity sensor described above and is constructed identically. Used as a thermometer, the change in the resistance of this sensor is a measure of change of temperature. Thin film sensors are faster than thermistors, typically with a time-constant of 2 ms which means that for any sensor velocity the temperature fluctuations may be measured to a higher wave number. These sensors are nevertheless at least an order of magnitude noisier than thermistors, which means that they are suitable for measuring microstructure only in regions where there are strong mean gradients. Using thermometry to measure dissipation is subject to large errors because, as indicated in eqn [5], dissipation is proportional to (LT Þ4 , and this requires accuracy in determining LT that is seldom achieved. Some success has been made using sensors that measure conductivity as a proxy for temperature. These sensors make use of the fact that the conductivity of sea water is determined by both salt and temperature and in most cases, the temperature causes most of the fluctuations. The techniques used are similar to those described above for temperature. Some of the sensors are smaller and faster than thermistors and less noisy than thin film thermometers. They are still limited to the same constraints as thermometers in that they must fully resolve the spectrum in order to estimate LT and utilize eqn [5].

allow water velocity fluctuations to be inferred. This configuration of sensors is generally mounted as a fixed array on a platform on the bottom and has been used to measure turbulent mixing in many places on continental shelves. This technique has the advantage over profiling dissipation sensors of measuring three components of velocity fluctuations over long periods of time at a single place. Dissipation is estimated from the k5=3 wavenumber range.

Acoustic Current Meters

E(k) E1(k1)

Acoustic techniques have also been used to measure water velocity in the ocean and indirectly to infer dissipation rates. One such technique utilizes an acoustic Doppler current meter optimized to measure vertical velocity fluctuations in the water column. In these instruments, a sound pulse is transmitted into the water and the sound scattered back to a sound receiver. The back-scattered pulse contains information about the water velocity because of the Doppler shift in the sound frequency. This technique is unable to measure to dissipation scales but instead, measures vertical velocities in the k5=3 wavenumber range defined by eqn [1]. By suitably defining a turbulent timescale, dissipation is estimated from the intensity in the fluctuations in the vertical velocity. This technique is very useful in studying turbulence in regions of intense mixing such as tidally driven flows. In another technique, an array of small acoustic transmitters and receivers is configured such that the transit time of a pulse of sound can be measured over a short distance of around 10–20 cm. Velocity fluctuations in the water change the transit time and

Conclusions The measurement of mixing rates in the ocean is important to our understanding of the distributions of temperature, salinity, and nutrients in the ocean. We need to understand this to include them correctly in climate and biological ocean models. The way in which energy is converted from sources at large scale and dissipated at small scales has required the development of a variety of ocean sensors. Some of these are described briefly above. It is hoped that enough of the key words and ideas have been put forward for the reader to understand some of the principles involved in turbulence measurement and at least some of the sensors and techniques used.

Symbols used a

E2(k1)

e f KT Lv LT u v V W z

An experimentally determined spectral constant Energy spectral density One-dimensional energy wavenumber spectrum – fluctuations along the axis of measurement One-dimensional energy wavenumber spectrum – fluctuations perpendicular to the axis of measurement Dissipation of turbulent kinetic energy measurement or sampling frequency molecular diffusivity of heat viscous cutoff scale temperature cutoff scale horizontal velocity fluctuation kinematic viscosity flow velocity along axis of sensor drop velocity distance coordinate (normally vertical)

See also Dispersion and Diffusion in the Deep Ocean. Fossil Turbulence. Internal Tidal Mixing. Intrusions. Island Wakes. Langmuir Circulation and Instability. Meddies and Sub-Surface Eddies. Mesoscale


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Eddies. Three-Dimensional (3D) Turbulence. Topographic Eddies Under-Ice Boundary Layer.

Further Reading Bradshaw P (1971) An Introduction to Turbulence and Its Measurement. Oxford, New York, Toronto, Sydney, Paris, Braunschweig: Pergamon Press. Dobson F, Hasse L, and Davis R (1980) Air–Sea Interaction Instruments and Methods. New York and London: Plenum Press. Frost W and Moulden TH (1977) Handbook of Turbulence, vol. 1: Fundamentals and Applications. New York: Plenum Press

Hinze JO (1959) Turbulence. New York, Toronto, London: McGraw-Hill. Journal of Atmospheric and Oceanic Technology (1999) 16(11), Special Issue on Microstructure Sensors. Neumann G and Pierson WJ (1966) Principles of Physical Oceanography. Englewood Cliffs, NJ: Prentice Hall. Patterson GK and Zakin JL (1973) Turbulence in liquids. Proceedings of the Third Symposium, 414pp., Department of Chemical Engineering, University of MissouriRolla. Summerhayes CP and Thorpe SA (1996) Oceanography, pp. 280--299. New York: John Wiley.


UNDER-ICE BOUNDARY LAYER M. G. McPhee, McPhee Research Company, Naches, WA, USA J. H. Morison, University of Washington, Seattle, WA, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3071–3078, & 2001, Elsevier Ltd.

Introduction Sea ice is in almost constant motion in response to wind, ocean currents, and forces transmitted within the ice cover itself, thus there is nearly always a zone of sheared flow between the ice and underlying, undisturbed ocean where turbulence transports momentum, heat, salt, and other contaminants vertically. The zone in which these turbulent fluxes occur, which can span from a few to hundreds of meters, is the under-ice boundary layer (UBL). This article describes general characteristics of the UBL, with emphasis on the physics of vertical turbulent transfer, specifically turbulent mixing length and eddy diffusivity. Extensive measurements of turbulence in the UBL, not available elsewhere, have not only made these ideas concrete, but have also provided quantitative guidance on how external forcing controls the efficiency of vertical exchange. Here we stress features that the UBL has in common with ocean boundary layers everywhere. The article on ice–ocean interaction emphasizes unique aspects of the interaction between sea ice and the ocean (see Ice–ocean interaction). While largely responsible for the relative paucity of oceanographic data from polar regions, sea ice also serves as an exceptionally stable platform, often moving with the maximum velocity in the water column. In effect, it provides a rotating geophysical laboratory with unique opportunities for directly measuring turbulent fluxes of momentum, heat, and salt at multiple levels in the oceanic boundary layer – measurements that are extremely difficult in the open ocean. Examples of important oceanographic boundary-layer processes first observed from sea ice include: (1) the Ekman spiral of velocity with depth; (2) Reynolds stress through the entire boundary layer, and its associated spiral with depth; (3) direct measurements of turbulent heat flux and salinity flux; (4) direct measurements of eddy viscosity and diffusivity in the ocean boundary layer; (5) the impact of surface buoyancy, both negative and positive,

on boundary layer turbulence, and (6) internal wave drag (‘dead water’) as an important factor in the surface momentum and energy budgets. The UBL differs from temperate open ocean boundary layers by the absence of strong diurnal forcing and of high frequency, wind-driven surface waves. It thus lacks the near surface zone of intense turbulence and dissipation associated with wave breaking, and organized Langmuir circulation due to the nonlinear interaction between waves and currents (e.g., the interaction of Stokes drift with near surface vorticity) (see Langmuir Circulation and Instability). On the other hand, quasi-organized roll structures associated with sheared convective cells have been observed under freezing ice, and are apparently a ubiquitous feature of freezing leads and polynyas. Large inertial-period oscillations in UBL horizontal velocity are observed routinely, especially in summer when the ice pack is relaxed. The annual cycle of buoyancy flux from freezing and melting mimics in some respects the diurnal cycle of heating and cooling, as well as the annual evolution of temperate ocean boundary layers. The range of surface forcing, with observations of surface stress ranging up to 1 Pa, and buoyancy flux magnitudes as high as 106W kg1, is comparable to that encountered in open oceans. All these factors suggest that similarities between the UBL and the open ocean boundary layer far outweigh the differences.

History and Basic Concepts Rotational Physics and the Ekman Layer

From 1893 to 1896, the Norwegian research vessel Fram drifted with the Arctic pack ice north of Eurasia in one of the most productive oceanographic cruises ever conducted. Among other important discoveries was the observation by Fridtjof Nansen, the great scientist–explorer–statesman, that the drift was consistently to the right of the surface wind. Nansen surmised that this effect arose from the differential acceleration in a rotating reference frame (the earth) on the sheared turbulent flow beneath the ice, and interested the young Swedish scientist, V.W. Ekman, in the problem. Ekman discovered an elegantly simple solution to the coupled differential equations describing the steady-state boundary layer, which exhibited attenuated circular rotation with depth (spirals) in both velocity and stress (momentum flux). The solution includes a constant phase difference between velocity and stress, resulting in a


155

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UNDER-ICE BOUNDARY LAYER

451 clockwise deflection of surface velocity with respect to surface stress in the Northern Hemisphere, roughly comparable to the 20–401 deflection Nansen observed. In his classic 1905 paper, Ekman extended his findings with remarkable insight to predict inertial oscillations, large circular currents superimposed on the mean current, and even derived credible estimates of eddy viscosity in the ocean from surfacedrift-to-wind-speed ratios. Ekman postulated the eddy viscosity should vary as the square of the surface wind speed, with kinematic values of order 0.04 m2 s1 for typical wind speeds of 10 m s1. After nearly a century, it is tempting to dismiss Ekman’s solution as not adequately accounting for vertical variation of eddy viscosity in the boundary layer. Surface (ice) velocity, for example, is strongly influenced by a zone of intense shear near the ice– ocean interface where eddy viscosity varies linearly with distance from the ice. For typical under-ice conditions, this approximately halves the angle between interfacial stress and velocity and significantly increases the ratio of surface speed to surface stress. The Ekman approach also ignores potentially important effects from density gradients in the water column, or from buoyancy flux at the interface. Nevertheless, measurements from the UBL show that with slight modification, Ekman theory does indeed provide a very useful first-order description of turbulent stress in the UBL. Turbulent stress is not much affected by either variation in eddy viscosity in the near surface layer (across which the stress magnitude varies by only about 10%), or by horizontal gradients in density of the boundary layer (‘thermal wind’). Both can have large impact on the mean velocity profile. The Ekman solution for turbulent stress is derived as follows. Using modern notation, the equations of motion in a noninertial reference frame rotating with the earth include an apparent acceleration resulting in the Coriolis force, with horizontal vector component rfk V, where r is density, V is the horizontal velocity vector, k is the vertical unit vector, and f is the Coriolis parameter (positive in the Northern Hemisphere). Ekman postulated that eddy viscosity, K, which behaves similarly to molecular viscosity but is several orders of magnitude larger, relates stress to velocity shear: tˆ ¼ KqV=qz where tˆ is a traction vector combining the horizontal components of stress in the water. Expressing horizontal vectors as complex numbers, e.g., V ¼ u þ iv, the steady-state, horizontally homogeneous equation for horizontal velocity in an otherwise quiescent ocean forced by stress at the surface is then given by: q2 V if V ¼ K 2 qz

½1

Implicit in eqn [1] is that k does not vary with depth, so differentiation of eqn [1] with respect to z and substituting tˆ =K for qV=qz yields a second-order differential equation for tˆ subject to boundary conditions that tˆt vanish at depth and that it match the applied interfacial stress, tˆ 0 at z ¼ 0. The solution is simply: 4

tˆ ðzÞ ¼ tˆ 0 e

dz

½2

where dˆ ¼ ðf=7f7Þðif=KÞ1=2 is a complex extinction coefficient that both attenuates and rotates stress with increasing depth, clockwise in the Northern Hemisphere, counterclockwise in the Southern Hemisphere. The practical differences between Ekman spirals in velocity and stress are illustrated by measurements of mean velocity and Reynolds stress during a period of rapid ice drift at Ice Station Weddell near 651S, 501W (Figure 1). The mean current in a reference frame drifting with the ice velocity (i.e., the negative of the dashed vector labeled ‘Bot’ in Figure 1A) shows the characteristic leftward turning with depth, but 24 20 16 N 8

Bot

4 _1

0.1 m s

(A)

24

16 20

8 4

0 _4

(B)

_2

10 m2 s

Figure 1 (A) Plan view of mean velocity averaged over a period of steady drift at Ice Station Weddell (1992). Numbers indicate meters from the ice–ocean interface. The vector labeled ‘Bot’ is the apparent velocity of the seafloor in the drifting reference frame. (B) Horizontal Reynolds stress. The dotted stress hodograph is from a similarity model, with boundary stress (dashed vector) inferred from the model solution that matches observed stress at 4 m. (Reproduced from McPhee MG and Martinson DG (1994) Science 263: 218–221.)



also includes a region of strong shear between 4 m and the ice–ocean interface, as well as an apparent eastward geostrophic current of several centimeters per second. The last may include its own vertical shear unrelated to UBL dynamics. None of these complicating factors has much impact on the Reynolds stress (Figure 1B), which shows (in a general sense) the depth attenuation and rotation predicted by a simple complex exponential (2) with vertically invariant eddy viscosity. The latter derives from a similarity based value for K, proportional to 7tˆ 0 =f 7, with a magnitude of about 0.02 m2 s1. Since the interfacial stress is approximately proportional to wind speed squared, this is indeed similar to Ekman’s development,1 with the magnitude implied by the observations within a factor of about two of Ekman’s prediction. Although the profile of Figure 1(B) is especially ‘clean,’ numerous other examples of spirals in Reynolds stress profiles exist from under-ice measurements, most consistent with the neutral scaling implied by Kp7tˆ 0 =f 7. Thus despite its simplicity, the Ekman approach provides a remarkably accurate account of momentum flux in the UBL for many commonly encountered situations. It is a relatively minor step to adjust the surface velocity to account for the variable K surface layer. Done properly, this leads to a Rossby similarity drag formulation. Buoyancy Flux and the Seasonal Cycle

The other major factor by which the under-ice boundary layer interacts with the ice cover and atmosphere is the annual cycle of mixed layer temperature, salinity, and depth. During summer, the mixed layer warms, freshens, and shoals, to be followed during and after freezeup, by cooling (to freezing), salination, and deepening. Although this cycle emulates in many ways the annual cycle of temperate mixed layers, a major distinction is that buoyancy is controlled mainly by salinity rather than temperature (the thermal expansion coefficient decreases rapidly as T approaches freezing, whereas the saline contraction coefficient remains relatively constant), thus freezing or melting at the ice–ocean interface is the main source of buoyancy flux for the UBL. In the perennial pack of the Arctic, heat absorbed in the upper ocean through summer leads, melt ponds, and thin ice contributes to bottom melting and is an important part of both the ice mass balance

157

and the total summer buoyancy increase for the UBL. Away from the continental shelves and ice margins, heat exchange with the deep ocean tends to be small, limited by the cold halocline that separates water of Atlantic origin from the surface. In the eastern Arctic, the marginal ice zone of Fram Strait, and in the vast seasonal sea ice zone surrounding the Antarctic continent, the UBL interacts directly with warmer deep ocean, and oceanic heat mixed into the boundary layer from below often controls the ice mass balance, and exerts major influence on overall ocean stability. Buoyancy plays a major role in these exchange processes and is not adequately represented by treating eddy viscosity as dependent solely on surface stress. Most of the UBL research in recent years has been devoted to understanding how buoyancy influences turbulent fluxes.

Turbulence in the Under-ice Boundary Layer Reynolds Flux

When ice is in motion relative to the underlying water, there is a net flux of momentum in the underlying boundary layer, most of which is carried by turbulent fluctuations arising from relatively small, chaotic instabilities in the flow, motions which will also induce fluxes of scalar properties (e.g., T, S) if a mean gradient in the property exists. The turbulent transport process is best demonstrated by considering the advective part of the material derivative. Consider, for example, the simplest form of the heat equation: horizontally homogeneous, with no internal sources or sinks of heat. In a Eulerian reference frame, this reduces to a simple balance between the material derivative of temperature and the vertical gradient of the molecular heat diffusion dT qT q qT ¼ þ u rT ¼ vT dt qt qz qz

½3

where vT is the molecular thermal diffusivity. Turbulent flux of temperature variations arises from the advective term, u DT. If velocity and temperature are expressed as the sum of mean and turbulent (fluctuating) parts: u ¼ U¯ þ u0 and T ¼ T¯ þ T 0 , and the flow is incompressible and horizontally homogeneous with no mean vertical velocity,

1 Ekman suggested that the ‘depth of frictional influence’ D ¼ pO(2K/f) varied as wind speed divided by Osin F, where F is latitude. This implies no f dependence for k. At high latitudes, this has minor impact.


u rT ¼

q /w0 T 0 S qz

½4

158


Normally this term completely dominates the molecular flux and eqn [3] is approximated by qT q ¼ /w0 T 0 S qt qz

½5

In a strict sense, the angle brackets represent an ensemble Reynolds average over many independent realizations of the flow, but for practical applications it is assumed that the large-scale, ‘mean’ properties of the flow and its turbulent fluctuations respond in different and separable wavenumber bands (so that the local time derivative in eqn [5] has meaning), and that a suitable average in time is representative of the Reynolds flux. A similar analysis of du/dt leads to the divergence of the Reynolds stress tensor formed from the velocity covariance matrix of the three fluctuating velocity components. Under the same simplifications as above, the advective term in the mean horizontal velocity equation becomes q ð/u0 w0 S þ i/v0 w0 SÞ qz where the horizontal vector quantity t ¼ /u0 w0 S þ i/v0 w0 S is traditionally called Reynolds stress. A second important turbulence property associated with the Reynolds stress tensor is its trace q2 ¼ /u0 u0 S þ /v0 v0 S þ /w0 w0 S

½6

which is twice the turbulent kinetic energy (TKE) per unit mass. The connection between turbulence and eddy viscosity becomes apparent when the horizontal velocity equation is written with the simplifying (but often reasonable) assumptions of horizontal homogeneity, no mean vertical velocity, and negligible impact of molecular viscosity: qV q þ if V ¼ ð/u0 w0 S þ i/v0 w0 SÞ qt qz q qV ¼ ut l qz qz

½7

The last term in eqn [7] represents the mixing-length hypothesis, essentially a scaling argument that Reynolds stress is uniquely related to the mean velocity shear by the product of velocity and length scales characterizing the largest, energy-containing eddies in the flow. Eddy viscosity is K ¼ utl. The steady version of eqn [7] differs from eqn [1] in that K may depend on z and remains within the scope of the outer derivative.

Scales of Turbulence

A reasonable choice for the turbulence velocity scale (ut) is the friction speed u* ¼ O7tˆ 7. In exceptional cases where destabilizing buoyancy flux (/w0 b0 S ¼ (g/r)/r0 w0 S) from rapid freezing is the main source of turbulence, a more appropriate choice is the convective scale velocity w* ¼ ðlj/w0 b0 S0 jÞ1=3 where l is the length scale of the dominant eddies. An alternative scale is q given by eqn [6]; however, observations in the UBL show the ratio q/u * to be relatively constant (B 3) in shear-dominated flows; the distinction may therefore be academic until a clear connection between q and w* is demonstrated. Mixing length is the distance over which the ‘energy-containing’ eddies are effective at diffusing momentum. Several observational studies in the UBL have shown a robust relationship between a length scale lpeak inversely proportional to the wavenumber at the maximum of the weighted spectrum of vertical velocity, and l inferred by other methods. Since the spectrum of vertical velocity is relatively easy to measure, lpeak provides a useful proxy for estimating l simultaneously at several levels in the UBL. A diagram of governing turbulence scales in the UBL is presented in Figure 2, developed by combining simple boundary-layer similarity theory with numerous observations from drifting sea ice ranging from the marginal ice zone of the Greenland Sea, to the central Arctic ocean under thick ice and at the edges of freezing leads, and in the Weddell Sea. Figure 2(A) shows neutral stratification in the bulk of the UBL, when surface buoyancy flux (melt rate) is too small to have appreciable impact on turbulence. This is a common condition for perennial pack ice, which grows or melts slowly most of the year. Working from the interface down, mixing length increases approximately linearly with depth through the surface layer, until it reaches a limiting value proportional to the planetary length scale lmax ¼ L* u*0 =f , where L* B0:03. Usually, the surface layer extends 5 m or less. From there the mixing length holds relatively constant through the extent of the Ekman (or outer) part of the UBL, to the depth of the pycnocline (typically 35–50 m in the western Arctic; 75–150 m in the Weddell Sea). If the neutral layer is very deep, stress decreases more or less exponentially, following approximately the Ekman solution (see the discussion of Figure 3B below); however, if the pycnocline is shallow, a finite stress will exist at zp (indicated in Figure 2 by u*p ) instigating upward mixing of pycnocline water with associated buoyancy flux, /w0 b0 Sp. Mixing length in the highly stratified fluid just below the mixed-layer–pycnocline interface is estimated from the turbulent kinetic


UNDER-ICE BOUNDARY LAYER Melt season _ stabilizing buoyancy

Neutral stability

159

Freezeup _ destabilizing buoyancy

Ice

Surface layer

s1 = |z|

〈w ′b ′〉0 ≈ 0

〈w ′b ′〉0 > 0

〈w ′b ′〉0 < 0

2

max = ∗ Λ∗u∗0 /|f |

Ekman layer

max → zpyc max =Λ∗u∗0 /|f | → RcLp

u∗p

z = zpyc Pycnocline

→RcLp

〈w ′b ′〉p

ρ

(A)

(B)

(C)

Figure 2 Schematic diagram of mixing length distributions in the UBL under conditions of (A) dynamically negligible surface buoyancy flux (neutral stratification in the well mixed layer), (B) upward buoyancy flux from summer melting, with formation of a seasonal pycnocline and a negative density gradient in the ‘well mixed’ layer, and (C) downward buoyancy flux from rapid freezing, with positive density gradient to the pycnocline. u * , Friction velocity; /w0 b0 S, buoyancy flux; k, Ka´rmań’s constant, 0.4; L * , similarity constant, 0.028; Rc, critical flux Richardson number, 0.2; f, Coriolis parameter; L ¼ u 3 =ðk/w 0 b 0 SÞ, Obukhov length; Z * ¼ (1 þ L * u * / * kRc|f|L))1/2, stability parameter.

energy equation, which is dominated by three terms: production of TKE by shear ðPS ¼ tˆ qU=qzÞ, production by buoyancy (Pb ¼ /w0 b0 S), and dissipation by molecular forces (e). Relating stress and shear by the mixing-length hypothesis, the balance of TKE production with dissipation is u3 =l /w0 b0 S ¼ e

½8

*

The negative ratio of buoyancy production to shear production is the flux Richardson number: Pb =PS ¼

l/w0 b0 S l ¼ 3 u kL

½9

*

where L ¼ u3 =ðk/w0 b0 SÞ is known as the Obukhov * length. Studies of turbulence in stratified flows have shown that the ratio eqn [9] does not exceed a limiting value (the critical flux Richardson number, Rc) of about 0.2. This establishes a limit for mixing length in stratified flow: lrRckL, and it is assumed that in the pycnocline this limit is approached, where

L is based on pycnocline fluxes of momentum and buoyancy. Estimates of mixing length in a near neutral UBL from the Ice Station Weddell data (Figure 1) are illustrated in Figure 3(A). Points marked lpeak were taken from the inverse of the wavenumber at the peak in the vertical velocity spectra (averaged over all 1-h flow realizations), as described above. Values marked le were obtained using eqn [8] assuming negligible buoyancy flux, with measured values for u * and e (obtained from spectral levels in the inertial subrange). They show clearly that the ‘wall layer’ scaling, l ¼ k|z| does not hold for depths greater than about 4 m. Rapid melting reduces the extent of the surface layer and the maximum mixing length (Figure 2B). The stability factor Z* ¼ ð1 þ L* u* =ðkRc jf jLÞÞ1=2 derives from similarity theory and ensures that the mixing length varies smoothly from the neutral limit ðlmax -L* u*0 =jf jÞ to the stable limit (lmax-kRcL0) for increasing stability. A consequence of reduced scales during melting is formation of a seasonal


160


0

0

1

2

m

_

3

4

5

4

m2 s 2 (× 10 ) 0.4 0.8 1.2

0

_

1.6

0

m2 s 1 0.02 0.01

Kfit

az

= oe

⎥ z⎥

0.03

_5

Klocal

_ 10

m

peak

_ 15

Ksim

_ 20 _ 25

_ 30 Turbulent length scale (A)

Turbulent stress magnitude (C)

(B)

Eddy viscosity

Figure 3 (A) Mixing length determined from the TKE equation (le) and from the inverse of the wavenumber at the peak in the weighted w spectrum (lpeak). Error bars indicate twice the standard deviation from the spectra calculated from 1-h segments of data. (B) Average Reynolds stress magnitude, with a least-squares fitted exponential decay with depth. Fit coefficients are t0 ¼ 1.44 104 m2 s2 and a ¼ 0.051 m1. (C) Eddy viscosity estimated by three methods as described in the text. (Reproduced from McPhee MG and Martinson DG (1994) Science 263, 218–221.)

pycnocline, above a ‘trapped’ layer with properties indicative of the mixed layer that existed before the freshwater influx. Rapid ice growth produces negative buoyancy via enhanced salinity at the interface, increasing TKE by the buoyancy production term in eqn [8]. The result is that mixing length and eddy viscosity increase in the UBL, sometimes dramatically. During the 1992 Lead Experiment, turbulent flux and dissipation measured from the edge of a freezing lead in a forced convective regime showed that, compared with the neutral UBL, there was a tenfold increase in mixing length (based on w spectral peaks) and in eddy heat and salt diffusivity (based on measured fluxes and gradients). The Obukhov length was 12 m, about 40% of the mixed layer extent, indicating relatively mild convection, yet the turbulence was greatly altered, apparently by the generation of quasi-organized roll structures in the lead, reminiscent of Langmuir circulations (a thin ice cover precluded any surface waves at the time of the measurements). Mixing length inferred from the lead measurements increased away from the surface following Monin– Obukhov similarity (adapted from atmospheric boundary layer studies), reaching a maximum value roughly comparable to the pycnocline depth scaled by von Ka´rmań’s constant.

The density profiles in Figure 2(B) and (C) are drawn schematically with slight gradients in the socalled mixed layer. This is at odds with conceptual models of the upper ocean which treat the boundary layer as completely mixed, but is consistent with measurements in the UBL. Wherever scalar fluxes of temperature and salinity are measurable, vertical gradients (albeit small) of mean temperature and salinity are found in the fully turbulent UBL, including statically unstable profiles as in Figure 2(C).

Effective Eddy Viscosity and Diffusivity

Figure 3(C) illustrates different methods for estimating bulk eddy viscosity in the UBL. The distribution labeled Ksim is from the similarity model used to construct the stress profile of Figure 1(B) by matching observed stress at 4 m. The vertical distribution labeled Klocal is the product lpeaku * at each level (Figure 3A and B). Its vertical average value is 0.019 m2 s1. Finally, the dashed line labeled Kfit in Figure 3(B) is from the least-squares fitted extinction coefficient ðRefdˆ gÞ for the Ekman stress solution eqn [2]. The last method is sensitive to small stress values at depth: if the bottommost cluster is ignored, Kfit ¼ 0.020 m2 s1.



The mixing length hypothesis holds for scalar properties of the UBL as well as momentum, so that it is reasonable to express, e.g., kinematic heat flux as /w0 T 0 S ¼ u* lT

qT qT ¼ KH qz qz

½10

In flows where turbulence is fully developed with large eddies and a broad inertial subrange, scalar eddy diffusivity and eddy viscosity are comparable (Reynold’s analogy). In stratified flows with internal wave activity and relatively low turbulence levels, momentum may be transferred by pressure forces that have no analog in scalar conservation equations, hence scalar mixing length may be considerably less than l. By measuring turbulent heat flux and the mean thermal gradient, it is possible to derive an independent estimate of eddy diffusivity in the UBL from eqn [10]. An example of this method is shown in Figure 4, where heat flux measurements averaged over five instrument clusters are compared with the negative thermal gradient. The data are from the same Ice Station Weddell storm as the other turbulence measurements of Figures 1 and 3. The mean thermal diffusivity, KH ¼ 0.018 m2 s1, is similar to the eddy viscosity (Figure 3C). Close correspondence between eddy viscosity and heat diffusivity was also found during the 1989 CEAREX drift north of Fram Strait, and during the 1992 LEADEX project. In the forced convective regime of the latter, salinity flux 270

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Figure 4 Time series of turbulent heat flux, rcp /w 0 T 0 S(W m2, circles) and temperature gradient qT=qz(mK m1 curve). The overbar indicates a vertical average over five turbulence clusters from 4 to 24 m. Error bars are twice the sample standard deviation. The temperature gradient was calculated by linear regression, after the calibration of each thermometer was adjusted by a constant amount so that the gradient was zero at time 86.95 when heat flux was zero (heavy arrow). (Reproduced from McPhee MG and Martinson DG (1994) Science 263: 218– 221.)

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was measured for the first time, with comparably large values for eddy salt diffusivity as for eddy viscosity and heat diffusivity (but with low statistical significance for the regression of /w0 S0 S against qS/ qz).

Outstanding Problems Mixing in the Pycnocline

Understanding of turbulent mixing in highly stratified fluid just below the interface between the wellmixed layer and pycnocline is rudimentary. Many conceptual models assume, for example, that fluid ‘entrained’ at the interface immediately assumes the properties of the well-mixed layer (i.e., is mixed completely), so that the interface sharpens during storms as it deepens following the mean density gradient. Instead, measurements during severe storms in the Weddell Sea show upward turbulent diffusion of the denser fluid with a ‘feathering’ of the interface. Depending on how it is defined, the pycnocline depth may thus decrease significantly during extreme mixing events. Where the bulk stability of the mixed layer is low and there is large horizontal variability in pycnocline depth (as in the Weddell Sea), advection of horizontal density gradients may have large impact on mixing, both by changing turbulence scales and by conditioning the water column for equation-of-state related effects like cabbeling and thermobaric instability. Even with the advantage of the stable ice platform, observations in the upper pycnocline are hampered by the small turbulence scales, by the difficulty of separating turbulence from high frequency internal wave velocities, and by rapid migration of the interface in response to internal waves or horizontal advection. Convection in the Presence of Sea Ice

The cold, saline water that fills most of the abyssal world ocean originates from deep convection at high latitudes. Sea ice formation is a (geophysically) very efficient distillation process and may play a critical role in deep convection in areas like the Greenland, Labrador, and Weddell Seas where the bulk stability of the water column is low. By the same token, melting sea ice is a strong surface stabilizing influence that can rapidly shut down surface driven convection as soon as warm water reaches the well mixed layer from below. Understanding the physics of turbulent transfer in highly convective regimes is a difficult problem both from theoretical and observational standpoints, complicated not only by uncertainty about how


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large-scale eddies interact with the stably stratified pycnocline fluid, but also by the possibility of frazil ice, small crystals that form within the water column. Depending on where it nucleates, frazil can represent a distributed internal source of buoyancy and heat in the UBL. Zones of intense freezing tend to be highly heterogeneous, concentrated in lead systems or near the ice margins, and require specialized equipment for studying horizontal structure. Measuring difficulties increase greatly in the presence of frazil ice or supercooled water, because any intrusive instruments present attractive nucleation sites. In addition to questions of UBL turbulence and surface buoyancy flux, factors related to nonlinearities in the equation of state for sea water may have profound influence on deep convection triggered initially by ice growth and UBL convection. Recent studies have shown, for example, that certain regions of the Weddell Sea are susceptible to thermobaric instability, arising from nonlinearity of the thermal expansion coefficient with increasing pressure. The importance of thermobaric instability for an ice-covered ocean is that once triggered, the potential energy released and converted in to turbulence as the water column overturns thermobarically, may be sufficient to override the surface buoyancy flux that would result from rapid melting as warm water reaches the surface.

PS q Rc S T u u*

Z* k L* l lT v vT t F

turbulence scale velocity horizontal velocity vector convective turbulence scale velocity turbulent buoyancy flux, (g/r)/w0 r0 S kinematic turbulent heat flux turbulent salinity flux complex attenuation coefficient dissipation rate of turbulent kinetic energy stability factor, (1 þ L * u * /(kRc|f|L)) 1/2 von K`rmK`n’s constant (0.4) similarity constant (B 0.03) turbulent mixing length scale turbulent scalar mixing length scale kinematic molecular viscosity, units m2 s1 molecular scalar (thermal) diffusivity, units m2 s 1 Reynolds stress: /u0 w0 S þ i/v0 w0 S latitude

See also Arctic Ocean Circulation. Bottom Water Formation. Deep Convection. Ice–ocean interaction. Internal Tides. Langmuir Circulation and Instability. Windand Buoyancy-Forced Upper Ocean.

Further Reading

Symbols used f g K KH i L Pb

ut V w* /w0 b0 S /w0 T0 S /w0 S0 S dˆ e

Coriolis parameter acceleration of gravity eddy viscosity scalar eddy diffusivity imaginary number Obukhov length, u3 =ðk/w0 b0 SÞ * production rate of turbulent kinetic energy by buoyancy, /w0 b0 S production rate of turbulent kinetic energy by shear, u3* /l turbulent kinetic energy scale velocity critical flux Richardson number (B 0.2) salinity temperature three-dimensional velocity vector (u, v, w components) friction velocity, square root of kinematic stress

Ekman VW (1905) On the influence of the earth’s rotation on ocean currents. Ark. Mat. Astr. Fys 2: 1--52. Gill AE (1982) Atmosphere–Ocean Dynamics. New York: Academic Press. Johannessen OM, Muench RD, and Overland JE (eds.) (1994) The Polar Oceans and Their Role in Shaping the Global Environment: The Nansen Centennial Volume. Washington DC: American Geophysical Society. McPhee MG (1994) On the turbulent mixing length in the oceanic boundary layer. Journal of Physical Oceanography 24: 2014--2031. Pritchard RS (ed.) (1980) Sea Ice Processes and Models. Seattle, WA: University of Washington Press. Smith WO (ed.) (1990) Polar Oceanography. San Diego, CA: Academic Press. Untersteiner N (ed.) (1986) The Geophysics of Sea Ice. New York: Plenum Press.


UPPER OCEAN HEAT AND FRESHWATER BUDGETS P. J. Minnett, University of Miami, Miami, FL, USA & 2009 Elsevier Ltd. All rights reserved.

Introduction Most of the solar energy reaching the surface of the Earth is absorbed by the upper ocean. Some of this is released locally, often within the course of the following night, but some heat is retained for longer periods and is moved around the planet by the oceanic surface currents. Subsequent heat release to the atmosphere helps determine the patterns of weather and climate around the globe. While maps of sea surface temperature measured from satellites are now commonplace, it is the underlying reservoir of heat stored in the upper ocean that has the impact on the atmospheric circulation and weather, not only over the oceans but also over the continents downstream. Because the specific heat of water is much greater than that for air, the thermal capacity of a layer of the ocean about 3-m thick is the same as that of the entire atmosphere above. The upper ocean heat content, however, is not so accessible to measurements by satellite-borne instruments and is therefore less well described, and its properties less well understood. The density of seawater is determined in a nonlinear fashion by temperature and salinity and, to a much lesser degree, by pressure. Warmer, fresher seawater is less dense than cooler, saltier water. The viscosity of seawater is very low and so the fluid is very sensitive to flow generation by density differences. However, as a result of the rotation of the Earth, oceanic flow is not simply a redistribution of mass so that the surfaces of constant density coincide with surfaces of constant gravitational force; deviations are supported by balancing the horizontal pressure forces, caused by the variable distribution of density, with the Coriolis force (geostrophy). Vertical exchanges between the upper ocean and the deeper layers are inhibited by layers of density gradients, called pycnoclines, some of which are permanent features of the ocean, and others, generally close to the surface, are transient, existing for a day or less. Upper ocean salinity, through its contribution to controlling the ocean density, is therefore an important variable in determining the density distribution of

the upper ocean and the availability of oceanic heat to drive atmospheric processes. The range of sea surface temperatures, and, by extension, the mixed layer temperature, extends from 1.8 1C, the freezing point of seawater, to above 30 1C in the equatorial regions, especially in the western Pacific Ocean and eastern Indian Ocean. In particularly favorable situations, surface temperatures in excess of 35 1C may be found, such as in the southern Red Sea. The lowest upper ocean salinities are found in the vicinity of large river outflows and are close to zero. For most of the open ocean, upper ocean salinities lie in the range of 34–37. (Ocean salinity is measured as a dimensionless ratio with a multiplier of 10 3. A salinity of 35 means that 1 kg of seawater contains 35 g of dissolved salts.) Unlike elevated surface temperatures that result in a lowering of the surface density and a stable near-surface water column, increasing surface salinities by evaporation lead to increasing density and an unstable situation where the denser surface waters sink.

Governing Processes The upper ocean heat and salt (or freshwater) distributions are determined by the fluxes of heat and moisture through the ocean surface, the horizontal divergence of heat and salinity by advection, and by fluxes through the pycnocline at the base of the upper ocean ‘mixed layer’. This can be expressed for heat content per unit area, H, by DH ¼ Qsurf þ Qhoriz þ Qbase Dt where Qsurf represents the heat fluxes through the ocean surface, Qhoriz the divergence of advective heat flux in the column extending from the surface to the depth of the mixed layer, and Qbase is the vertical heat flux through the pycnocline at the base of the mixed layer, often presumed to be small in comparison with the surface exchanges. The surface heat flux has three components: the radiative fluxes, the turbulent fluxes, and the heat transport by precipitation. The radiative fluxes are the sum of the shortwave contribution from the sun, and the net infrared flux, which is in turn the difference between the incident atmospheric emission and the emission from


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the sea surface. The turbulent fluxes comprise those of sensible and latent heat. A similar expression can be used for the upper ocean freshwater budget, where the fluxes are simply those of water. The surface exchanges are the difference between the mass fluxes due to precipitation and evaporation, and the horizontal advective fluxes can be best framed in terms of the divergence of salinity. The depth of the mixed layer is often not easy to determine and there are several approaches used in the literature, including the depth at which the temperature is cooler than the surface temperature, and values of 0.1, 0.2, or 0.5 K are commonly used. Another is based on an increase in density, and a value of 0.125 kg m 3 is often used. These are both proxies for the parameter that is really desired, which is the depth to which turbulent mixing occurs, thereby connecting the atmosphere to the heat stored in the upper ocean. In situations of low wind speed and high insolation, a significant shallow pycnocline can develop through temperature stratification, and this decouples the ‘mixed’ layer beneath from the atmosphere above. Nevertheless, in most discussions of the surface heat and salt budget, these diurnal effects are discounted and the depth of integration is to the top of the seasonal pycnocline, or in the absence of the seasonal pycnocline, to the depth of the top of the permanent pycnocline. Surface Heat Exchanges

The heat input at the surface is primarily through the absorption of insolation. Of course this heating occurs only during daytime and is very variable in the course of a day because of the changing solar zenith angle, and by modulation of the atmospheric transparency by clouds, aerosols, and variations in water vapor. At a given location, there is also a seasonal modulation. In the Tropics, with the sun overhead on a very clear day, the instantaneous insolation can exceed 1000 W m 2. The global average of insolation is about 170 W m 2. The reflectivity of the sea surface in the visible part of the spectrum is low and depends on the solar zenith angle and the surface roughness, and thereby on surface wind speed. For a calm surface with the sun high in the sky, the integrated reflectance, the surface albedo, is about 0.02, with an increase to B0.06 for a solar zenith angle of 601. Having passed through the sea surface the solar irradiance, Ll, is absorbed along the propagation path, z, according to Beer’s law: dLl =dz ¼ kl Ll where the absorption coefficient, kl, is dependent on the wavelength of the light (red being absorbed more

quickly than blue) and on the concentration of suspended and dissolved material in the surface layer, such as phytoplankton. When the wind is low, the nearsurface density stratification that results from the absorption of heat causes the temperature increase to be confined to the near-surface layers, causing the growth of a diurnal thermocline. This is usually eroded by heat loss back to the atmosphere during the following night. If the wind speed during the day is sufficiently high, greater than a few meters per second, the subsurface turbulence spreads the heat throughout the mixed layer. There are a few locations where the insolation is high, the water is very clear, and the mixed layer depth sufficiently shallow that a small fraction of the solar radiation penetrates the entire mixed layer and is absorbed in the underlying pycnocline. Although the absorption and emission of thermal infrared radiation are confined to the ocean surface skin layer of a millimeter or less, the net infrared budget is a component of the surface heat flux that indirectly contributes to the upper ocean heat budget. The infrared budget is the difference between the emission, given by esT4, where e is the broadband infrared surface emissivity, s is Stefan–Boltzmann constant, and T is the absolute temperature of the sea surface. For T ¼ 20 1C, the surface emission is B410 W m 2. The incident infrared radiation is the emission from greenhouse gases (such as CO2 and H2O), aerosols, and clouds, and as such is very variable. For a dry, cloud-free polar atmosphere, the incident atmospheric radiation can be o200 W m 2, whereas for a cloudy tropical atmosphere, 400 W m 2 can be exceeded. The net infrared flux at the surface is generally in the range of 0–100 W m 2, with an average of about 50 W m 2. The turbulent heat fluxes at the ocean surface are so called because the vertical transport is accomplished by turbulence in the lower atmosphere. They can be considered as having two components: the sensible heat flux that results from a temperature difference between the sea surface and the overlying atmospheric boundary layer, and the latent heat flux that results from evaporation at the sea surface. The sensible heat flux depends on the air–sea temperature difference and the latent heat flux on the atmospheric humidity near the sea surface. Both have a strong wind speed dependence. Since the ocean is usually warmer than the atmosphere in contact with the sea surface, and since the atmosphere is rarely saturated at the surface, both components usually lead to heat being lost by the ocean. The global average of latent heat loss is about 90 W m 2 but sensible heat loss is only about 10 W m 2. Extreme events, such as cold air outbreaks from the eastern coasts of continents



over warm western boundary currents, can lead to much higher turbulent heat fluxes, even exceeding 1 kW m 2. The final component of the surface heat budget is the sensible heat flux associated with precipitation. Rain is nearly always cooler than the sea surface and so precipitation causes a reduction of heat content in the upper ocean. Typical values of this heat loss are about 2–3 W m 2 in the Tropics, but in cases of intense rainfall values of up to 200 W m 2 can be attained. Surface Freshwater Exchanges

Over most of the world’s ocean, the flux of fresh water through the ocean surface is the difference between evaporation and precipitation. The loss of fresh water at the sea surface through evaporation is linked to the latent heat flux through the latent heat of evaporation. Clearly, precipitation exhibits very large spatial and temporal variability, especially in the Tropics where torrential downpours associated with individual cumulonimbus clouds can be very localized and short-lived. Estimates of annual, globally averaged rainfall over the oceans is about 1 m of fresh water per year, but there are very large regional variations with higher values in areas of heavy persistent rain, such as the Intertropical Convergence Zone (ITCZ) which migrates latitudinally with the seasons. Over much of the mid-latitude oceans, drizzle is the most frequent type of precipitation according to ship weather reports. The global distribution of evaporation exceeds that of oceanic precipitation, with the difference being made up by the freshwater influx from rivers and melting glaciers. Advective Fluxes

The determination of the amount of heat and fresh water moved around the upper oceans is not straightforward as the currents are not steady, exhibiting much temporal and spatial variation. The upper ocean currents are driven both by the surface wind stress, including the large-scale wind patterns such as the trade winds and westerlies, and by the large-scale density differences that give rise to the thermohaline circulation that links all oceans at all depths. The strong western boundary surface currents, such as the Gulf Stream, carry much heat poleward, but have large meanders and shed eddies into the center of the ocean basins. Indeed, the ocean appears to be filled with eddies. Thus the measurements of current speed and direction, and temperature and salinity taken at one place at one time could be quite different when repeated at a later date.

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Measurements Much of what we know about the upper ocean heat and salt distribution has been gained from analysis of measurements from ships. Large databases of shipboard measurements have been compiled to produce a ‘climatological’ description of upper ocean heat and salt content. In some ocean areas, such as along major shipping lanes, the sampling density of the temperature measurements is sufficient to provide descriptions of seasonal signals, and the length of measurements sufficiently long to indicate long-term climate fluctuations and trends, but these interpretations are somewhat contentious. In other ocean areas, the data are barely adequate to confidently provide an estimate of the mean state of the upper ocean. Temperature is a much simpler measurement than salinity and so there is far more information on the distribution of upper ocean heat than of salt. Historically temperatures were measured by mercury-in-glass thermometers which recorded temperatures at individual depths. Water samples could also be taken for subsequent chemical analysis for salinity. The introduction of continuously recording thermometers, such as platinum resistance thermometers and later thermistors, resulted in measurements of temperature profiles, and the use of expendable bathythermographs (XBTs) meant that temperature profiles could be taken from moving ships or aircraft. The continuous measurement of salinity was a harder problem to solve and is now accomplished by calculating salinity from measurements of the ocean electrical conductivity. The standard instruments for the combined measurements of temperature and conductivity are referred to as CTDs (conductivity–temperature–depth) and are usually deployed on a cable from a stationary research ship, although some have been installed in towed vehicles for measurements behind a moving ship in the fashion of a yo-yo. In recent years, CTDs have been mounted in autonomous underwater vehicles (AUVs) that record profiles along inclined saw-tooth paths through the upper ocean (say to 600 m) and which periodically break surface to transmit data by satellite telemetry. Similarly, autonomous measurements from deep-water (to 2000 m) floats are transmitted via satellite when they surface. In the ARGO project, begun in 2000, over 3000 floats have been deployed throughout the global ocean. The floats remain at depth for about 10 days, drifting with the currents, and then make CTD measurements as they come to surface where their positions are fixed by the Global Positioning System (GPS), and the profile data transmitted to shore. Where time series of profiles are required at a particular location, internally recording


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CTDs can be programmed to run up and down wires moored to the seafloor. These have been used effectively in the Arctic Ocean, but the instruments have to be recovered to retrieve the data as the presence of ice prevents the use of a surface float for data telemetry. Additional sensors, such as transmissometers to measure turbidity, often augment the CTD measurements. Further information is supplied by a network of moored buoys that now span the tropical Pacific and Atlantic Oceans and which support sensors at fixed depths. The spatial distribution of upper ocean temperatures can be derived from satellite measurements of the sea surface temperatures which can now be made with global accuracies of 0.4 K or better using infrared and microwave radiometers. Such data sets now extend back a couple of decades. In 2009 and 2010, two new low-frequency microwave radiometers capable of measuring open ocean salinity are planned for launch (Aquarius is a NASA instrument, and SMOS – Soil Moisture, Ocean Salinity – is an ESA mission). To convert these satellite measurements of surface temperature and surface salinity into upper ocean heat and salt contents requires knowledge of the mixed layer depth, and while this is not directly accessible from satellite measurements, it can be inferred from measurements of ocean surface topography, derived from satellite altimetry, through the use of a simple upper ocean model. Such upper ocean heat content estimates are now being routinely derived and used in an experimental mode to assist in hurricane forecasting and research. Several lines across ocean basins have been sampled from a fleet of research ships in the framework of the World Ocean Circulation Experiment (WOCE) which took place between 1990 and 2002. Many of these sections are currently being reoccupied in the Repeat Hydrography Program to determine changes on decadal scales.

Distributions Heat

The quantitative specification of the upper ocean heat content remains rather uncertain, not so much because of our ability to measure the sea surface temperature (Figure 1), which is generally a good estimate of the mixed layer temperature, especially at night, but in determining the depth of the mixed layer. If the objective is to estimate the heat potentially available to the atmosphere, then the depth of the mixed layer based on density stratification in the pycnocline is more appropriate than the depth based on a temperature gradient in the thermocline,

although this is often used because of the availability of more data. On an annual basis, this can lead to significant differences in the estimates of the depth of the oceanic layer that can supply heat to the atmosphere (Figure 2). The difference between the tops of the thermocline and pycnocline results from density stratification caused by vertical salinity gradients (a halocline). In the low-latitude oceans, this is sometimes called a ‘barrier layer’ and can be as thick as the overlying isothermal layer, that is, halving the thickness of the upper ocean layer in contact with the atmosphere compared to that which would be estimated using temperature profiles alone. The barrier layers are probably caused by the subduction of more saline waters underneath water freshened by rainfall or river runoff. At high latitudes, the nonlinear relationship between seawater density and temperature and salinity means that density is nearly independent of temperature. The depth of the surface layer is therefore determined by the vertical salinity profile. During ice formation, brine is released from the freezing water and this destabilizes the surface layer, causing convective mixing. During ice melt, the release of fresh water stabilizes the upper ocean. In the Arctic Ocean, the depth of the mixed layer is determined by the depth of the halocline. The annual means of course do not reveal the details of the seasonal cycle in heat content, which in turn reflect the seasonal patterns of the surface fluxes and advective transports. Figure 3 shows the global distributions of the surface fluxes for January and July. The patterns in the insolation (short-wave heat flux) reflect the changes in the solar zenith angle, and the seasonal changes in cloud cover and properties. The seasonal patterns of the surface winds are apparent in the turbulent fluxes. The seasonal changes in the sea surface temperatures and upper ocean heat content are the summations of small daily residuals of local heating and cooling. Under clear skies and low winds, the absorption of insolation in the upper ocean leads to a stabilization of the surface layer through the formation of a near-surface thermocline. While the surface heat budget remains positive (i.e., the insolation exceeds heat loss through turbulent heat loss and the net infrared radiation), the diurnal thermocline grows with an attendant increase in the sea surface temperature. As the insolation decreases and the surface heat budget changes sign, the surface heat loss results in a fall in surface temperature and the destabilization of the near-surface layer. The resultant convective instability erodes the thermal stratification, returning the upper layer to a state close to


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that before diurnal heating began. In the heating season, on average, there will be more heat in the upper ocean at the end of the diurnal cycle, and in the cooling season there will be less. On days when the wind speed is greater than a few meters per second, the wind-induced turbulent mixing prevents the growth of the diurnal thermocline and the heat input during the day, and removed at night, is distributed throughout the mixed layer. Figure 4 shows measurements of the diurnal heating, expressed as a difference between the ‘skin’ temperature and a bulk temperature at the depth of a few meters, as a function of wind speed and time of day.

The magnitude of the surface temperature signal of diurnal warming is very strongly dependent on wind speed, and can be eroded very quickly if winds increase in the course of a day. The upper ocean of course exhibits variability on timescales longer than a year, often with profound consequences around the globe. The best known is the El Ninõ–Southern Oscillation that results in a marked change in sea surface temperature, depth of the mixed layer, and consequently upper ocean heat content in the equatorial Pacific Ocean. The perturbations to the atmospheric circulation have effects on weather patterns, including rainfall, around the


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UPPER OCEAN HEAT AND FRESHWATER BUDGETS NOAA/ESRL physical sciences division

NOAA/ESRL physical sciences division





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globe. Other multiyear features include the North Atlantic Oscillation, Arctic Oscillation, and Pacific Decadal Oscillation, in all of which there is a shift in both atmospheric circulation and oceanic response. In the case of the Arctic Oscillation, determined from the strength of the polar vortex relative to midlatitude surface pressure, a negative phase results in high surface pressures in the Arctic, and a more uniform distribution of sea ice. This is considered the normal situation. The positive phase results in lower surface pressure fields over the Arctic Ocean, a

thinning of the ice cover, and intrusion of relatively warm Atlantic water into the Arctic Basin. These have consequences on the upper ocean salinity and density stratification, and on interactions with the atmosphere, although the complexities of these feedbacks are poorly understood. The heat and salinity content advected through imaginary boundaries extending across oceans from coast to coast and from the surface to depth are a measure of the transport of heat and salt. For example, to maintain the Earth’s radiative equilibrium



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Figure 4 Signatures of diurnal heating revealed in the temperature difference between a radiometrically measured ocean skin temperature and a bulk temperature at a depth of 2 m. The measurements were taken in the Caribbean Sea from the Royal Caribbean International cruise liner Explorer of the Seas, which has been equipped as a research vessel. The temperature differences are plotted as a function of local mean time (LMT) and colored by wind speed (left), and as function of wind speed, colored by LMT (left). The largest temperature differences occur in early afternoon on days when the winds are low. Figure provided by Dr. C. L. Gentemann.

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60° E 120° E 180 120° W 60° W

0

40

Figure 5 Global distribution of salinity at a depth of 10 m as a global average (left) and monthly averages for Jan. (center) and Jul. (right). The figure is based on images produced at the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado (http:// www.cdc.noaa.gov).

with the sun and space the combined heat transport of the atmosphere and ocean from the Tropics toward the Poles is about 5.5 1015 W. How this is partitioned between the atmosphere and ocean is the subject of much research. Within the Atlantic Ocean, the northward transport of heat across 241 N is about 1.3 1015 W. Interestingly the average heat transport in the Atlantic is northward, even south of the Equator: 0.3 1015 W northward at 301 S and 0.6 1015 W at 111 S, although these estimates include transport at depth. The differences in heat transport between such lines at different latitudes provide estimates of the net heat absorbed by the upper ocean or given up to the atmosphere within the surface area of the oceans enclosed by the sections. Thus 77757 W m 2 are estimated to be released by the Atlantic Ocean to the atmosphere between 361 and 481 N, but only 8733 W m 2 between 221 and 361 N. In the North Pacific, 39719 W m 2 flow to the atmosphere between 241 and 481 N. Similarly the differences in the salt (or freshwater) content advected across these imaginary

boundaries indicate the imbalance between precipitation plus continental runoff and evaporation. Fresh Water

The large-scale patterns of upper ocean salinity (Figure 5) mirror the distribution of the annual freshwater flux at the sea surface (Figure 6), which is determined by the difference between rainfall and evaporation. The patterns of the components of the freshwater flux are quite zonal in character, with a band of heavy rainfall in the ITCZ and the maxima in evaporation occurring in the regions of the trade winds. There is very little known variability in the seasonal distribution of surface salinity, with the exceptions being in coastal regions where river run off often has a seasonal modulation, especially in the Bay of Bengal where the rainfall influencing the river discharge is dominated by the monsoons. The precipitation over the Bay of Bengal also shows a strong monsoonal influence, but over much of the oceans the seasonal variability is relatively


UPPER OCEAN HEAT AND FRESHWATER BUDGETS Precipitation

Evaporation

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Freshwater flux

10

Annual

60° N

Jan.

30° N Eq. 30° S 60° S

2

60° N

Jul.

1 0 mm d−1

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60° N

6

3

Eq. 30° S 60° S

8

4

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30° N Eq.

mm d−1

30° S 60° S 0

60° E 120° E 180 120° W 60° W 0 0

60° E 120° E 180 120° W 60° W 0 0

60° E 120° E 180 120° W 60° W 0

Figure 6 Global distributions of precipitation, evaporation, and freshwater flux at the ocean surface. Annual means are shown in the top row besides monthly averages for Jan. (middle row) and Jul. (bottom row). The color scale is at left for precipitation (positive mass flux into the ocean) and evaporation (positive mass flux into the atmosphere). The color scale for the freshwater flux is at right (positive mass flux into the atmosphere). The figures were generated from the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data Set (HOAPS) (http://www.hoaps.zmaw.de).

muted. Similarly for evaporation, although variations in the Northern Hemisphere signal are greater than those in the Southern Hemisphere. Pronounced maxima in the wintertime evaporation occur over the Gulf Stream and Kuroshio, and represent enhanced moisture fluxes from the sea surface driven by cold, dry air flowing off the continents over the warm surface waters of the north-flowing currents (Figure 6). On shorter timescales, there is pronounced variability in rainfall associated with the passage of weather fronts at mid-latitudes and with individual clouds in the Tropics. These small-scale, shortduration rainfall events hinder the accurate determination of the freshwater flux into the sea surface. In the Tropics, the rainfall associated with individual cumulonimbus clouds has a diurnal signature, especially in the vicinity of islands, even small atolls, where the diurnal sea breeze can trigger convection that results in rainfall, either directly into the ocean, or as runoff from land. The Arctic Ocean is a particularly interesting area regarding the local freshwater budget as the vertical stability is constrained by the salinity gradients in the halocline. Freshwater volumes in the Arctic Ocean are often calculated relative to a seawater salinity of 34.8. The fresher surface waters are sustained by riverine inflow, primarily from the great Siberian rivers and the Mackenzie River in Canada, that between them annually contribute about 3200 km3.

The inflow from the Pacific Ocean through the Bering Strait is about 2500 km3. The freshwater outflow is mainly through the Canadian Archipelago as liquid (B3200 km3) and through Fram Strait (B2400 km3 as liquid and B2300 km3 as ice). The contribution of precipitation minus evaporation is c. 2000 km3. The fresh water generated by brine rejection during ice formation would be B10 000 km3, which is a relatively small proportion of the riverine and Bering Strait input. The residence time of fresh water in the Arctic Ocean is about 10 years.

Severe Storms An important consequence of variations in the upper ocean heat content is severe storm generation and intensification. The prediction of the strength and trajectory of land-falling hurricanes and cyclones benefits from knowledge of the upper ocean heat content in the path of the storm. A surface temperature of 26 1C is generally accepted as being necessary for hurricane development, but the rate of development depends on the heat in the upper ocean available to drive the storm’s intensification. The passage of a severe storm leaves a wake that is identifiable as a depression of the surface temperature of several degrees and a deficit in the upper ocean heat content. These may survive for several days and can influence the development of subsequent


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storms should they pass over the wake. There are several well-documented cases in the Atlantic where hurricanes approaching land have suddenly lost intensity as they follow, or cross, the path of a prior storm. The converse is also true and hurricanes can undergo sudden intensification when they pass over regions of high upper ocean heat content, as can result from the meandering of the Loop Current in the Gulf of Mexico, for example. Monitoring the upper ocean heat content has become important for severe storm forecasting in the Tropics, especially in terms of sudden intensification. Using a combination of satellite measurements of sea surface temperature, sea surface topography, and a simple ocean model, the spatial distribution of the heat content between the surface and the estimated depth of the 26 1C isotherm is calculated on a daily basis. This is referred to as the ‘tropical cyclone heat

potential’ (Figure 7) and indicates regions where intensification of severe storms is likely. The rate of heat transfer from ocean to atmosphere in a hurricane is very difficult to measure, and varies greatly with the size, intensity, and stage of development of the storm. Estimates range in the order of 1013–1014 W. We have already seen that the northward heat flux in the Atlantic Ocean at 241 N is B1.3 1015 W. Thus, even though severe storms grow and are sustained by large heat fluxes, the magnitudes of the associated flow are relatively small in comparison to the poleward oceanic heat transport which is ultimately released to the atmosphere.

Reactions to Climate Change Away from polar regions, the density of seawater is a strong function of its temperature, and a consequence 1 Jan. 2006

45° N

45° N

0°

0°

45° S

45° S 15 Mar. 2006

45° N

0°

0°

GJ m−2

2

45° S

45° S

1 Jul. 2006

45° N

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0°

1

0°

45° S

45° S 15 Sep. 2006

45° N

0°

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0°

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45° N

45° S

135° W 90° W

45° W

0°

45° E

90° E

135° E

180°

135° W 90° W

Figure 7 Examples of daily maps of ‘tropical cyclone heat potential’ for 1 Jan., 15 Mar., 1 Jul., and 15 Sep. 2006. The March and September dates correspond roughly to the peak of cyclone activity in each hemisphere. The maps were derived from satellite measurements of sea surface temperature, sea surface topography, and a simple ocean model. The figure was derived from images generated by the NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML), Miami, Florida (http://www.aoml.noaa.gov).



of increasing temperatures as a result of global change is the expansion of the upper ocean, which will contribute to sea level rise. In fact, about half of the observed rise in global sea level during the twentieth century of 1–2 mm yr 1 can be attributable to expansion of the warming upper ocean. In addition to thermal expansion, another major impact of climate change is the increase in the upper ocean freshwater budget (reduction in salinity) as the land ice (glaciers and ice caps of Greenland and Antarctica) melt and the runoff enter the high-latitude oceans. This will result in an increase in the stability of the upper ocean and a consequent likely reduction in the mixed layer depths, especially in winter (Figure 2). Here the atmosphere is coupled to the heat available in a very deep ocean layer. Mixing heat and fresh water from the upper ocean to depth is also a driver of the global thermohaline circulation and disruption to this will also have significant consequences on the nearsurface components of the circulation and on the details of the poleward transfer of heat. This will impact global weather patterns, including rainfall over the ocean and land, in ways that are difficult to predict.

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Salinity Measurements. Upper Ocean Mean Horizontal Structure. Upper Ocean Mixing Processes. Upper Ocean Time and Space Variability. Upper Ocean Vertical Structure. Windand Buoyancy-Forced Upper Ocean. Wind Driven Circulation.

Further Reading

Improvements in our ability to determine the upper ocean heat and freshwater budgets, and monitor their changes with time, will occur in the near future with new satellite missions that will both continue the existing time series of sea surface temperature, topography and rainfall, and also introduce new variables: notably sea surface salinity. Additional information on the subsurface distributions of heat and fresh water will be provided by the autonomous profiling floats of the ARGO project that measure temperature and salinity from about 2000-m depth to a few meters below the surface. These will be augmented by AUVs, or ‘gliders’, roaming the oceans taking measurements along undulating paths, transmitting the data via satellite communications when they break the surface. The interpretation of the measurements, from both in situ and space-borne sensors, will be aided by increasingly complex, high-resolution models of the ocean state and the coupled ocean–atmosphere system.

Chen SS and Houze RA (1997) Diurnal variation and lifecycle of deep convective systems over the tropical Pacific warm pool. Quarterly Journal of the Royal Meteorological Society 123: 357--388. Foltz GR, Grodsky SA, Carton JA, and McPhaden MJ (2003) Seasonal mixed layer heat budget of the tropical Atlantic Ocean. Journal of Geophysical Research 108: 3146 (doi:10.1029/2002JC001584). Gill AE (1982) Atmosphere–Ocean Dynamics. San Diego, CA: Academic Press. Hasegawa T and Hanawa K (2003) Decadal-scale variability of upper ocean heat content in the tropical Pacific. Geophysical Research Letters 30: 1272 (doi:10.1029/2002GL016843). Josey SA, Kent EC, and Taylor PK (1999) New insights into the ocean heat budget closure problem from analysis of the SOC air–sea flux climatology. Journal of Climate 12: 2856--2880. Levitus S, Antonov J, and Boyer T (2005) Warming of the world ocean, 1955–2003. Geophysical Research Letters 32: L02604 (doi:10.1029/2004GL021592). Macdonald AM (1998) The global ocean circulation: A hydrographic estimate and regional analysis. Progress in Oceanography 41: 281--382. Peixoto JO and Oort AH (1992) Physics of Climate. New York: American Institute of Physics. Serreze MC, Barrett AP, Slater AG, et al. (2006) The largescale freshwater cycle of the Arctic. Journal of Geophysical Research 111: C11010 (doi:10.1029/2005JC003424). Shay LK, Goni GJ, and Black PG (2000) Effects of a warm oceanic feature on hurricane Opal. Monthly Weather Review 128: 1366--1383. Siedler G, Church J, and Gould J (eds.) (2001) Ocean Circulation and Climate: Observing and Modelling the Global Ocean. San Diego, CA: Academic Press. Willis JK, Roemmich D, and Cornuelle B (2004) Interannual variability in upper ocean heat content, temperature, and thermosteric expansion on global scales. Journal of Geophysical Research 109: C12036 (doi:10.1029/2003JC002260).

See also

Relevant Websites

Evaporation and Humidity. Heat and Momentum Fluxes at the Sea Surface. Heat Transport and Climate. Ocean Circulation. Ocean Circulation: Meridional Overturning Circulation. Open Ocean Convection. Satellite Remote Sensing of Sea Surface Temperatures. Satellite Remote Sensing:

http://www.remss.com – AMSR Data, Remote Sensing Systems. http://aquarius.gsfc.nasa.gov – Aquarius Mission Website, NASA. http://www.esr.org – Aquarius/SAC-D Satellite Mission, ESR.

Future Developments


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http://www.hoaps.zmaw.de – Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data. http://www.ghrsst-pp.org – High-Resolution SSTs from Satellites, GHRSST-PP. http://www.noc.soton.ac.uk – NOC Flux Climatology, at Ocean Observing and Climate pages of the National Oceanography Centre (NOC), and The World Ocean Circulation Experiment (WOCE) 1990–2002, NOC, Southhampton.

http://ushydro.ucsd.edu – Repeat Hydrography Project. http://www.cdc.noaa.gov – Search for Gridded Climate Data at PSD, ESRL Physical Sciences Division, NOAA. http://www.esa.int – SMOS, The Living Planet Programme, ESA. http://www.aoml.noaa.gov – Tropical Cyclone Heat Potential, Atlantic Oceanographic and Meteorological Laboratory (AOML).


UPPER OCEAN MEAN HORIZONTAL STRUCTURE M. Tomczak, Flinders University of South Australia, Adelaide, SA, Australia

following an introductory overview of some elementary property fields.

Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3083–3093, & 2001, Elsevier Ltd.

Introduction The upper ocean is the most variable, most accessible, and dynamically most active part of the marine environment. Its structure is of interest to many science disciplines. Historically, most studies of the upper ocean focused on its impact on shipping, fisheries, and recreation, involving physical and biological oceanographers and marine chemists. Increased recognition of the ocean’s role in climate variability and climate change has led to a growing interest in the upper ocean from meteorologists and climatologists. In the context of this article the upper ocean is defined as the ocean region from the surface to a depth of 1 km and excludes the shelf regions. Although the upper ocean is small in volume whencompared to the world ocean as a whole, it is of fundamental importance for life processes in the sea. It determines the framework for marine life through processes that operate on space scales from millimeters to hundreds of kilometers and on timescales from seconds to seasons. On larger space and timescales, its circulation and water mass renewal processes span typically a few thousand kilometers and several decades, which means that the upper ocean plays an important role in decadal variability of the climate system. (In comparison, circulation and water mass renewal timescales in the deeper ocean are of the order of centuries, and the water masses below the upper ocean are elements of climate change rather than climate variability.) The upper ocean can be subdivided into two regions. The upper region is controlled by air–sea interaction processes on timescales of less than a few months. It contains the oceanic mixed layer, the seasonal thermocline and, where it exists, the barrier layer. The lower region, known as the permanent thermocline, represents the transition from the upper ocean to the deeper oceanic layers. It extends to about 1 km depth in the subtropics, is some what shallower near the equator and absent poleward of the Subtropical Front. These elements of the upper ocean will be defined and described in more detail,

Horizontal Property Fields The annual mean sea surface temperature(SST) is determined by the heat exchange between ocean and atmosphere. If local solar heat input would be the only determinant, contours of constant SST would extend zonally around the globe, with highest values at the equator and lowest values at the poles. The actual SST field (Figure 1) comes close to thissimple distribution. Notable departures occur for two reasons. 1. Strong meridional currents transport warm water poleward in the western boundary currents along the east coasts of continents. Examples are theGulf Stream in the North Atlantic Ocean and the Kuroshio in the North Pacific Ocean. In contrast, cold water is transported equatorward along the west coast of continents. 2. In coastal upwelling regions, for example off the coasts of Peru and Chile or Namibia, SST is lowered as cold water is brought to the surface from several hundreds of meters depth. The annual mean sea surface salinity(SSS) is controlled by the exchange of fresh water between ocean and atmosphere and reflects it closely (Figure 2), the only departures being observed as a result of seasonal ice melting in the polar regions. As a result, the subtropics with their high evaporation and low rainfall are characterized by high salinities, while the regions of the westerly wind systems with their frequent rain-bearing storms are associated with low salinities(Figure 3). Persistent rainfall in the intertropical convergence zone produces a regional minimum in the SSS distribution near the equator. Departures from a strict zonal distribution are again observed, for the same reasons listed for the SST distribution. In addition, extreme evaporation rates in the vicinity of large deserts are reflected in high SSS, and large river run-off produced by monsoonal rainfall over south east Asia results in low SSS in the Gulf of Bengal. As a result, the SSS distribution of the north-west Indian Ocean shows a distinct departure from the normal zonal distribution. Seasonal variations of SST and SSS are mainly due to three factors.


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UPPER OCEAN MEAN HORIZONTAL STRUCTURE

Figure 1 Annual mean sea surface temperature (1C) (the contour interval is 21C). (Reproduced from World Ocean Atlas 1994.)

1. Variations in heat and freshwater exchange between ocean and atmosphere are significant for the SST distribution, which shows a drop of SST in winter and a rise in summer, but much less important for the SSS distribution, since rainfall and evaporation do not vary much over the year inmost ocean regions. 2. Changes in the ocean current system, particularly in monsoonal regions where currents reverse twice a year, cause the water of some regions to be replaced by water of different SST and SSS. 3. Monsoonal variations of freshwater input from major rivers influences SSS regionally. The temperature distribution at 500 m depth (Figure 4) reflects the circulation of the upper ocean. At this depth the temperature shows little horizontal variation around a mean of 8–101C. Departures from this mean temperature are, however, observed. (1) The western basins of the subtropics have the highest temperatures in all oceans. They indicate the centres of the subtropical gyres (see below). (2) Polewardof 351 latitude temperatures fall rapidly as the polar regions are reached, an indication of the absence of the permanent thermocline (see below). The salinity distribution at 500 m depth (Figure 5) shows clear similarities to the temperature distribution

Figure 2 Mean meridional distribution of sea surface salinity and mean meridional freshwater balance (evaporation precipitation).

and a strong correlation between high temperatures and high salinities. The salinity field displays a totalrange nearly as large as the range seen at the surface (Figure 3). The mean salinity varies strongly between ocean basins, with the North Atlantic Ocean having



Figure 3 Annual mean sea surface salinity. (Reproduced from World Ocean Atlas 1994.)

Figure 4 Annual mean potential temperature (1C) at 500 m depth. (Reproduced from World Ocean Atlas 1994.)


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Figure 5 Annual mean salinity (PSU) at 500 m depth.(Reproduced from World Ocean Atlas 1994.)

the highest salinity at this depth and the North Pacific Ocean the lowest. The horizontal oxygen distribution is chosen to represent conditions for marine life. Nutrient levels are inversely related to oxygen, and although the relationship varies between ocean basins, an oxygen maximum can always be interpreted as a nutrient minimum and an oxygen minimum as a nutrient maximum. At the sea surface the ocean is always saturated with oxygen. A map of sea surface oxygen would therefore only illustrate the dependence of the saturation concentration on temperature (and to a minor degree salinity) and show an oxygen concentration of 8 mll 1 or more at temperatures near freezing point and 4 mll 1 at the high temperatures in the equatorial region. The oxygen distribution at 500 m depth carries a dual signal. It reflects the dependence of the saturation concentration on temperature and salinity in the same way as at the surface but modified by the effect of water mass aging. If water is out of contact with the atmosphere for extended periods of time it experiences an increase in nutrient content from the remineralization of falling detritus; this process consumes oxygen. Water in the permanent thermocline can be a few decades old, which reduces its

oxygen content to 60–80% of the saturation value (Figure 6). The northern Indian Ocean is an exception to this rule; its long ventilation time (see below) produces oxygen values below 20% saturation. In the polar regions oxygen values at 500 m depth are generally closer to saturation as a result of winter convection in the mixed layer (see below).

The Mixed Layer and Seasonal Thermocline Exposed to the action of wind and waves, heating and cooling, and evaporation and rainfall, the ocean surface is a region of vigorous mixing. This produces a layer of uniform properties which extends from the surface down as far as the effect of mixing can reach. The vertical extent or thickness of this mixed layer is thus controlled by the time evolution of the mixing processes. It is smallest during spring and summer when the ocean experiences net heat gain (Figure 7).The heat which accumulates at the surface is mixed downward through the action of wind waves. During this period of warming the depth of the mixed layer is determined by the maximum depth which wave mixing can affect. Because winds



179

Figure 6 Annual mean oxygen saturation (%) at 500 m depth(the contour interval is 10%). (Reproduced from World Ocean Atlas 1994.)

Temperature 1

2 3

Temperature 5

4

1 2 3

Depth

Depth

4

Warming cycle

Cooling cycle

Figure 7 Time evolution of the seasonal mixed layer. Left, the warming cycle; right, the cooling cycle. Numbers can be approximately taken as successive months, with the association shown in Table 1.

areoften weaker during midsummer than during spring, wind mixing does not reach quite so deep during the summer months, and the mixed layer may consist of two or more layers of uniform properties (Figure 7, line 4 of the warming cycle). During fall and winter the ocean loses heat. This cooling produces a density increase at the sea

surface. As a result, mixing during the cooling period is no longer controlled by wave mixing but by convection. The convection depth is determined by the depth to which the layer has to be mixed until static stability is reached. The mixed layer therefore increases with time during fall and winter and reaches its greatest vertical extent just before spring. The thin region of rapid temperature change below the mixed layer is known as the seasonal thermocline. It is strongest (i.e., is associated with the largest change in temperature) in summer and disappears in winter. In the tropics (within 201 of the equator) the heat loss during winter is not strong enough to erase the seasonal thermocline altogether, and the seasonal character of the thermocline is then only seen as a variation of the associated vertical temperature gradient. In the subtropics the mixed layer depth varies between 20–50 m during summer and 70–120 m during winter. In subpolar regions the mixed layer depth can grow to hundreds of meters during winter. Three locations of particularly deep winter mixed layers are the North Atlantic Ocean between the Bay of Biscay and Iceland, the eastern South Indian Ocean south of the Great Australian Bight and the region to the west


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Table 1

Association between number in Figure 7 and months Northern Hemisphere

Southern Hemisphere

Number in Figure 7

Warming cycle

Cooling cycle

Warming cycle

Cooling cycle

1 2 3 4 5

February April May June

June August September October January

August October November December

December February March April July

of southern Chile. In these regions mixed layer depths can exceed 500 m during late winter.

The Barrier Layer The mixed layer depth is often equated with the depth of the seasonal thermocline. Historically this view is the result of the paucity of salinity or direct density observations and the resulting need to establish information about the mixed layer from a vertical profile of temperature alone. This approach is acceptable in many situations, particularlyin the temperate and subpolar ocean regions. There are, however, situations where it can be quite misleading. A temperature profile obtained in the equatorial western Pacific Ocean, for example, can show uniform temperatures to depths of 80–100 m. Such deep homogeneity in a region where typical wind speeds

33

34

rarely exceed those of a light breeze cannot be produced by wave mixing. The truth is revealed in a vertical profile of salinity which shows a distinct salinity change at a much shallower depth, typically 25–50 m, indicating that wave mixing does not penetrate beyond this level and that active mixing is restricted to the upper 25–50 m. In these situations the upper ocean contains an additional layer known as the barrier layer (Figure 8). The mixed layer extends to the depth where the first density change is observed. This density change is the result of a salinity increase with depth and therefore associated with a halocline (a layer of rapid vertical salinity change). The temperature above and below the halocline is virtually identical. The barrier layer is the layer between the halocline and thethermocline. The barrier layer is of immense significance for the oceanic heat budget. In most ocean regions the mixed 20

S

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(B)

Figure 8 The structure of the upper ocean in the absence (A) and presence (B) of a barrier layer. T: temperature (1C),S: salinity, st: density. Note the uniformity of temperature(T) from the surface to the bottom of the barrier layer in(B). The stations were taken in the central South China Sea during September 1994.



layer experiences a net heat gain at the surface during spring and summer and has to export heatin order to maintain its temperature in a steady state. If (as described in a previous section) the mixed layer extends down to the seasonal thermocline, this is achieved through the entrainment of colder water into the mixed layer from below. The presence of the barrier layer means that the water entrained from the region below the mixed layer is of the same temperature as the water in the mixed layer itself. The entrainment process is still active but does not achieve the necessary heat export. The barrier layer acts as a barrier to the vertical heat flux, and the heat gained by the mixed layer has to be exported through other means, mainly through horizontal advection by ocean currents and, if the mixed layer is sufficiently transparent to the incoming solar radiation, through direct downward heat transfer from the atmosphere to the barrier layer. The existence of the barrier layer has only come to light in the last decade or two when high-quality salinity measurements became available in greater numbers. It has now been documented for all tropical ocean regions. In the Pacific Ocean the regional extent of the barrier layer is closely linked with high local rainfall in the Intertropical and South Pacific Convergence Zones of the atmosphere. This suggests that the Pacific barrier layer is formed by the lowering of the salinity in the shallow mixed layer in response to local rainfall. In contrast, the barrier layer in the Indian Ocean varies seasonally in extent, and the observed lowering of the mixed layer salinity seems to be related to the spreading of fresh water from rivers during the rainy monsoon season. In the Atlantic Ocean the barrier layer is most likely the result of subduction of high salinity water from the subtropics under the shallow tropical mixed layer. There are also observations of seasonal barrier layers in other tropical ocean regions, such as the South China Sea.

The Subtropical Gyres and the Permanent Thermocline The permanent or oceanic the rmocline is the transition from the upper ocean to the deeper oceanic layers. It is characterized by a relatively rapid decrease of temperature with depth, with a total temperature drop of some 151C over its vertical extent, which varies from about 800 m in the subtropics to less than 200 m near the equator. This depth range does not display the relatively strong currents experienced in the upper ocean but still forms part of the general wind driven circulation, so its water moves with the same current systems seen at the sea surface but with lesser speed.

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The permanent thermocline is connected with the atmosphere through the Subtropical Convergence, broad region of the upper ocean poleward of the subtropics where the wind-driven surface currents converge, forcing water to submerge (‘subduct’) under the upper ocean layer and enter the permanent thermocline. This convergence is particularly intense in the subtropical front, a region of enhanced horizontal temperature change within the Subtropical Convergence found at about 351Nand 401S. The Subtropical Front is therefore considered the poleward limit of the permanent thermocline (Figure 9). There is also a zonal variation in the vertical extent, with smallest values in the east and largest values in the west. Taken together, the permanent thermocline appears bowl shaped, being deepest in the western parts of the subtropical ocean (25–301N and 30–351S). The shape is the result of geostrophic adjustment in the wind-driven circulation, which produces anticyclonic water movement in the subtropics known as the subtropical gyres. In most ocean regions the permanent thermocline is characterized by a tight temperature–salinity(TS) relationship, lower temperatures being associated with lower salinities. If temperature or salinity is plotted on a constant depth level across the permanent thermocline, the highest temperature and salinity values are found in the western subtropics (Figures 4 and 5). The tight TS relationship indicates the presence of a stable water mass, known as Central Water. This water mass is formed at the surface in the subtropical convergence, particularly at the downstream end of the western boundary currents, where it is subducted and from where it renews (‘ventilates’) the permanent thermocline by circulating in the subtropical gyres, moving equatorward in the east, westward with the equatorial current system and returning to the ventilation region in the west. As a result the age of the Central Water does not increase in a simple meridional direction from the subtropical front towards the equator but is lower in the east and higher in the west. As the Subtropical Front is a feature of both hemispheres, each ocean, with the exception of the Indian Ocean which does not reach far enough north to have a Subtropical Front in the northern hemisphere, has Central Water of northern and southern origins (Figure 9). Fronts between the different varieties of Central Water are a prominent feature of the permanent thermocline. These fronts are characterized by strong horizontal temperature and salinity gradients but relatively small density change because the effect of temperature on density is partly compensated by the effect of salinity. As a result smallscale mixing processes such as double diffusion,


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N 60° Subtropical Front North Atlantic Central Water

Subtropical Front

30°

Western North Pacific Central Water Indian 0°

Tr a Eastern nsit io n North Zo ne Pacific Central Water

North Pacific Equatorial Water

Australasian Mediterranean Water

South Pacific Equatorial Water

Central Water

Western South Pacific Central Water

30°

South Atlantic Central Water

Eastern South Transition Pacific Zone Central Water

l Front

Front

Subtropical

Subtropica

60° S

30°

60°

90°

120°

150° E 180° W 150°

120°

90°

60°

30°

W 0° E

30°

Figure 9 Regional distribution of the water masses of the permanent thermocline.

filamentation and interleaving are of particular importance in these fronts.

The Equatorial Region The equatorial current system occupies the region 151S–151N and is thus more than 3000 km wide. Most of itis taken up by the North and South Equatorial Currents, the westward flowing equatorial elements of the subtropical gyres discussed above. Between these two currents flows the North Equatorial Countercurrent as a relatively narrow band eastward along 51N in the Atlantic and Pacific Oceans and, during the north-east monsoon season, along 51S in the Indian Ocean. Another eastward current, the Equatorial Undercurrent, flows submerged along the equator, where it occupies the depth range 50–250 m as a narrow band ofonly 200 km width. Currents near the equator are generally strong, and for dynamical reasons transport across the equator is more or less restricted to the upper mixed layer and to a narrow regime of a few hundred kilometers width along the western boundary of the oceans. This restriction andthe narrow eastward

currents embedded in the general westward flow, shape the distribution of properties in the permanent thermocline near the equator. Insituations where subtropical gyres exist (the Atlantic and Pacific Oceans) in both hemispheres they enter the equatorial current system from the north east and from the south east, leaving a more or less stagnant region (‘shadow zone’) between them near the eastern coast. Figure 10 shows the age distribution for the Atlantic Ocean. The presence of particularly old water in the east indicates a stagnant region or ‘shadow zone’ between the subtropical gyres. The strong eastward flowing currents in the equatorial current system modify the age distribution in the permanent thermocline further. In Figure 10 the Equatorial Undercurrent manifests itself as a band of relatively young water, which is carried eastward. The Indian Ocean does not extend far enough to the north to have a subtropical convergence in the Northern Hemisphere. In the absence of a significant source of thermocline water masses north of theequator the water of the Northern Hemisphere can only be ventilated from the south. Figure 11 shows property fields of the permanent thermocline in the


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Figure 10 Pseudo age of Central Water in equatorial region of the Atlantic Ocean at 500 m depth. The quantity pseudo age expresses the time elapsed since the water had last contact with the atmosphere; it is determined by using anarbitrary but realistic oxygen consumption rate for the permanent thermocline. (Reproduced from Poole and Tomczak m (19) Optimum multiparameter analysis of the water mass structure in the Atlantic Ocean thermocline. Deep-Sea Research 46: 1895–1921.)

Indian Ocean and pathways of its water masses. The region between 51S and the equator is dominated by the westward flow of Australasian Mediterranean Water (AAMW), a water mass formed in the Indonesian seas. Its mass transport is relatively modest, and it is mixed into the surrounding waters before it reaches Africa. Indian Central Water(ICW) originates near 301S in large volume; it joins the anticyclonic circulation of the subtropical gyre and can be followed (at the depth level shown in Figure 11 by its temperature of 11.71C and salinity of 35.1) across the equator along the African coast and into the Northern Hemisphere. The flow into the Northern Hemisphere is thus severely restricted, and the ventilation of the northern Indian Ocean thermocline is unusually inefficient. This is reflected in the extremely low oxygen content throughout the northern Indian Ocean.

The Polar Regions Poleward of the subtropical front the upperocean changes character. As polar latitudes are approached the distinction between upper ocean and deeper layers disappears more and more. There is no

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Figure 11 Climatological mean temperature (1C) (A),salinity (PSU) (B) and oxygen concentration (ml l 1) (C) in the Indian Ocean for the depth range 300–450 m, with pathways for Indian Central Water (ICW)and Australasian Mediterranean Water (AAMW). (Reproduced from Tomczak and Godfrey, 1994.)

permanent thermocline; temperature, salinity and all other properties are nearly uniform with depth. The surface mixed layer is, of course, still well defined as the layer affected by wave mixing, but its significance for the heat exchange with the atmosphere is greatly reduced because frequent convection events produced by surface cooling penetrate easily into the waters below themixed layer. Because in the polar regions the upper ocean and the deeper layers form a single dynamic unit, the horizontal structure of the upper ocean in these regions is strongly influenced by features of the deeper layers. Figure 12 shows the arrangement of the



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Figure 12 Fronts in the Southern Ocean. (Reproduced from Tomczak and Godfrey, 1994) STF, Subtropical Front; SAF, Subantarctic Front; PF, Polar Front; CWB, Continental Water boundary; AD, Antarctic Divergence.

various fronts in the Southern Ocean. The fronts are associated with the Antarctic Circumpolar Current. They occupy about 20% of its area but carry 75% of its transport. These fronts extend from the surface to the ocean floor and are thus not exclusive features of the upper ocean. At the low temperatures experienced in the polar seas the density is very insensitive to temperature changes and iscontrolled primarily by the salinity. During ice formation salt seeps out and accumulates under the ice, increasing the water density and causing it to sink. Salt from the upper ocean is thus transferred to the deep ocean basins. As a result, a significant amount of fresh water is added to the upper ocean when the ice melts and floats over the oceanic water. The resulting density gradient guarantees stability of the water column even in the presence of temperature inversions. A characteristic feature of the upper ocean in the polar regions is therefore the widespread existence of shallow temperature maxima. In the Arctic Ocean the water

below the upper ocean can be as much as 41C warmer than the mixed layer. Intermediate temperature maxima in the Antarctic Ocean are less pronounced (up to 0.51C) but occur persistently around Antarctica.

See also Ekman Transport and Pumping. Geophysical Heat Flow. Heat Transport and Climate. Satellite Remote Sensing of Sea Surface Temperatures. Satellite Remote Sensing: Salinity Measurements. Wind- and Buoyancy-Forced Upper Ocean. Wind Driven Circulation.

Further Reading Tomczak M and Godfrey JS (1994) Regional Oceanography: an Introduction. Oxford: Pergamon.


UPPER OCEAN MIXING PROCESSES

The ocean’s effect on weather and climate is governed largely by processes occurring in the few tens of meters of water bordering the ocean surface. For example, water warmed at the surface ona sunny afternoon may remain available to warm the atmosphere that evening, or it may be mixed deeper into the ocean not to emerge for many years, depending on near-surface mixing processes. Local mixing of the upper ocean is predominantly forced from the state of the atmosphere directly above it. The daily cycle of heating and cooling, wind, rain, and changes in temperature and humidity associated with mesoscale weather features produce a hierarchy of physical processes that act and interact to stir the upper ocean. Some of these are well understood, whereas others have defied both observational description and theoretical understanding. This article begins with an example of in situ measurements of upper ocean properties. These observations illustrate the tremendous complexity of the physics, and at the same time reveal some intriguing regularities. We then describe a set of idealized model processes that appear relevant to the observations and in which the underlying physics is understood, at least at a rudimentary level. These idealized processes are first summarized, then discussed individually in greater detail. The article closes with a brief survey of methods for representing upper ocean mixing processes in large-scale ocean models. Over the past 20 years it has become possible to make intensive turbulence profiling observations that reveal the structure and evolution of upper ocean mixing. An example is shown in Figure 1, which illustrates mixed-layer1 evolution, temperature

structure and small-scale turbulence. The small white dotsin Figure 1 indicate the depth above which stratification is neutral or unstable and mixing is intense, and below which stratification is stable and mixing is suppressed. This represents a means of determining the vertical extent of the mixed layer directly forced by local atmospheric conditions. (We will call the mixed-layer depth D.) Following the change in sign from negative (surface heating) to positive (surface cooling) of the surface buoyancy flux, Jb0 , the mixed layer deepens. (Jb0 represents the flux of density (mass per unit volume) across the sea surface due to the combination of heating/cooling and evaporation/ precipitation.) The mixed layer shown in Figure 1 deepens each night, butthe rate of deepening and final depth vary. Each day, following the onset of daytime heating, the mixed layer becomes shallower. Significant vertical structure is evident within the nocturnal mixed layer. The maximum potential temperature (y) is found at mid-depth. Above this, y is smaller and decreases toward the surface at the rate of about 2 mK in 10 m. The adiabatic change in temperature, that due to compression of fluid parcels with increasing depth, is 1 mKin 10 m. The region above the temperature maximum is superadiabatic, and hence prone to convective instability. Below this superadiabatic surface layer is a layer of depth 10–30 m in which the temperature change is less than 1 mK. Within this mixed layer, the intensity of turbulence, as quantified by the turbulent kinetic energy dissipation rate, e, is relatively uniform and approximately equal to Jb0 . (e represents the rate at which turbulent motions in a fluid are dissipated to heat. It is an important term in the evolution equation for turbulent kinetic energy, signifying the tendency for turbulence to decay inthe absence of forcing.) Below the mixed layer, e generally (but not always) decreases, whereas above, e increases by 1–2 factors of 10. Below the mixed layer is a region of stable stratification that partially insulates the upper ocean from the ocean interior. Heat, momentum, and chemical species exchanged between the atmosphere and the ocean interior must traverse the centimeters thick cool

1 Strictly, a mixed layer refers to a layer of fluid which is not stratified (vertical gradients of potential temperature, salinity and potential density, averaged horizontally or in time, are zero. The terminology is most precise in the case of a convectively forced boundary layer. Elsewhere, oceanographers use the term loosely to describe the region of the ocean that responds most directly to

surface processes. Late in the day, following periods of strong heating, the mixed layer may be quite shallow (a few meters or less), extending to the diurnal thermocline. In winter and following series of storms, the mixed layer may extend vertically to hundreds of meters, marking the depth of the seasonal thermocline at midlatitudes.

J. N. Moum and W. D. Smyth, Oregon State University, Corvallis, OR, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3093–3100, & 2001, Elsevier Ltd.

Introduction


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Figure 1 Observations of mixing in the upper ocean over a five-dayperiod. These observations were made in March 1987 in the North Pacific using a vertical turbulence profiler and shipboard meteorological sensors. (A) The variation in the surface buoyancy flux, Jb0 , which is dominated by surface heating and cooling. The red (blue) areas represent daytime heating (nighttime cooling). Variations in the intensity of nighttime cooling are primarily due to variations in winds. (B) Potential temperature referenced to the individual profile mean in order to emphasize vertical rather than horizontal structure (y; K). To the right is an averaged vertical profile from the time period indicated by the vertical bars at top and bottom of each of the left-hand panels. (C) The intensity of turbulence as indicated by the viscous dissipation rate of turbulence kinetic energy, e. To the right is an averaged profile with the mean value of Jb0 indicated by the vertical blue line. The dots in (B) and (C) represent the depth of the mixed layer as determined from individual profiles.



skin at thevery surface, the surface layer, and the mixed layer to modify the stable layer below. These vertical transports are governed by a combination of processes, including those that affect only the surface itself (rainfall, breaking surface gravity waves), those that communicate directly from the surface throughout the entire mixed layer (convective plumes) or a good portion of it (Langmuir circulations) and also those processes that are forced at the surface but have effects concentrated at themixed-layer base (inertial shear, Kelvin–Helmholtz instability, propagating internal gravity waves). Several of these processes are represented in schematic form in Figure 2. Whereas Figure 1 represents the observed time evolution of the upper ocean at a single location, Figure 2 represents an idealized three-dimensional snapshot of some of the processes that contribute to this time evolution. Heating of the ocean’s surface, primarily by solar (short-wave) radiation, acts to stabilize the water column, thereby reducing upper ocean mixing. Solar radiation, which peaks at noon and is zero at night, penetrates the air–sea interface (limited by absorption and scattering to a few tens of meters), but

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heat is lost at the surface by long-wave radiation, evaporative cooling and conduction throughout both day and night. The ability of the atmosphere to modify the upper ocean is limited by the rate at which heat and momentum can be transported across the air–sea interface. The limiting factor here is theviscous boundary layer at the surface, which permits only molecular diffusion through to the upper ocean. This layer is evidenced by the ocean’s coolskin, a thin thermal boundary layer (a few millimeters thick), across which a temperature difference of typically 0.1 K is maintained. Disruption ofthe cool skin permits direct transport by turbulent processes across theair–sea interface. Once disrupted, the cool skin reforms over a period of some tens of seconds. A clear understanding of processes that disrupt the coolskin is crucial to understanding how the upper ocean is mixed.

Convection Cooling at the sea surface creates parcels of cool, dense fluid, which later sink to a depth determined Wind

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Thermo cline Breakin g interna l waves Figure 2 Diagram showing processes that have been identified by a widerange of observational techniques as important contributors to mixing the upperocean in association with surface cooling and winds. The temperature (y) profiles shown here have the adiabatic temperature (that due to compression of fluid parcels with depth) removed; thisis termed potential temperature. The profile of velocity shear (vertical gradient of horizontal velocity) indicates no shear in the mixed layer and nonzero shear above. The form of the shear in the surface layer is a current area of research. Shear-induced turbulence near the surface may be responsible for temperature ramps observed from highly resolved horizontal measurements. Convective plumes and Langmuir circulations both act to redistribute fluid parcels vertically; during convection, they tend to movecool fluid downward. Wind-driven shear concentrated at the mixed-layer base (thermocline) may be sufficient to allow instabilities to grow, from which internal gravity waves propagate and turbulence is generated. At the surface, breaking waves inject bubbles and highly energetic turbulence beneath the sea surface and disrupt the ocean’s cool skin, clearing a pathway for more rapid heat transfer into the ocean.


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by the local stratification in a process known as convection. Cooling occurs almost every night and also sometimes in daytime in association with weather systems such as cold air outbreaks from continental landmasses. Convection may also be causedby an excess of evaporation over precipitation, which increases salinity, and hence density, at the surface. Winds aid convection by a variety of mechanisms that agitate the sea surface, thereby disrupting the viscous sublayer and permitting rapid transfer of heat through the surface (see below). Convection in the ocean is analogous to that found in the daytime atmospheric boundary layer, which is heated from below, and which has been studied in great detail. Recourse to atmospheric studies of convection has helped in understanding the ocean’s behavior. Surface tension and viscous forces initially prevent dense, surface fluid parcels from sinking. Once the fluid becomes sufficiently dense, however, these forces are overcome and fluid parcels sinkin the form of convective plumes. The relative motion of the plumes helps to generate small-scale turbulence, resulting in a turbulent field encompassing a range of scales from the depth of the mixed layer (typically 100 m) to a few millimeters. A clear feature of convection created bysurface cooling is the temperature profile of the upper ocean (Figure 1). Below the cool skin is an unstable surface layer that is the signature of plume formation. Below that is a well-mixed layer in which density (as well as temperature and salinity) is relatively uniform. The depth of convection is limited bythe local thermocline. Mixing due to penetrative convection into the thermocline represents another source of cooling of the mixed layer above. Within the convecting layer, there is an approximate balance between buoyant production of turbulent kinetic energy and viscous dissipation, as demonstrated by the observation eEJb0 . The means by which the mixed layer is restratified following nighttime convection are not clear. Whereas someone-dimensional models yield realistic time series of sea surface temperature, suggesting that restratification is a one-dimensional process (see below), other studies of this issue have shown onedimensional processes to account for only 60% of the stratification gained during the day. It has been suggested that lateral variations in temperature, due to lateral variations in surface fluxes, or perhaps lateral variations in salinity due to rainfall variability, may be converted by buoyant forces into vertical stratification. These indicate the potential importance of three-dimensional processes to restratification.

Wind Forcing Convection is aided by wind forcing, in part because winds help to disrupt the viscous sublayer at the sea surface, permitting more rapid transport of heat through the surface. In the simplest situation, winds produce a surface stress and a sheared current profile, yielding a classic wall-layer scaling of turbulence and fluxes in the surface layer, similar to the surface layer of the atmosphere. (Theory, supported by experimental observations, predicts a logarithmic velocity profile and constant stress layer in the turbulent layer adjacent to a solid boundary. This is typically found in the atmosphere during neutral stratification and is termed wall-layer scaling.) This simple case, however, seems to berare. The reason for the difference in behaviors of oceanic and atmospheric surface layers is the difference in the boundaries. The lower boundary of the atmosphere is solid (at least over land, where convection is well-understood), but the ocean’s upper boundary is free to support waves, ranging from centimeter-scale capillary waves, through wind waves (10s of meters) to swell (100s of meters). Thesmaller wind waves lose coherence rapidly, and are therefore governed by local forcing conditions. Swell is considerably more persistent, and may therefore reflect conditions at a location remote in space and time from the observation, e.g., a distant storm. Breaking Waves

Large scale breaking of waves is evidenced at the surface by whitecapping and surface foam, allowing visual detection from above. This process, which is not at all well understood, disrupts the ocean’s cool skin, a fact highlighted by acoustic detection of bubbles injected beneath the sea surface by breaking waves. Small-scale breaking, which has no visible signature (and is even less well understood but is thought to be due to instabilities formed in concert with the superposition of smaller-scale waves) also disrupts the ocean’s cool skin. An important challenge for oceanographers is to determine the prevalence of small-scale wave breaking and the statistics of cool skin disruption at the sea surface. The role of wave breaking in mixing is an issue of great interest at present. Turbulence observations in the surface layer under a variety of conditions have indicated that at times (generally lower winds and simpler wave states) the turbulence dissipation rate (and presumably other turbulence quantities including fluxes) behaves in accordance with simple wall-layer scaling and is in this way similar to the atmospheric surface layer. However, under higher winds, and perhaps more complicated wave states, turbulence dissipation rates greatly exceed those



predicted by wall-layer scaling. This condition has been observed to depths of 30 m, well below a significant wave height from the surface, and constituting a significant fraction of the ocean’s mixed layer. (The significant wave height is defined as the average height of the highest third of surface displacement maxima. A few meters is generally regarded as a large value.) Evidently, an alternative to wall-layer scaling is needed for these cases. This is a problem of great importance in determining both transfer rates across the air–sea interface to the mixed layer below and the evolution of the mixed layer itself. It is at times when turbulence is most intense that most of the air–sea transfers and most of the mixed layer modification occurs. Langmuir Circulation

Langmuir circulations are coherent structures within the mixed layer that produce counter rotating vortices with axes aligned parallel to the wind. Their surface signature is familiar as windrows: lines of bubbles and surface debris aligned with the wind that mark the convergence zones between the vortices. These convergence zones are sites of downwind jets in the surface current. They concentrate bubble clouds produced by breaking waves, or bubbles produced by rain, which are then carried downward, enhancing gas-exchange rates with the atmosphere. Acoustical detection of bubbles provides an important method for examining the structure and evolution of Langmuir circulations. Langmuir circulations appear to be intimately related to the Stokes drift, a small net current parallel to the direction of wave propagation, generated by wave motions. Stokes drift is concentrated at the surface and is thus vertically sheared. Small perturbations in the wind-driven surface current generate vertical vorticity, which is tilted toward the horizontal (downwind) direction by the shear of the Stokes drift. The result of this tilting is a field of counterrotating vortices adjacent to the ocean surface, i.e., Langmuir cells. It is the convergence associated with these vortices that concentrates the wind-driven surface current into jets. Langmuir cells thus grow by a process of positive feedback. Ongoing acceleration of the surface current by the wind, together with convergence of the surface current by the Langmuir cells, provides a continuous source of coherent vertical vorticity (i.e., the jets), which is tilted by the mean shear to reinforce the cells. Downwelling speeds below the surface convergence have been observed to reach more than 0.2 m s1, comparable to peak downwind horizontal flow speeds. By comparison, the vertical velocity scale associated with convection, w ¼ ðJb0 DÞ1=3 is

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closer to 0.01 m s1. Upward velocities representing the return flow to the surface appear to be smaller and spread over greater area. Maximum observed velocities are located well below the sea surface but also well above the mixed-layer base. Langmuir circulations are capable of rapidly moving fluid vertically, thereby enhancing and advecting the turbulence necessary to mix the weak near-surface stratification which forms in response to daytime heating. However, this mechanism does not seem to contribute significantly to mixing the base of the deeper mixed layer, which is influenced more by storms and strong cooling events. In contrast, penetration of the deep mixed layer base during convection (driven by the conversion of potential energy of dense fluid plumes created by surface cooling/evaporation into kinetic energy and turbulence) is believed to be an important means of deepening the mixed layer. So also is inertial shear, as explained next. Wind-Driven Shear

Wind-driven shear erodes the thermocline at the mixedlayer base. Wind-driven currents often veer with depth due to planetary rotation (cf. the Ekman spiral). Fluctuations in wind speed and direction result in persistent oscillations at near-inertial frequencies. Such oscillations are observed almost everywhere in the upper ocean, and dominate the horizontal velocity component of the internal wave field. Because near-inertial waves dominate the vertical shear, they are believed to be especially important sources of mixing at the base of the mixed layer. In the upper ocean, near-inertial waves are generally assumed to be the result of wind forcing. Rapid diffusion of momentum through the mixed layer tends to concentrate shear at the mixed layer base. This concentration increases the probability of small-scale instabilities. The tendency toward instability is quantified by the Richardson number, Ri ¼ N2/S2, where N2 ¼ (g/r) dr/dz, represents the stability of the water column, and shear, S, represents an energy source for instability. Small values of Ri (o1/4) are associated with Kelvin–Helmholtz instability. Through this instability, the inertial shear is concentrated into discrete vortices (Kelvin–Helmholtz billows) with axes aligned horizontally and perpendicular to the current. Ultimately, the billows overturn and generate small-scale turbulence and mixing. Some of the energy released by the instabilities propagates along the stratified layer as high frequency internal gravity waves. These processes are depicted in Figure 2. The mixing of fluid from below the mixed layer by inertial shear contributes to increasing the density of the mixed-layer and to mixedlayer deepening.


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Temperature Ramps

Another form of coherent structure in the upper ocean has been observed in both stable and unstable conditions. In the upper few meters temperature ramps, aligned with the wind and marked by horizontal temperature changes of 0.1 K in 0.1 m, indicate the upward transport of cool/warm fluid during stable/unstable conditions. This transport is driven by an instability triggered by the wind and perhaps similar to the Kelvin–Helmholtz instability discussed above. It is not yet clearly understood. Because it brings water of different temperature into close contact with the surface, and also because it causes large lateral gradients, this mechanism appears to be a potentially important factor in near-surface mixing.

Effects of Precipitation Rainfall on the sea surface can catalyze several important processes that act to both accentuate and reduce upper oceanmixing. Drops falling on the surface disrupt the viscous boundary layer, andmay carry air into the water by forming bubbles. Rain is commonly said to ‘knock downthe seas.’ The evidence for this is the reduction in breaking wave intensity and whitecapping at the sea surface. Smaller waves (o20 cm wavelength) may be damped by subsurface turbulence as heavy rainfall actsto transport momentum vertically, causing drag on the waves. The reduced roughness of the small-scale waves reduces the probability of the waves exciting flow separation on the crests of the long waves, and hence reduces the tendency of the long waves to break. While storm winds generate intense turbulence near the surface, associated rainfall can confine this turbulence tothe upper few meters, effectively insulating the water below from surface forcing. This is due to the low density of fresh rainwater relative to the saltier ocean water. Turbulence must work against gravity to mix the surface water downward, and turbulent mixing is therefore suppressed. So long asvertical mixing is inhibited, fluid heated during the day will be trapped near the sea surface. Preexisting turbulence below the surface will continue to mixfluid in the absence of direct surface forcing, until it decays due to viscous dissipation plus mixing, typically over the time scale of a buoyancy period, N1. Deposition of pools of fresh water on the sea surface, such as occurs during small-scale squalls, raises some interesting prospects for both lateral spreading and vertical mixing of the fresh water. In the warm pool area of the western equatorial Pacific, intense squalls are common. Fresh light puddles at the surface cause the surface density field to be

heterogeneous. Release of the density gradient may then occur as an internal bore forming on the surface density anomaly, causing a lateral spreading of the fresh puddle. Highly resolved horizontal profiles of temperature, salinity and density reveal sharp frontal interfaces, the features of which depend on the direction of the winds relative to the buoyancy-driven current. These are portrayed in Figure 3. When the wind opposes the buoyancy current, the density anomaly at the surface is reduced, possibly as a result of vertical mixing in the manner suggested in Figure 3B(B). This mechanism results in a rapid vertical redistribution of fresh water fromthe surface pool and a brake on the propagation of the buoyancy front. Similarly, an opposing ambient current results in shear at the base of the fresh layer, which may lead to instability and consequent mixing. The nature of these features has yet to be clearly established, as has the net effect on upper ocean mixing.

Ice on the Upper Ocean At high latitudes, the presence of an icelayer (up to a few meters thick) partially insulates the oceana Wind

No convection

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Figure 3 Two ways in which the frontal interface of a fresh surface poolmay interact with ambient winds and currents. (A) The case in which the buoyancy-driven current, wind and ambient current are all in the same direction. In this case, the buoyancydriven current spreads and thins unabated. In (B), the buoyancydriven current is opposed by wind and ambient current. In this case, the frontal interface of the buoyancy-driven current may plunge below the ambient dense water, so that convection near the surface intensifies mixing at the frontal interface. Simultaneously, shearforced mixing at the base of the fresh puddle may increase entrainment of dense water from below.



gainst surface forcing. This attenuates the effects of wind forcing on the upper ocean except at the lowest frequencies. The absence of surface waves prevents turbulence due to wave breaking and Langmuir circulation. However, a turbulence source is provided by the various topographic features found on the underside of the ice layer. These range in size from millimeter-scaledendritic structures to 10 m keels, and can generate significant mixing nearthe surface when the wind moves the ice relative to the water below or currents flow beneath the ice. Latent heat transfer associated with melting and freezing exerts a strong effect on the thermal structure of the upper ocean. Strong convection can occur under ice-free regions, in which the water surface is fully exposed to cooling and evaporative salinity increase. Such regions include leads (formed by diverging ice flow) and polynyas (where wind or currents remove ice as rapidly as it freezes). Convection can also be caused by the rejection of salt by newly formed ice, leaving dense, salty water near the surface.

Parameterizations of Upper Ocean Mixing Large-scale ocean and climate models are incapable of explicitly resolving the complex physics of the upper ocean,and will remain so for the foreseeable future. Since upper ocean processes are crucial in determining atmosphere–ocean fluxes, methods for their representation in large-scale models, i.e., parameterizations, are needed. The development of upper-ocean mixing parameterizations has drawn on extensive experience in the more general problem of turbulence modeling. Some parameterizations emphasize generality, working from first principles as much as possible, whereas others sacrifice generality to focus on properties specific to the upper ocean. An assumption common to all parameterizations presently in use is that the upper ocean is horizontally homogeneous, i.e., the goal is to represent vertical fluxes in terms of vertical variations in ocean structure, leaving horizontal fluxes to be handled by other methods. Such parameterizations are referred to as‘one-dimensional’ or ‘column’ models. Column modeling methods may be classified aslocal or nonlocal. In a local method, turbulent fluxes at a given depth are represented as functions of water column properties at that depth. For example, entrainment at the mixed-layer base may be determined solely by the local shear and stratification. Nonlocal methods allow fluxes to be influenced directly by remote events. For example, during nighttime

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convection, entrainment at the mixed-layer base may be influenced directly by changes in the surface cooling rate. In this case, the fact that large convection rolls cannot be represented explicitly in a column model necessitates the nonlocal approach. Nonlocal methods include ‘slab’ models, in which currents and water properties do not vary at all across the depth of the mixed layer. Local representations may often be derived systematically from the equations of motion, whereas nonlocal methods tend tobe ad hoc expressions of empirical knowledge. The most successful models combine local and nonlocal approaches. Many processes are now reasonably wellrepresented in upper ocean models. For example, entrainment via shear instability is parameterized using the local gradient Richardson number and/or a nonlocal (bulk) Richardson number pertaining to the whole mixed layer. Other modeling issues are subjects of intensive research. Nonlocal representations of heat fluxes have resulted in improved handling of nighttime convection, but the corresponding momentum fluxes have not yet been represented. Perhaps the most important problem at present is there presentation of surface wave effects. Local methods are able to describe the transmission of turbulent kinetic energy generated at the surface into the ocean interior. However, the dependence of that energy flux on surface forcingis complex and remains poorly understood. Current research into the physics of wave breaking, Langmuir circulation, wave-precipitation interactions, and other surface wave phenomena will lead to improved understanding, and ultimately to useful parameterizations.

See also Breaking Waves and Near-Surface Turbulence. Bubbles. Deep Convection. Heat and Momentum Fluxes at the Sea Surface. Internal Tides. Langmuir Circulation and Instability. Penetrating Shortwave Radiation. Surface Gravity and Capillary Waves. Three-Dimensional (3D) Turbulence. Under-Ice Boundary Layer. Upper Ocean Vertical Structure. Whitecaps and Foam. Wave Generation by Wind.

Further Reading Garrett C (1996) Processes in the surface mixed layer of the ocean. Dynamics of Atmospheres and Oceans 23: 19--34. Thorpe SA (1995) Dynamical processes at the sea surface. Progress in Oceanography 35: 315--352.


UPPER OCEAN STRUCTURE: RESPONSES TO STRONG ATMOSPHERIC FORCING EVENTS L. K. Shay, University of Miami, Miami, FL, USA & 2009 Elsevier Ltd. All rights reserved.

Introduction Ocean temperature structure changes from profiler and remotely sensed data acquired during hurricane passage have been documented in the literature. These oceanic response measurements have emphasized the sea surface temperature (SST) cooling and deepening of the wind-forced ocean mixed layer (OML). The level of SST cooling and OML deepening process are associated with the oceanic current response, which has two major components (Figure 1). First, the momentum response is associated with the OML current divergence in the nearfield with a net transport away from the storm center. This divergent flow causes upwelling of the isotherms and an upward vertical velocity. Over the next half of the cycle, currents and their transport converge toward the track, forcing downwelling of warmer water into the thermocline. This cycle of upwelling and downwelling regimes occurs over distances of an inertial wavelength and is proportional to the product of the storm translation speed and the local inertial period. Over these distances, horizontal pressure gradients couple the wind-forced OML to the thermocline as part of the three-dimensional cold wake. In the Northern Hemisphere, wind-forced currents rotate anticyclonically (clockwise) with time and depth where the period of oscillation is close to the local inertial period (referred to as near-inertial). This near-inertial current vector rotation with depth creates significant vertical current shears across the OML base and the top of the seasonal thermocline that induces vertical mixing and cooling and deepening of the layer. For these two reasons, the upper ocean current transport and vertical current shear are central to understanding the ocean’s thermal response to hurricane forcing. The SST response, and by proxy the OML temperature response, typically decreases by 1–5 1C to the right of the storm track at one to two radii of maximum winds (Rmax) due to surface wind field asymmetries, known as the ‘rightward bias’. Although warm SSTs (Z26 1C) are required to maintain a

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hurricane, maximum SST decreases and OML depth increases of 20–40 m are primarily due to entrainment mixing of the cooler thermocline water with the warmer OML water. Ocean mixing and cooling are a function of forced current shear (qv/qz ¼ s) that reduce the Richardson number (defined as the ratio of buoyancy frequency (N2) and (s2)) to decrease below criticality. The proportion of these physical processes to the cooling of the OML heat budget are sheardriven entrainment mixing (60–85%), surface heat and moisture fluxes (Qo) (5–15%), and horizontal advection by ocean currents (5–15%) under relatively quiescent initial ocean conditions (no background fronts or eddies). As per Figure 1, vertical motion (upwelling) increases the buoyancy frequency associated with more stratified water that tends to increase the Richardson number above criticality. In strong frontal regimes (e.g., the Loop Current (LC) and warm core rings (WCRs)) with deep OML, cooling induced by these physical processes is considerably less than observed elsewhere. During hurricane Opal’s passage in the Gulf of Mexico (GOM), SST cooling within a WCR was B1 1C compared to B3 1C on its periphery. In these regimes where the 26 1C isotherm is deep (i.e., 100 m), more turbulent mixing induced by vertical current shear is required to cool and deepen an already deep OML compared to the relatively thin OMLs. That the entrainment heat fluxes at the OML base are not significantly contributing to the SST cooling implies there is more heat for the hurricane itself via the heat and moisture surface fluxes. These regimes have less ‘negative feedback’ to atmosphere than typically observed over the cold wake. To accurately forecast hurricane intensity and structure change in coupled models, the ocean needs to be initialized correctly with both warm and cold fronts, rings and eddies observed in the tropical and subtropical global oceans. The objective of this article is to build upon the article by Shay in 2001 to document recent progress in this area of oceanic response to hurricanes with a focus on the western Atlantic Ocean basin. The rationale here is that in the GOM (Figure 1(c)), in situ measurements are more comprehensive under hurricane conditions than perhaps anywhere else on the globe. Second, once a hurricane moves over this basin, it is going to make landfall along the coasts of Mexico, Cuba, and the United States. In the first


193

UPPER OCEAN STRUCTURE: RESPONSES TO STRONG ATMOSPHERIC FORCING EVENTS (c)

(a)

32° N

Mississippi Alabama Louisiana

30° N

−200 m

26° N

WCR Envelope

24° N

Warm Core Ring

−200 m

22° N

Yucatan Straits

Transport

−200 m

Florida Current Cuba

96° W

92° W

88° W

84° W

80° W

xu

′

*

h ∆h

(z)

v mixing z

w Thermocline −8

Loop Current

Northwest Caribbean Sea

Mexico

20° N 100° W Qo

Florida

Texas

28° N

(b)

Georgia

−6

−4

−2

Upwelling 0 R max

2

4

6

8

Figure 1 (a) Tropical cyclone image and (b) a cross-section schematic of the physical processes that alter the OML depth (h: light gray line) forced by hurricane winds (u *) such as shear-induced mixing (qv/qz ¼ shear) and OML depth changes (Dh: dark gray line), upwelling (w) due to transport (arrows) by currents away from the storm center relative to the surface depression (Z0 ), and surface heat fluxes (Qo) from the ocean to the atmosphere, all of which may contribute to ocean cooling during TC passage. (c) States and countries surrounding the Gulf of Mexico and northwest Caribbean Sea and identification of the key oceanic features and processes and areas relative to the 200-m isobath. (a, b) Adapted from Shay LK (2001) Upper ocean structure: Response to strong forcing events. In: Weller RA, Thorpe SA, and Steele J (eds.) Encyclopedia of Ocean Sciences, pp. 3100–3114. London: Academic Press.

section following this, progress on understanding the wind forcing and the surface drag coefficient behavior at high winds is discussed within the context of the bulk aerodynamic formula. In the next section, the importance of temperature, current, and shear measurements with respect to model initialization are described. While cold wakes are usually observed in relatively quiescent oceans (i.e., hurricanes Gilbert (1988); Ivan, Frances (2004)), the oceanic response is not nearly as dramatic in warm features. This latter point has important consequences for coupled models to accurately simulate the atmospheric response where the sea–air transfers (e.g., surface fluxes) may not decrease to significant levels as observed over cold wakes. These physical processes for oceanic response are briefly documented here for recent hurricanes such as the LC, WCR, and cold core ring (CCR) interactions and coastal ocean response during hurricanes Lili in 2002, Ivan in 2004, and Katrina and Rita in 2005. Concluding remarks as well as suggested avenues for future research efforts are in the final section.

Atmospheric Forcing Central to the question of storm forcing and the ocean response is the strength of the surface wind stress and the wind stress curl defined at 10 m above the surface. Within the framework of the bulk aerodynamics formula, the wind stress is given by t ¼ ra cd W10 ðu10 i þ v10 jÞ where ra is the air density, cd is the surface drag coefficient, the magnitude of the 10-m wind (W10 ¼ O(u10 2 þ v10 2 ), where u10 and v10 represent the surface winds at 10 m in the east (i) and north (j) directions, respectively). Momentum transfer between the two fluids is characterized by the variations of wind speed with height and a surface drag coefficient that is a function of wind speed and surface roughness. It is difficult to acquire flux measurements for the high wind and wave conditions under the eyewall at 10 m; however, profilers have been deployed from


194

UPPER OCEAN STRUCTURE: RESPONSES TO STRONG ATMOSPHERIC FORCING EVENTS

aircraft to measure the Lagrangian wind profiles in hurricanes. These profiler data suggest a logarithmic variation of mean wind speed in the lowest 200 m of the boundary layer. Based on this variation, the surface wind stress, roughness length, and neutral stability drag coefficient determined by the profile method indicate a leveling of the surface momentum flux as winds increase above hurricane force with a slight decrease of the drag coefficient with increasing winds. Donelan and colleagues found the characteristic behavior cd since surface conditions change from aerodynamically smooth to aerodynamically rough (cd increasing with wind speed) conditions. In rough flow, the drag coefficient is related to the height of the ‘roughness elements’ per unit distance downwind or the spatial average of the downwind slopes. In a hurricane, rapid changes in wind speed and direction occur over short distances compared to those required to approach full-wave development. The largest waves in the wind-sea move slowly compared to the wind and travel in directions differing from the surface winds. Under such circumstances, longer waves contribute to the roughness of the sea and a ‘saturation’ of the drag coefficient occurs after wind speeds exceed 33 m s 1 (Figure 2). Beyond this threshold, the surface does not become any rougher. These results suggest that there may be a limiting state in the aerodynamic roughness of the sea surface.

× 10−3

5

The oceanic response is usually characterized as a function of storm translation speed (Uh), radius of maximum winds (Rmax), surface wind stress at 10-m level (tmax), OML depth (h), and the strength of the seasonal thermocline either by reduced gravity (g0 ¼ g(r2 – r1)/r2 where r1 is the density of the upper layer of depth h1, and r2 is the density in the lower layer of depth h2 where r24r1) or buoyancy frequency (see Table 1). The latitude of the storm sets the local planetary vorticity through the local Coriolis parameter (f ¼ 2O sin(j), where O is the angular rotation rate of the Earth (7.29 10 5 s 1), and j is the latitude). The inverse of the local Coriolis parameter (f 1) is a fundamental timescale referred to as the inertial period (IP ¼ 2pf 1). The local IPs decrease poleward, for example, at 101 N, IP B70 h, at 241 N IP B30 h, and at 351 N IP B20 h. The relative importance of this parameter cannot be overemphasized in that at low latitudes such as the eastern Pacific Ocean (EPAC) warm pool, the nearinertial current and shear response will require over a day to develop during hurricane passage. By contrast, at the mid-latitudes, near-inertial motions will develop significant shears across the base of the OML much more quickly. Thus, the initial SST cooling and OML deepening will be minimal at lower latitudes compared to the mid-latitudes for the same oceanic stratification and storm structure.

Measured drag coefficients by various methods

Green squares = profile method (Ocampo-Torres et. al., 1994) Blue asterisks = profile method (Donelan et. al., 2004) Red circles = surface slope (Donelan et. al., 2004) Magenta dots = dissipation (Large and Pond, 1981)

4.5 Drag coeff. referred to 10 m

Air–Sea Parameters

4 3.5 3 2.5

∗

2

∗

∗ ∗

∗

1.5

∗

1

∗

0.5 0

0

5

10 15 20 25 30 35 40 Wind speed extrapolated to height of 10 m, U10 (m s−1)

45

50

Figure 2 Laboratory measurements of the neutral stability drag coefficient (10 3) by profile, eddy correlation (‘Reynolds’), and momentum budget methods. The drag coefficient refers to the wind speed measured at the standard anemometer height of 10 m. The drag coefficient formula of Large and Pond (1981) is also shown along with values from Ocampo-Torres et al. (1994) derived from field measurements. From Donelan MA, Haus BK, Reul N, et al. (2004) On the limiting aerodynamic roughness of the ocean in very strong winds. Geophysical Research Letters 31: L18306, figure 2 (doi: 1029/2004GRL019460).



195

Table 1 Air–sea parameters and scales for hurricanes Lili (2002) for both the LC and GOM common water, and Ivan (2004), Katrina (2005), and Rita (2005) over the GOM basin Parameter

Lili (LC)

Lili (GOM)

Ivan

Katrina

Rita

Radius of max. winds Max. wind stress Translational speed Wavelength Mixed layer depth Inertial period Thermocline thickness Barotropic phase speed Barotropic deformation radius Baroclinic phase speed Baroclinic deformation radius

Rmax (km) tmax (N m 2) Uh (m 1 s) L (km) h (m) IP (d) b (m) c0 (m 1 s) a0 (km) c1 (m 1 s) a1 (km)

25 7.1 6.9 770 110 1.3 200 120 2100 1.5 26

18 8.0 7.7 775 35 1.16 200 150 2400 2.8 46

32 6.7 5.5 594 35 1.25 200 72 1002 2.8 40

42 7.6 6.3 608 74 1.12 200 147 2250 2.5 38

19 8.7 4.7 454 70 1.12 200 150 2300 1.9 29

Froude number (Fr)

Uh/c1

2.5

2.8

2.2

2.5

2.5

Note that these parameters are based on where measurements were acquired; for example, Ivan moved over the DeSoto Canyon and over the shelf compared to Lili moving over the eastern side of the Yucatan Shelf, then into the central GOM. Katrina and Rita scales are based on the north-central GOM.

Ocean Structure

An important parameter governing the response is the wave phase speed of the first baroclinic mode due to oceanic density changes between the OML and the thermocline. In a two-layer model, both barotropic and baroclinic modes are permitted. The barotropic (i.e., depth-independent) mode is referred to as the external mode whereas the first baroclinic (depthdependent) mode is the first internal mode associated with vertical changes in the stratification. The phase speed of the first baroclinic mode (c1) is given by c1 2 ¼ g0 h1 h2 =ðh1 þ h2 Þ where the depth of the upper layer is h1, and the depth of the lower layer is h2. In the coastal ocean, phase speeds range from 0.1 to 0.5 m s 1, whereas in the deep ocean, this phase speed ranges between 1 and 3 m s 1 depending on the density contrast between the two layers. The barotropic mode has a phase speed c0 ¼ OgH where H represents the total depth (h1 þ h2), and is typically 100 times larger than the first baroclinic mode phase speed. An important nondimensional number for estimating the expected baroclinic response depends on the Froude number (ratio of the translation speed to the first baroclinic mode phase speed Uhc1 1). If the Froude number is less than unity (i.e., stationary or slowly moving storms), geostrophically balanced currents are generated by the positive wind stress curl causing an upwelling of cooler water induced by upper ocean transport directed away from the storm track (Figure 1). When the hurricane moves faster than the first baroclinic mode

phase speed, the ocean response is predominantly baroclinic associated with upwelling and downwelling of the isotherms and the generation of strong nearinertial motions in a spreading three-dimensional wake. The predominance of a geophysical process also depends upon the deformation radius of the first baroclinic mode (a1 1) defined as the ratio of the first mode phase speed (c1) and f. In the coastal regime, the deformation radius is 5–10 km, but in deeper water, it increases to 20–50 km due to larger phase speeds. For observed scales exceeding the deformation radius, Earth’s rotational effects, through the variations of f, dominate the oceanic dynamics where timescales are equal or greater than IP. Thus the oceanic mixed layer response to hurricanes is characterized as rotating, stratified shear flows forced by winds and waves. Basin-to-basin Variability

Profiles from the background GOM, LC subtropical water, and the tropical EPAC are used to illustrate differences in the buoyancy frequency profile (Figure 3). In an OML, the vertical density gradients (N) are essentially zero because of the vertical uniformity of temperature and salinity. Maximum buoyancy frequency (Nmax) in the Gulf is 12–14 cycles per hour (cph) located between the mixed layer depth (40 m) and the top of the seasonal thermocline compared to c. 5–6 cph in the LC water mass distributed over the upper part of the water column. In the EPAC, however, Nmax B20 cph due to the sharpness of the thermocline and halocline


196

UPPER OCEAN STRUCTURE: RESPONSES TO STRONG ATMOSPHERIC FORCING EVENTS (a)

(b)

0

Depth (m)

100

200

300 EPAC GOM LC

400

500 (c)

0

5

10 15 20 Temperature (°C)

25

30

33

34

(d)

0

35 36 Salinity (psu)

37

Depth (m)

100

200

300

400

500 1020

1022

1024

1026

Density (kg

1028

1030

0

5

m−3 )

10

15

20

25

N (cph)

Figure 3 (a) Temperature (1C), (b) salinity (practical salinity units, psu), (c) density (kg m 3), and (d) buoyancy frequency (N : cycles per hour) profiles from the eastern Pacific Ocean (red) , the GOM common water (green), and the LC water (blue) as measured from airborne expendable ocean profilers. Notice the marked difference between the gradients at the base of the OML between the three profiles.

(pycnocline) located at the base of the OML (i.e., 30 m). Beneath this maximum, buoyancy frequencies (Z3 cph) are concentrated in the seasonal thermocline over an approximate thermocline scale (b) of 200 m and exponentially decay with depth approaching 0.1 cph. In the LC water, Nmax ranges from 4 to 6 cph and remains relatively constant, and below the 20 1C isotherm depth (B250 m), buoyancy frequency decreases exponentially. The Richardson number increases with increases in the buoyancy frequency for a given current shear (s). This implies that a higher shear is needed in a regime like the EPAC to lower the Richardson number to below-critical values for the upper ocean to mix and cool compared to the water mass in the

GOM. Given a large N at lower latitudes (121 N) where the IP is long (B58 h) in the EPAC warm pool, SST cooling and OML deepening will be much less than in the GOM as observed during hurricane Juliette in September 2001 (not shown). Significant SST cooling of more than 5 1C only occurred when Juliette moved northwest where Nmax decreased to B14 cph at higher latitudes. Levels of SST cooling similar to those for the same hurricane in the GOM would be observed in the common water but not in the LC water mass since the 26 1C isotherm depth is 3–4 times deeper. These variations in the stratification represent a paradox for hurricane forecasters and are the rationale underlying the use of satellite radar altimetry in mapping isotherm depths and



estimating oceanic heat content (OHC) from surface height anomalies (SHAs) and assimilating them into oceanic models.

the 26 1C isotherm are large. In the LC regime, for example, this isotherm may be deeper than 150 m whereas in the common water the 26 1C isotherm is located at 40 m. The corresponding OHC relative to the 26 1C isotherm is given by

Gulf of Mexico Basin Warm subtropical water is transported poleward by upper-ocean currents from the tropics through the Caribbean Sea and into the GOM (see Figure 1(c)). This subtropical water exits the northwestern Caribbean Sea through the Yucatan Straits where the transport of B24 Sv (1 Sv ¼ 106 m3 s 1) forms the LC core. Given upper ocean currents B1 m s 1 of the LC, horizontal density gradients between this ocean feature and surrounding GOM common water occur over smaller scales due to markedly different temperature and salinity structure (Figure 4). Variations in isotherm depths and OHC values relative to In situ observations

OHC ¼ cp

Derived

26 dz

Temperature (°C), 19.7° N, 85.0° W

0

GDEM3 Climatology WOA01 Climatology Pre-Isidore MODAS Pre-Isidore, measured Pre-Isidore HYCOM-OI Pre-Isidore HYCOM-MODAS

50

20° N

r½TðzÞ

where cp is specific heat at constant pressure, D26 is the 26 1C isotherm depth, and OHC is zero wherever SST is less than 26 1C. Within the context of a twolayer model approach and a ‘hurricane season’ climatology, the 26 1C isotherm depth and its OHC relative to this depth are monitored using satellite techniques by combining SHA fields from satellite altimeters onboard the NASA Jason-1, US Navy Geosat Follow On, and European Research Satellite-2

24° N

20° N

D26 Z 0

Depth (m)

24° N

197

100 150 200 250

88° W

84° W

80° W

88° W

H-OI

84° W

80° W

300

18

H-MODAS

24° N

20° N

88° W

84° W

0

80° W

88° W

84° W

OHC (kJ cm−2), 19 Sep. 2002 50 100 150

30

Salinity (psu), 19.7° N, 85.0° W GDEM3 Climatology WOA01 Climatology Pre-Isidore MODAS Pre-Isidore, measured Pre-Isidore HYCOM-OI Pre-Isidore HYCOM -MODAS

50 Depth (m)

20° N

26 T (°C)

0 24° N

22

80° W

100 150 200 250 300 35.75

36

36.25 36.5 S (psu)

36.75

37

Figure 4 OHC (kJ cm 2) in the northwest Caribbean Sea and southeast GOM from an objective analysis of in situ aircraft measurements, satellite altimetry, HYCOM NRL-CH nowcast, and HYCOM NRL-MODAS nowcast (four left panels). Temperature (right top) and salinity (right bottom) vertical profiles at a location in the northwest Caribbean Sea, where red lines are climatological profiles (GDEM3 dashed, WOA01 solid), solid blue lines are observed profiles, dashed blue lines are MODAS profiles, and black lines are model nowcasts (HYCOM-NRL dashed and HYCOM-MODAS solid). Adapted from Halliwell GR, Jr., Shay LK, Jacob SD, Smedstad OM, and Uhlhorn EW (in press) Improving ocean model initialization for coupled tropical cyclone forecast models using GODAE nowcasts. Monthly Weather Review.


198


(and Envisat) missions with observed SSTs. In the 1970s, Leipper coined the phrase ‘hurricane heat potential’, which represents integrated thermal structure relative to 26 1C water. In the LC regime, OHC values relative to this isotherm depth often exceed 100 kJ cm 2. For oceanic response studies, the key science issue is that such deep isotherms (and OHC levels) tend to be resistive to significant storm-induced cooling by forced near-inertial current shears across the base of deep OML. Loop Current Cycle

The LC is highly variable and when it penetrates beyond latitudes of 251 N, WCR shedding events occur at periods of 6–11 months when CCRs are located on their periphery prior to separation. By contrast, south of this latitude, WCR shedding periods increase to more than 17 months based on a series of metrics developed by Leben and colleagues. These WCRs, with diameters of approximately 200 km, then propagate west to southwest at average phase speeds of B5 km day 1, and remain in the GOM for several months. At any given time, two or three WCRs may be embedded within the complex GOM circulation patterns. Theoretical developments suggest that the LC cycle can be explained in terms of the momentum imbalance paradox theory. This theory predicts that when a northward-propagating anomalous density current (i.e., Yucatan Current) flows into an open basin (GOM) with a coast on its right (Cuba), the outflow balloons near its source forming a clockwiserotating bulge (e.g., LC) since the outflow cannot balance the along-shelf momentum flux after turning eastward. The ballooning of the current satisfies the momentum flux balance along the northern Cuban coast. The subsequent WCR separation from the LC is due to the planetary vorticity gradients where most of the inflow forces a downstream current and the remaining inflow forms a warm ring. Subtropical water emerging from the Caribbean Sea may enter the LC bulge prior to shedding events and impact the OHC distribution, and if in phase with the height of hurricane season may spell disaster for residents along the GOM. Model Initialization

Ocean models that assimilate data are an effective method for providing initial and boundary conditions in the oceanic component of coupled prediction models. The thermal energy available to intensify and maintain a hurricane depends on both the temperature and thickness of the upper ocean

warm layer. The ocean model must be initialized so that features associated with relatively large or small OHC are in the correct locations and T–S (and density) profiles, along with the OHC, are realistic. Ocean forecast systems based on the hybrid coordinate ocean model (HYCOM) have been evaluated in the northwest Caribbean Sea and GOM for September 2002 prior to hurricanes Isidore and Lili, and in September 2004 prior to Ivan. An examination of the initial analysis prior to Isidore is from an experimental forecast system in the Atlantic basin (Figure 4). This model assimilates altimeter-derived SHAs and SSTs. Comparison of OHC maps by the model and observations demonstrate that the analysis (labeled NRL-CH) reproduces the LC orientation but underestimates values of the heat content. In the Caribbean Sea, the thermal structure (T(z)) hindcast tends to follow the September ocean climatology but does not reproduce the larger observed OHC values. The model ocean is less saline than both climatology and profiler measurements above 250 m and less saline than those between 250 and 500 m. Evaluations of model products are needed prior to coupling to a hurricane model to insure that ocean features are in the correct locations with realistic structure. Mixing Parametrizations

One of the significant effects on the upper ocean heat budget and the heat flux to the atmosphere is the choice of entrainment mixing parametrizations at the OML base (see Figure 1). Sensitivity tests have been conducted using five schemes: K-profile parametrization (KPP); Goddard Institute for Space Studies level-2 closure (GISS); Mellor–Yamada level2.5 turbulence closure scheme (MY); quasi-slab dynamical instability model (Price–Weller–Pinkel dynamical instability model, PWP); and a turbulent balance model (Kraus–Turner turbulence balance model, KT). Simulated OML temperatures for realistic initial conditions suggest similar response except that the magnitude of the cooling differs as well as its lateral extent of the cooling patterns (Figure 5). Three higher-order turbulent mixing schemes (KPP, MY, and GISS) seem to be in agreement with observed SST cooling patterns with a maximum of 4 1C whereas PWP (KT) over- (under-) estimate SST cooling levels after hurricane Gilbert. This case is an example of ‘negative feedback’ to the atmosphere given these cooling levels due primarily to shear instability at the OML base. Similar to the post-season hurricane forecast verifications, more oceanic temperature, current, and salinity measurements must be acquired to evaluate these schemes to build a larger


UPPER OCEAN STRUCTURE: RESPONSES TO STRONG ATMOSPHERIC FORCING EVENTS Longitude (W) 96°

94°

92°

90°

88°

86°

30°

30°

28°

28°

26°

26°

24°

24°

22°

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(c)

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26°

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31 30 29 28 27 26 25 24 23 22 21

28° 26° 24° 22° 20° 96°

94°

92°

90°

88°

Temperature (°C)

Latitude (N)

(a)

199

86°

Figure 5 Simulated mixed layer temperatures during hurricane Gilbert for mixing schemes (a) KPP, (b) PWP, (c) KT, (d) MY, and (e) GISS. Differences between these five cases are visible with PWP being the coolest and KT being the warmest. Black line indicates track of the storm at 06 GMT 16 Sep. 1988.

statistical base for the oceanic response to high-wind conditions in establishing error bars for the models.

Oceanic Response Recently observed interactions of severe hurricanes (category 3 or above) with warm ocean features such

as the LC and WCR (Lili in October 2002, Katrina and Rita in August and September 2005) are contrasted with hurricanes that interact with CCR (Ivan in September 04) and cold wakes (Gilbert in September 1988) in the GOM. The levels of observed upper cooling and OML depth patterns are predicated on the amount of shear-induced mixing in the upper ocean (see Figure 1). The SST response is


200


determined from an optimal interpolation scheme using the NASA TRMM microwave imager (TMI) and advanced microwave sensing radiometer (AMSR- E) where the diurnal cycle was removed from the data. LC Interactions

Hurricanes Isidore (21 September 2002) and Lili (02 October 2002) interacted with the LC in nearly the same area spaced about 10 days apart. For negative feedback regimes, one would anticipate that after the first hurricane, there would have been a significant ocean response with little thermal energy available for the second storm as Isidore moved slowly from Cuba to the Yucatan Peninsula. The cyclonic (counterclockwise) rotating surface wind stress (in the Northern Hemisphere) should have upwelled isotherms due to divergent wind-driven transport that may have been balanced by horizontal advection due to strong northward currents through the Yucatan Straits. While observed cooling levels in the straits were less than 1 1C, the upper ocean cooled by (a) 29 Sep. 2002

4.5 1C over the Yucatan Shelf. Since upwelling induced by the persistent trade wind regime maintains a seasonal thermocline close to the surface over this shelf, impulsive wind events force upwelling of colder thermocline water quickly due to transport away from the coast. Isidore remained over the Yucatan Peninsula and weakened to a tropical storm that then moved northward creating a cool wake of B28.5 1C SSTs across the central GOM. Lili reached hurricane status on 26 September while passing over the Caribbean Sea along a similar northwest trajectory as Isidore, making a first landfall along the Cuban coast (Figure 6). As Lili moved into the GOM basin, the storm intensified to a category 4 storm along the LC boundary just as Rmax decreased to form a new eyewall (where winds are a maximum). In the common water, the SST cooling was more than 2 1C due to shear-induced mixing compared to less than 1 1C SST cooling in the LC (Figure 6(c)). This suggests that ‘less negative feedback’ (minimal ocean cooling) to hurricane Lili occurred over the LC than over the common water. Afterward, Lili began a weakening cycle to category

31

32.0° N

30

30.0° N 28.0° N

29

26.0° N

28

24.0° N

27

22.0° N

26

20.0° N

25

(c) Post-pre

−0.5

32.0° N 30.0° N 28.0° N 26.0° N 24.0° N 22.0° N 20.0° N 100° W

(b) 3 Oct. 2002

100° W

96° W

92° W

88° W

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80° W

(d) In situ sampling

−1 TD TS H1 H2 H3 H4 H5 96° W

Loop Current

−1.5

−2

92° W

88° W

84° W

80° W

XBT XCP XCT

−2.5

Figure 6 (a) Pre-Lili, (b) post-Lili, (c) pre–post-Lili SST (1C) field from AVHRR data, courtesy of RSMAS Remote Sensing Laboratory, and (d) measurement grid conducted by NOAA research aircraft on 2 Oct. 2002 (open symbols represent nonfunction probes). Panel (c) is relative to the track and intensity of Lili and the position of the LC. Notice the cold wake in the GOM common water compared to essentially no cold wake in the LC. More details of the response in the LC is given in Figure 9. Black box represents the region where in situ measurements from aircraft expendable were acquired during Lili’s passage.



30

4 0

60 28.5

0

4 8 X/R max

0

40

0

80

5

−1.

−0.

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0

4 8 X/R max

12

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27.5

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4 8 X /R max

−3 −10 −40 0

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0

−10

−4 −8

20

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0

4 0

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0

29

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.5

0

−5 80

10

−30

28

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.5

0

0

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4 10

0

−30

−1

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28

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−1

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suggestive of ‘less negative feedback’ to the storm as it crossed over the Yucatan Straits. Given the 10-day time interval between Isidore and Lili, pre-Lili SSTs warmed to over 29 1C in the experimental domain. After Lili’s passage, SSTs decreased to 28.5 1C in the LC; however, along the northern extremity of the measurement domain, SSTs cooled to 27 1C in the Gulf of Mexico common water (GCW), which equates to more than 2 1C cooling. The GCW mixes quickly due to current shears across the OML base forcing the layer to deepen. In the LC itself, there was little evidence of cooling and layer deepening. Given the advective timescale (LV 1 where L is cross-stream scale and V is the maximum current of the LC) of about a day, heat transport from the Caribbean Sea occurs rapidly and will offset temperature decreases induced by upwelling of the isotherms and mixing as in the hurricane Isidore case. The observed current shears during the hurricane were 1.5 10 2 s 1 or about a factor of 2–3 less

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1 status due to enhanced atmospheric shear, dry-air intrusion along the western edge, and interacting with the shelf water cooled by Isidore. As shown in Figure 6(d), oceanic and atmospheric profilers were deployed in the south-central part of the GOM from research aircraft. The design strategy was to measure upper-ocean response to a propagating and mature hurricane over the LC. Multiple research flights deployed profilers in the same location before, during, and after passage, which captured not only the LC response to Lili but also to Isidore as the hurricane intensified to category 3 status moving across the Yucatan Straits 10 days early. The minimal LC response highlights the importance of this current system for intensity changes. These profiler data were objectively analyzed over a 31 31 domain in latitude and longitude with a vertical penetration to 750-m depth and aligned with the hurricane path (Figure 7). A day after Isidore, SSTs that remained were above 28 1C, which is

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Figure 7 Objective analysis of SSTs (1C) and MLDs (m). Left columns are SSTs and DSSTs and right columns show MLDs and DMLDs for pre-storm, storm, and post-storm (Wake 1) measurements from Lili in the southeastern GOM as per Figure 6(c). Panels are in storm-coordinate system for cross-track (X/Rmax) and along-track (Y/Rmax) based on Rmax and the storm track orientation at 292 1 T North as in Figure 6(c) centered at 23.21 N and 86.11 W. The DSST (1C) and DMLD (m) are estimated by subtracting the prestorm data from the storm and post-storm data and the arrows represent current measurements from airborne expendable current profilers. Blue shaded areas are more than 2 1C consistent with satellite-derived SSTs in Figure 6(c).


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than observed previously in quiescent regimes due to the strength of this background upper ocean flow of the LC. This lack of shear-induced mixing has implications for hurricane intensity as they move over the deep, warm pools of the LC, which represent a reservoir of thermal energy for hurricanes to tap. Cold and Warm Core Ring Interactions

Hurricane Ivan (Sep. 04) entered the GOM as a category 5 storm and then weakened to a category 4 storm due to a combination of lower OHC, vertical shear in the atmosphere associated with an upperlevel trough, and drier air being drawn into its circulation. During its GOM trajectory, Ivan encountered two CCRs and a WCR where the surface pressure decreased by B10 mb during a brief encounter. Shelf water, cooled by hurricane Frances (10 days earlier) along the northern GOM along with increasing atmospheric shear, acted to oppose intensification during an eyewall replacement cycle (defined as the formation of a secondary eyewall that replaces a collapsing inner eyewall). As shown in Figure 8, pre- and post-SSTs to Ivan reveal the location of both WCR and CCR located

along the track of hurricane Ivan and the cold wake due to enhanced current shear instability. The SST difference field, shown in Figure 8(c), indicates that both the WCR and CCR SSTs are eroded away by the strong forcing. The SSTs over the CCRs indicate cooling levels exceeding 4 1C along and to the right of Ivan’s track that were embedded within the cool wake of about 3.5 1C of Ivan. The northern CCR may have been partially responsible for the observed weakening of Ivan as suggested by Walker and colleagues. Notice that just as in the case of Lili, SST cooling of less than 1 1C was observed in the LC in the southern part of the basin. Prior to landfall, Ivan moved over 14 acoustic Doppler current profiler (ADCP) moorings that were deployed as part of the Slope to Shelf Energetics and Exchange Dynamics (SEED) project (Figure 8(d)), as discussed by Teague and colleagues. These profiler measurements provided the evolution of the current (and shear) structure from the deep ocean across the shelf break and over the continental shelf. The current shear response, estimated over 4-m vertical scales, is shown in Figure 9 based on objectively analyzed data from these moorings. Over the shelf, the current shears increased due to hurricane Ivan strong

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winds. The normalized shear magnitude over the shelf (depths of 100 m) is larger by a factor of 4 compared to normalized values over the deeper part of the mooring array (500–1000 m). Notice that the current shear rotates anticyclonically (clockwise) in time over 6-h intervals associated with the forced near-inertial response (periods slightly shorter than the local inertial period). In this measurement domain, the local inertial period is close to 24 h which is close to the diurnal tide. By removing the relatively weak tidal currents and digitally filtering the records, the analysis revealed that the predominant response was due to

forced near-inertial motions. These motions have a characteristic timescale for the phase of each mode to separate from the wind-forced OML current response when the wind stress scale (2Rmax) exceeds the deformation radius associated with the first baroclinic mode (B40 km). This timescale increases with the number of baroclinic modes due to decreasing phase speeds. The resultant vertical energy propagation from the OML response is associated with the predominance of the anticyclonic (clockwise) rotating energy with depth and time that is about 4 times larger than the cyclonic (counterclockwise) rotating component.


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the LC and rapidly intensified to similar intensity as Katrina. After Rita interacted with the eastern tip of the WCR, the hurricane began a weakening cycle due to the cooler water associated with a CCR located on the periphery of the WCR similar to Ivan and cooler water on the shelf. Pre- and post-SST analyses include an interval a few days prior and subsequent to hurricane passage to quantify cooling levels in the oceanic response (Figure 11). Prior to Katrina, SSTs exceeded 31 1C in the GOM without any clear evidence of the LC. Subsequent to Katrina, maximum cooling occurred on the right side of the track with SST decreasing to about 28 1C over the outer West Florida Shelf where OML typically lies close to the surface. Observed SSTs decreased by more than 4 1C along the LC’s periphery, mainly due to shear-induced mixing and upwelling over the shelf. As Katrina moved over the LC, the SST response was less than 2 1C as expected

In 2005, hurricane Katrina deepened to a category 5 storm over the LC’s western flank with an estimated wind stress of B7 N m 2. The variations of Katrina’s intensity correspond well with the large OHC values in the LC and the lobe-like structure (eventually a WCR) in the northern GOM. Since SSTs exceeding 30 1C were nearly uniformly distributed in this regime, the LC structure was not apparent in the SST signals. This deeper heat reservoir of the LC provided more heat for the hurricane where satellite-inferred OHC values exceeded 120 kJ cm 2 or more than 5 times the threshold suggested by early studies to sustain a hurricane. Within the next 2 weeks, Rita formed and moved through the Florida Straits into the GOM basin (Figure 10(c)). While Rita’s path did not exactly follow Katrina’s trajectory in the south-central Gulf, Rita moved toward the north-northwest over (a)

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UPPER OCEAN STRUCTURE: RESPONSES TO STRONG ATMOSPHERIC FORCING EVENTS (a) 25 Aug. 2005

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over the deeper subtropical water, consistent with the weaker LC response to Lili. These deeper warm pools tend to resist the development of strong shear-induced mixing episodes. Similarly, pre-Rita SSTs ranged from 28.5 to 29 1C over most of the GOM except for the shelf waters cooled by Katrina. However, after Rita’s passage, the dramatic SSTs cooling of 3–4 1C occurred because of the combination of upwelling and cold water advection associated with a CCR that moved between the WCR and the LC. This scenario was analogous to the Ivan case with the CCRs embedded in the cold wake. To illustrate this effect, oceanic profiler measurements were acquired on 15 and 26 September 2005 in a pattern centered on the LC and the lobe-like structure that eventually became the WCR. The earlier research flight was originally conceived as a post-Katrina experiment in an area where it rapidly intensified over the LC and WCR complex to assess altimeter-derived estimates of isotherm depths and OHC variations. Pairs of profilers, deployed in the

center of this WCR structure, confirmed similar depths of the OML of 75 m where the 26 1C isotherm was located at about 120 m. Hurricane Rita’s trajectory clipped the northeastern part of this warm structure as the storm was weakening prior to landfall on the Texas–Louisiana border. While the OHC levels remained relatively the same in this area between pre- and post-Rita (Figure 12), the dramatic cooling between the LC and shed WCR on 26 September was primarily due to the advection of a CCR moving between these ocean features. In addition to upwelling, vertical mixing cooled the ocean as suggested by the vertical sections (Figures 12(c) and 12(d)). Over this period, the WCR propagated westward at a translation speed of 12 km day 1, or nearly double their speeds. Within the WCR, the 26 1C isotherm depth decreased from a maximum depth of 115 m to B88 m. An important research question emerging from the profiler analysis is whether the strong winds associated with Rita forced the WCR to separate prematurely and propagate faster toward the west.


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Summary Progress has been made in understanding the basic oceanic and atmospheric processes that occur during hurricane passage. There is a continuing need to isolate fundamental physical processes involved in these interactions through focused experimental, empirical, theoretical, and numerical approaches. The GOM is one such basin where detailed process studies can focus on the oceanic response to the hurricane forcing as well as the atmospheric response to ocean forcing. Observational evidence is mounting that the warm and cold core features and the LC system are important to the coupled response during hurricane passage. This is not unique to the GOM as this behavior has also been recently observed in other regions such as the western Pacific Ocean and the Bay of Bengal. Thus, it is a global problem that needs to be addressed.

This coupled variability occurs over the storm scales that include fundamental length scales such as the radius of maximum winds and radius to galeforce winds. The fundamental science questions are that how the ocean and atmosphere are coupled, and that what are the appropriate timescales of this interaction? These questions are not easily answered, given especially the lack of coupled measurements spanning the spectrum of hurricane parameters such as strength, radius, and speed. One school of thought is that the only important process with respect to the ocean is under the eyewall where ocean cooling occurs. However, observed cooling under the eyewall is not just due to the surface flux alone (see Figure 1). In this regime, the maximum winds and heat and moisture fluxes occur; however, the broad surface circulation over the ocean also has nonzero fluxes that contribute the thermal



energy buildup toward the eyewall of a hurricane. The importance of stress-induced mixing and current shear instabilities in surface cooling and deepening of the surface mixed layer cannot be overstated. The deeper this layer (and 26 1C isotherm depth), the more is the heat available to the storm through the heat and moisture fluxes. Notwithstanding, it is not just the magnitude of the OHC, since the depth of the warm water is important to sustaining these surface fluxes. Future research needs to focus on these multiple scale aspects associated with the atmospheric response to ocean forcing (minimal negative feedback) and to continue studies of the

oceanic response to hurricanes over a spectrum of oceanic conditions. High-quality ocean measurements are central to addressing these questions and improving coupled models. For the first time, a strong near-inertial current response was observed by newly developed Electromagnetic Autonomous Profiling Explorer (EMAPEX) floats deployed in front of hurricane Frances (2004) by Sanford and colleagues (Figure 13). These profiling floats have provided the evolving near-inertial, internal wave radiation in unprecedented detail that includes not only the temperature and salinity (and thus density), but also the horizontal current 1.5

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structure. Notice that the phase propagation of the forced near-inertial currents is upward associated with downward energy propagation from the OML as current vectors rotate anticyclonically (clockwise) with time and depth (in the Northern Hemisphere). Velocity shears associated with these near-inertial currents force mixing events as manifested in a large fraction of the observed SST cooling of more than 2 1C (and layer deepening). Given these measurements of the basic state variables, the evolution of the Richardson numbers forced by a hurricane can be determined to evaluate mixing parametrization schemes used in coupled models for forecasting at the national centers. The variability of the surface drag coefficient has received considerable attention over the last 5 years. Several treatments have concluded that there is a leveling off or a saturation value at B3073 m s 1. The ratio of the enthalpy (heat and moisture) coefficient and the drag coefficient is central to air–sea fluxes impacting the hurricane boundary layer. In this context, the relationship between the coupled processes such as wave breaking and the generation of sea spray and how this is linked to localized air–sea

fluxes remains a fertile research area. A key element of this topic is the atmospheric response to the oceanic forcing where there seem to be contrasting viewpoints. One argument is that the air–sea interactions are occurring over surface wave (wind-wave) time and space scales and cause significant intensity changes by more than a category due to very large surface drag coefficients. While, these sub-mesoscale phenomena may affect air–sea fluxes, the first-order balances are primarily between the atmospheric and oceanic mixed layers. The forced surface waves modulate the heat and momentum fluxes. Future Research

A promising avenue of research has focused on the upper ocean’s role on intensity change. Climatologically, for the western Atlantic basin, the expected number of category 5 storms is one approximately every 3 years. Over the last 4 years, there have been a total of six category 5 storms, well above this mean. Based on extensive deliberations by the international tropical cyclone community, intensity and structure changes are primarily due to environmental

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conditions such as atmospheric circulation, internal dynamics, and oceanic circulation processes. Gyrescale ocean circulation redistributes ocean heat throughout the basins primarily through poleward advection and transport along its western boundary. While there is an open scientific question whether the increased frequency of occurrence of severe hurricanes is due to global warming or natural cycle associated with geophysical processes, the severe hurricanes during the 2005 season interacted with the warm Caribbean Current and the LC. As shown here and in recently published papers, the oceanic response over these regimes differs considerably from that observed quiescent regimes. The key issue is the level of observed ocean cooling in these regimes that is considerably less (i.e., ‘less negative feedback’) than compared to other areas where the cooling is more dramatic. Since winds begin to mix the thin ‘skin’ layer of SST well in front of the storm, the surface temperature reflects the temperature of the oceanic mixed layer under high winds. This point is often overlooked in atmospheric models where SSTs are prescribed or weakly coupled to an ocean where the basic state is at rest. As discussed above, intense hurricanes in the GOM may have encountered the LC and WCR during their lifetimes (Figure 14). With the exception of hurricane Allen (1980), which maintained severe status outside the envelope of this oceanic variability, when hurricanes encounter these features, changes in hurricane intensity are often observed even though warm SSTs prevail during summer months over most of the basin. As noted above, during a 7-week period in 2005, Katrina, Rita, and Wilma all rapidly deepened to catagory 5 status in less than 24 h. Lowest central pressures for this unprecedented hurricane trifecta over a 7-week timescale were 896, 892, and 882 mb. Until Wilma, Gilbert in 1988 held the lowest surface pressure record of 888 mb in the basin. With surface winds in excess of 70 m s 1 within 36 h of landfall over the LC and WCR complex, Katrina and Rita had a pronounced impact on the northern GOM coast as well as offshore structures such as oil rigs. If these oceanic conditions had prevailed during the summer of 1969, hurricane Camille, the strongest land-falling hurricane on record in the Atlantic Ocean basin, may have aligned with the axis of this warm current system. Given the natural variability of this deep warm reservoir, such interactions must be explored in more detail for not only the oceanic response, but also the potential feedbacks to the hurricanes where ocean cooling is minimized with respect to the next-generation forecast models.

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Acknowledgments L.K. Shay gratefully acknowledges NSF support and the support of the NOAA Aircraft Operations Center. Mr. Bill Teague provided Ivan current data; and Drs. Mark Donelan, Brian Haus, George Halliwell, S. Daniel Jacob, and Tom Sanford shared material. SSTs were provided by Remote Sensing Systems website (http://www.remss.com), courtesy of Dr. Chelle Gentemann. Benjamin Jaimes, Eric Uhlhorn, and Jodi Brewster also contributed to this article.

See also Breaking Waves and Near-Surface Turbulence. Upper Ocean Mixing Processes. Upper Ocean Time and Space Variability. Upper Ocean Vertical Structure.

Further Reading Chassignet EP, Smith L, Halliwell GR, and Bleck R (2003) North Atlantic simulations with the hybrid coordinate ocean model (HYCOM): Impact of the vertical coordinate choice and resolution, reference density, and thermobaricity. Journal of Physical Oceanography 33: 2504--2526. D’Asaro EA (2003) The ocean boundary layer under hurricane Dennis. Journal of Physical Oceanography 33: 561--579. Donelan MA, Haus BK, Reul N, et al. (2004) On the limiting aerodynamic roughness of the ocean in very strong winds. Geophysical Research Letters 31: L18306 (doi: 1029/2004GRL019460). Gentemann C, Donlon CJ, Stuart-Menteth A, and Wentz F (2003) Diurnal signals in satellite sea surface temperature measurements. Geophysical Research Letters 30(3): 1140--1143. Halliwell GR, Jr., Shay LK, Jacob SD, Smedstad OM, and Uhlhorn EW (in press) Improving ocean model initialization for coupled tropical cyclone forecast models using GODAE nowcasts. Monthly Weather Review. Jacob SD and Shay LK (2003) The role of oceanic mesoscale features on the tropical cyclone-induced mixed layer response. Journal of Physical Oceanography 33: 649--676. Large WG and Pond S (1981) Open ocean momentum flux measurements in moderate to strong wind. Journal of Physical Oceanography 11: 324--336. Leben RR (2005) Altimeter derived Loop Current metrics. In: Sturges W and Lugo-Fernandez A (eds.) Geophysical Monograph, No. 161: Circulation in the Gulf of Mexico: Observations and Models, pp. 181--201. Washington, DC: American Geophysical Union. Lugo-Fernandez A (2007) Is the Loop Current a chaotic oscillator? Journal of Physical Oceanography 37: 1455--1469.


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Nof D (2005) The momentum imbalance paradox revisited. Journal of Physical Oceanography 35: 1928--1939. Ocampo-Torres FJ, Donelan MA, Merzi N, and Jai F (1994) Laboratory measurements of mass transfer of carbon dioxide and water vapour for smooth and rough flow conditions. Tellus 46B: 16--32. Powell MD, Vickery PJ, and Reinhold TA (2003) Reduced drag coefficient for high wind speeds in tropical cyclones. Nature 422: 279--283. Sanford TB, Dunlap JH, Carlson JA, Webb DC, and Girton JB (2005) Autonomous velocity and density profiler: EM-APEX. In: Proceedings of the IEEE/OES 8th Working Conference on Current Measurement Technology, IEEE Cat No. 05CH37650, pp. 152–156 (ISBN: 0-7803-8989-1). Shay LK (2001) Upper ocean structure: Response to strong forcing events. In: Weller RA, Thorpe SA, and Steele J (eds.) Encyclopedia of Ocean Sciences, pp. 3100--3114. London: Academic Press. Shay LK and Uhlhorn EW (2008) Loop Current response to hurricanes Isidore and Lili. Monthly Weather Review 136 (doi: 10.1175/2008MWR2169).

Sturges W and Leben R (2000) Frequency of ring separations from the Loop Current in the Gulf of Mexico: A revised estimate. Journal of Physical Oceanography 30: 1814--1819. Teague WJ, Jarosz E, Carnes MR, Mitchell DA, and Hogan PJ (2006) Low frequency current variability observed at the shelf break in the northern Gulf of Mexico: May–October 2004. Continental Shelf Research 26: 2559--2582 (doi:10.1016/j.csr.2006.08.002). Vukovich FM (2007) Climatology of ocean features in the Gulf of Mexico using satellite remote sensing data. Journal of Physical Oceanography 37: 689--707. Walker N, Leben RR, and Balasubramanian S (2005) Hurricane forced upwelling and chlorophyll a enhancement within cold core cyclones in the Gulf of Mexico. Geophysical Research Letter 32: L18610 (doi: 10. 1029/2005GL023716).

Relevant Website http://www.remss.com – Remote Sensing Systems Home Page.


UPPER OCEAN TIME AND SPACE VARIABILITY D. L. Rudnick, University of California, San Diego, CA, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3114–3120, & 2001, Elsevier Ltd.

The processes discussed below are ordered roughly by increasing time and space scales (Figure 1). Most of the processes are covered in greater detail elsewhere in this volume. It is hoped that this section will provide a convenient introduction to the variability of the upper ocean, and that the reader can proceed to the more in-depth articles as needed.

Introduction

Turbulence and Mixing

The upper ocean is the region of the ocean in direct contact with the atmosphere. Air–sea fluxes of momentum, heat, and fresh water are the primary external forces acting upon the upper ocean (see Heat and Momentum Fluxes at the Sea Surface; Evaporation and Humidity; Wind- and Buoyancy-Forced Upper Ocean). These fluxes impose the temporal and spatial scales of the overlying atmosphere. The internal dynamics of the ocean cause variability at scales distinct from the forcing. This combination of forcing and dynamics creates the tapestry of oceanic phenomena at timescales ranging from minutes to decades and length scales from centimeters to thousands of kilometers. This article is concerned primarily with the physical processes causing time and space variability in the upper ocean. The physical balances to be considered are the conservation of mass, heat, salt, and momentum. Thus, physical phenomena are discussed with special reference to their effects on the temporal and spatial variability of temperature, salinity, density, and velocity. While many other biological, chemical, and optical properties of the ocean are affected by the phenomena outlined below, their discussion is covered by other articles in this volume. The most striking feature often seen in vertical profiles of the upper ocean is the surface mixed layer, a layer that is vertically uniform in temperature, salinity, and horizontal velocity (see Upper Ocean Vertical Structure and Upper Ocean Mean Horizontal Structure). The turbulence that mixes this layer derives its energy from wind and surface cooling. The region immediately below the mixed layer tends to be stratified, and is often called the seasonal thermocline because its stratification varies with the seasons. The seasonal thermocline extends down a few hundred meters to roughly 1000 m. Beneath the seasonal thermocline is the permanent thermocline whose stratification is constant on timescales of at least decades. Here the discussion is concerned with variability of the mixed layer and seasonal thermocline.

The upper ocean is distinguished from the interior of the ocean partly because of the very high levels of turbulence present (see Breaking Waves and NearSurface Turbulence and Upper Ocean Mixing Processes). The smallest scale of motion worthy of note in the ocean is the Kolmogoroff scale, on the order of 1 cm, where energy is dissipated by molecular viscosity. At this scale, the ocean can be considered isotropic; that is, properties vary in the same way regardless of the direction in which they are measured. At much larger scales than the Kolmogoroff scale, the vertical stratification of the ocean becomes important. In the seasonal thermocline, a dominant mechanism for mixing is the Kelvin-Helmholtz instability, in which a vertical shear of horizontal velocity causes the overturn of stratified water (see Internal Waves). The resulting ‘billows’ are observed tobe on the order of 1 m thick and to decay on the order of an hour. A great deal of observational and theoretical work in the last 20 years has been devoted to relating the strength of this mixing to larger (in the order of 10 m) and more easily measurable quantities such as shear and stratification. The resulting Henyey-Gregg parameterization is one of the most fundamentally important achievements of modern oceanography.

Langmuir Circulation and Convection Turbulence in the mixed layer is fundamentally different from that in the seasonal thermocline. Because the mixed layer is nearly unstratified, the largest eddies can be as large as the layer is thick, often about 100 m. These large eddies have come to be called Langmuir cells in honor of Irving Langmuir, the Nobel laureate in chemistry who first described them. Langmuir cells are elongated vortices whose axes are horizontal and oriented nearly parallel to the wind. The cells have radii comparable in size to the mixed layer depth, and can be as long as 1–2 km. Langmuir cells often appear in pairs with opposite


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Tides

Atmospheric storms

Horizontal length scale (m)

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~

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Seasonal cycle

107 El Nino climate

104 Fronts and eddies Internal waves

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Solar heating 102

Langmuir circulation convection

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100

10

_1

10

_2

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102 minute

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hour

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month

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Figure 1 A schematic diagram of the distribution in time and space of upper ocean variability. The temporal and spatial limits of the phenomena should be considered approximate.

senses of rotation. The cells thus create alternating regions of surface convergence and divergence. The regions of convergence collect material floating on the surface such as oil and seaweed. Langmuir first became aware of these cells after noticing lines of floating seaweed during a crossing of the Atlantic. Langmuir cells are forced by a combination of wind and surface waves, and are established typically within an hour after the wind starts blowing. Langmuir cells disappear quickly after the wind stops. Recent research indicates that Langmuir cells often vacillate in strength on the timescale of roughly 15 minutes. Convection cells forced by surface cooling also cause the mixed layer to be homogenized and to deepen (see Open Ocean Convection). A typical feature in the mixed layer is the daily cycle of stratification, with daytime heating causing nearsurface stratification and nighttime cooling causing convection that destroys this stratification and

deepens the mixed layer. The vertical extent of convection cells corresponds to the depth of the mixed layer (of order 100 m); the cells have an aspect ratio of one so their horizontal and vertical scales are equal. Because solar heating has a large, essentially global, scale the daily heating and cooling of the upper ocean is coherent and predictable over large scales. Horizontal velocity in the mixed layer also varies strongly at a 24 h period, as the daily cycle of stratification affects the depth to which the wind forces currents. The deepest mixed layers in the oceans, at high latitudes, are convectively mixed. Convection cells are thus more effective at deepening the mixed layer than are Langmuir cells.

Internal Waves Just as there are gravity waves on the surface of the ocean, there are gravity waves in the thermocline.



These thermocline gravity waves, modified by the Earth’s rotation, are known as internal waves (see Internal Waves). They exist in a range of frequencies bounded at the lower end by the inertial frequency f and at the upper end by the buoyancy frequency N. A parcel of water given an initial velocity will travel in a circle under the influence of the Coriolis force. The inertial frequency f, twice the local vertical component of the Earth’s rotation vector, is the frequency of rotation around such a circle. The resulting horizontal current is known as an inertial oscillation. The inertial period is 12 h at the poles, 24 h at 301 latitude, and infinite at the equator because local vertical is normal to the Earth’s axis of rotation. The buoyancy frequency N, proportional to the square root of the vertical density gradient, is the frequency of oscillation of a water parcel given a displacement in the vertical. The resulting vertical motion has a frequency of less than one to several cycles per hour in typical ocean stratification. Internal waves oscillate in planes tilted from the horizontal as a function of the frequency between f and N. Internal waves have amplitudes on the order of tens of meters. They may be coherent over vertical scales that approach the depth of the ocean, particularly at high frequencies near N. Lower frequency internal waves, approaching f, have shorter vertical wavelengths often of order 100 m or less. The horizontal wavelength of an internal wave is related to its frequency and vertical wavelength through the internal wave dispersion relation. For a given vertical wavelength, a high frequency internal wave will have shorter horizontal wavelength than a low frequency wave. At the low frequency end of the internal wave spectrum, the near-inertial waves are especially important in the upper ocean. Near-inertial waves are quite ubiquitous because they are so readily excited by wind forcing on the ocean’s surface. In measurements of horizontal current, inertial oscillations are often the most obvious variability because horizontal currents ‘ring’ at the resonant inertial frequency. Just as a bell has a distinctive tone when struck, the ocean has inertial currents when hit, for example, by a storm. Strong inertial currents are one of the indications in the ocean of the recent passage of a hurricane. The radius of an inertial current circle is its speed divided by its rotation rate, U/f. If the current speed is 0.1 ms 1, then for a midlatitude inertial frequency of 10 4 s 1, the radius is 1 km. In the aftermath of a storm, the inertial currents and radii may be nearly an order of magnitude larger. Nearinertial waves are a dominant mechanism for transporting wind-driven momentum downward from the mixed layer to the seasonal thermocline and into the

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interior. Because near-inertial motions have short vertical scales, they dominate the shear spectrum in the ocean. This shear eventually leads to enhanced turbulence and mixing the penetration of inertial shear into the ocean and the geography of shear and mixing are active topics of research. Tides are well known to anyone who has spent at least a day at the beach. The dominant tidal periods are near one day and one-half day. Tides are most obvious to the casual observer of the sea surface, and they are easily seen in records of horizontal current in the open ocean. Internal tides exist as well, for example forced by tidal flow over bumps on the ocean bottom (see Internal Waves). These internal tides, seen as variability in density and velocity at a location, are a form of internal wave and are governed by the same dynamics. Isolated pulses of tidal internal waves, known as ‘solitons,’ are prevalent in certain regions of rough bottom topography, and are a field of current research.

Fronts and Eddies While vertically uniform, the mixed layer can vary in the horizontal on a wide range of scales. We have already discussed Langmuir circulation and convection cells on scales of order 100 m, but there may be horizontal variability on longer scales. Just as there are fronts in the atmosphere, visible for example in the satellite pictures of clouds shown on the evening television news, there are fronts in the ocean. Fronts in the ocean separate regions of warm and cool water, or fresh and salty water. The most obvious fronts in the mixed layer have widths on the order of 10–100 km, and typically persist for weeks. Fronts of this size have currents directed along the front as a result of the geostrophic momentum balance. That is, the Coriolis force balances the pressure gradient due to having water of varying density across the front. The less dense (usually warmer) water is on the right side of the current in the Northern Hemisphere (the sense of the current is the opposite in the Southern Hemisphere). Fronts in the mixed layer are sites of enhanced vertical circulation on the order of tens of meters per day. Strong biological productivity at fronts is attributed to this vertical circulation which brings deeper water rich in nutrients to the surface. Fronts at scales shorter than 10 km also exist in the mixed layer. At these shorter scales, the geostrophic balance may not be expected to hold. Typical fronts at these scales are observed to be warm and salty on one side and cold and fresh on the other such that the density contrast across the front vanishes. Such a


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front is often said to be compensated, since temperature and salinity gradients compensate in their effect on density. The presence of compensated fronts in the mixed layer is consistent with a horizontal mixing that is an increasing function of the horizontal density gradient. That is, small-scale horizontal density fronts do not persist as long as compensated fronts. Because of their small scale, fronts of order 1 km are poorly observed in the ocean, and are a topic of current research. Observed fronts are usually not observed to be perfectly straight, rather they wiggle. The wiggles, or perturbations, often grow to be large in comparison with the width of the front. When the perturbations grow large enough, the front may turn back on itself and a detached eddy is formed. The eddies often have sizes on the order of 10 km, when they are confined in depth to the mixed layer. This length scale is related to the Rossby radius of deformation; at scales larger than the Rossby radius flows tend to be geostrophic. The Rossby radius for the mixed layer is given by: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi gHDr=r f where g is acceleration due to gravity, H is the depth of the mixed layer, r is the density of the water, and Dr is the change in density across the mixed layer base. For a typical mixed layer, H is 100 m and Dr is 0.2 kg m 3, g is 9.8 m s 2, and r is 1025 kg m 3, so the Rossby radius is about 6 km. Eddies that extend deeper have larger radii, as can be inferred from the formula for the Rossby radius. Large eddies can persist for as long as several months, while smaller eddies are shorter lived. The small-scale mixed layer eddies, a prominent feature in satellite photos of the sea surface, are typically observed to rotate in the counterclockwise direction in the Northern Hemisphere, and clockwise south of the equator. Again, because of their small size, they have been inadequately observed and are a topic of current research.

Wind-forced Currents (see Wind Driven Circulation) One of the oldest theories of ocean circulation is due to V.W. Ekman, who in 1905 suggested a balance between the Coriolis force and the stress due to wind blowing over the ocean surface. The prediction of this theory for a steady wind is a current that spirals to the right (in the Northern Hemisphere) and decays with depth. This spiral structure was not clearly observed in the ocean until the 1980s with the advent

of moorings with modern current meters. Although the details of the stress parameterization used by Ekman were found to be inadequate to describe observations, the general picture of a spiral remains valid to this day. An alternative theoretical construct to explain upper ocean structure is the bulk mixed layer model. Oceanic properties, such as temperature, salinity, and velocity, are assumed to be vertically uniform in the mixed layer, with a region of very strong vertical gradients at the mixed layer base. The mixed layer is then forced by air–sea fluxes of heat, fresh water, and momentum at the surface, and by turbulent fluxes at the base. The bulk mixed layer model has proven remarkably successful at predicting some basic features of the upper ocean, particularly the vertical temperature structure. Interestingly, the disparate conceptual models of the Ekman spiral and the bulk mixed layer can be rationalized. The upper ocean velocity structure is often, but certainly not always, observed to be vertically uniform near the surface with a region of high shear beneath, in accordance with the bulk mixed layer model. On the other hand, long time averages of ocean current tend to have a spiral structure, in qualitative agreement with the Ekman spiral. This is so if the averages are long enough to span many cycles of mixed layer shoaling and deepening, as due to the daily cycle of surface heating. Thus the timeaverage current spiral may be very different from a typical snapshot of a nearly vertically uniform current. The averaged wind-driven spiral extends downward to a depth comparable to, but slightly deeper than, the mixed layer. The shape of the spiral is strongly influenced by higher frequency variability in the stratification, such as the daily cycle in mixed layer depth discussed above. A spiral is observed in response to temporally variable winds, as well as to steady winds. The temporally variable spiral may have a different vertical structure to the steady spiral. In particular, the current spirals to the left with depth in response to a wind that rotates more rapidly than f in a clockwise direction, in contrast to the steady spiral to the right. Regardless of the detailed velocity structure in the upper ocean, the net transport caused by a steady wind is 901 to the right of the wind in the Northern Hemisphere (and to the left in the Southern Hemisphere). This transport (the vertical integral of velocity) is called the Ekman transport. The Ekman transport is proportional to the wind stress and inversely proportional to the inertial frequency. Thus wind of a given strength will cause more transport near the equator than it would closer to the poles.



The spectrum of wind over the midlatitude ocean peaks at periods of a few to several days. These periods correspond to the time required for a typical storm to pass. The wind-driven current and transport is thus prominent at these periods. Atmospheric storms have typical horizontal sizes of a few to several hundred kilometers, and the direct oceanic response to these storms has similar horizontal scales. The prominent large-scale features of the wind field such as the westerlies in midlatitudes and the trade winds in the tropics directly force currents in the upper ocean. These currents have large horizontal length scales that reflect the winds.

Seasonal Cycles Just as the seasons cause well-known changes in weather, the annual cycle is one of the most robust signals in the ocean. Summer brings greater heat flux from the atmosphere to the ocean, and warmer ocean temperatures. As the ocean warms up at the surface, stratification increases and the mixed layer becomes shallower. The heat flux reverses in many locations during the winter and the ocean cools at the surface. The resulting convection causes the mixed layer to deepen; at some high latitude locations the mixed layer can deepen to several hundred meters in the winter. Winter conditions in high and midlatitude mixed layers are very important to the general circulation of the oceans, as it is these waters that penetrate into the thermocline and set properties that persist for decades. Along with cooler temperatures, winter brings typically stormier weather and more wind and precipitation. Wind-driven currents often peak during the winter in midlatitudes, at the same time that salinity decreases in response to the increased precipitation. Seasonal cycles occur over the whole globe in an extremely coherent fashion, because they are driven primarily by the solar heat flux. However, the seasonal cycle can vary at different oceanic locations. For example, the seasonal cycle at the equator is smaller than that at midlatitudes because solar heat flux varies less over the year. The Arabian Sea has a pronounced semi-annual cycle. Cold northerly winds in winter cool the ocean and deepen the mixed layer as typical for midlatitudes. More unusual is a second period of relatively low ocean temperatures and deep mixed layers during the summer south-west monsoon. Wind-driven mixing causes the cooling during the south-west monsoon as cool water is mixed up to the surface. The Arabian Sea monsoon is the classic

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example of a seasonal wind driven by land–sea temperature differences. Monsoons also exist overthe south-west USA and south-east Asia, among others. Additional local seasonal effects may be caused by river outflows and weather patterns influenced by orography.

Climatic Signals The ocean has significant variability at periods longer than 1 year. The most well known recurrent interannual climatic phenomenon is El Ninõ (see El Ninõ Southern Oscillation (ENSO)). An El Ninõ occurs when trade winds reverse at the equator causing upwelling to cease off the coast of South America. The most obvious consequence of an El Ninõ is dramatically elevated ocean temperatures at the equator. These high temperatures progress poleward from the equator along the coast of the Americas, affecting water properties in large regions of the Pacific. El Ninõ has been hypothesized to start with anomalous winds in the western equatorial Pacific, eventually having an effect on the global ocean and atmosphere. El Ninõs occur sporadically every roughly 3–7 years, and are becoming more predictable as observations and models of the phenomenon improve. The reverse phase of El Ninõ, the so-called La Nina, is remarkable for exceptionally low equatorial temperatures and strong trade winds. Oscillations with periods of a decade and longer also exist in the ocean and atmosphere. Such oscillations are apparent in the ocean as basin-scale variations in sea surface temperature, for example. Salinity and velocity are also likely variable on decadal timescales, although the observational database for these is sparse in comparison with that for temperature. Atmospheric decadal oscillations in temperature and precipitation are well established. Scientists are actively researching whether and how the ocean and atmosphere are coupled on decadal timescales. The basic idea is that the ocean absorbs heat from the atmosphere and stores it for many years because of the ocean’s relatively high heat capacity. This heat may penetrate into the ocean interior and be redistributed by advective processes. The heat may resurface a decade or more later to affect the atmosphere through anomalous heat flux. The coupled ocean–atmosphere process just described is controversial, and the observations to support its existence are inadequate. A major challenge for the immediate future is to obtain the measurements needed to resolve such processes of significance to climate.


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Conclusion

Further Reading

The upper ocean varies on a wide range of temporal and spatial scales. Processes range from mixing occurring on scales of centimeters and minutes to decadal climatic oscillations of entire ocean basins. Fundamental to the ocean is the fact that these processes can rarely be studied in isolation. That is, processes occurring on one scale affect processes on other scales. For example, decadal changes in ocean stratification are strongly affected by turbulent mixing at the smallest scales. Turbulent mixing is modulated by the internal wave field, and internal waves are focused and steered by geostrophic fronts and eddies. The interaction among processes of different scales is likely to receive increasing attention from ocean scientists in the coming years.

See also Breaking Waves and Near-Surface Turbulence. Double-Diffusive Convection. El Ninõ Southern Oscillation (ENSO). Evaporation and Humidity. Heat and Momentum Fluxes at the Sea Surface. Internal Waves. Open Ocean Convection. Upper Ocean Mean Horizontal Structure. Upper Ocean Mixing Processes. Upper Ocean Vertical Structure. Wind- and Buoyancy-Forced Upper Ocean.

Davis RE, de Szoeke R, Halpern D, and Niiler P (1981) Variability in the upper ocean during MILE. Part I: The heat and momentum balances. Deep-Sea Research 28: 1427--1452. Ekman VW (1905) On the influence of the earth’s rotation on ocean currents. Arkiv Matematik, Astronomi och Fysik 2: 1--52. Eriksen CC, Weller RA, Rudnick DL, Pollard RT, and Regier LA (1991) Ocean frontal variability in the Frontal Air–Sea Interaction Experiment. Journal of Geophysical Research 96: 8569--8591. Gill AE (1982) Atmosphere–Ocean Dynamics. New York: Academic Press. Gregg MC (1989) Scaling turbulent dissipation in the thermocline. Journal of Geophysical Research 94: 9686--9698. Langmuir I (1938) Surface motion of water induced by wind. Science 87: 119--123. Lighthill MJ and Pearce RP (eds.) (1981) Monsoon Dynamics. Cambridge: Cambridge University Press. Munk W (1981) Internal waves and small-scale processes. In: Warren BA and Wunsch C (eds.) Evolution of Physical Oceanography, pp. 264--291. Cambridge, USA: MIT Press. Philander SG (1990) El Ninõ, La Ninã, and the Southern Oscillation. San Diego: Academic Press. Roden GI (1984) Mesoscale oceanic fronts of the North Pacific. Annals of Geophysics 2: 399--410.


UPPER OCEAN VERTICAL STRUCTURE J. Sprintall, University of California San Diego, La Jolla, CA, USA M. F. Cronin, NOAA Pacific Marine Environmental Laboratory, Seattle, WA, USA & 2009 Elsevier Ltd. All rights reserved.

Introduction The upper ocean connects the surface forcing from winds, heat, and fresh water, with the quiescent deeper ocean where this heat and fresh water are sequestered and released on longer time- and global scales. Classically the surface layer includes both an upper mixed layer that is subject to the direct influence of the atmosphere, and also a highly stratified zone below the mixed layer where vertical property gradients are strong. Although all water within the surface layer has been exposed to the atmosphere at some point in time, water most directly exposed lies within the mixed layer. Thus, the surface layer vertical structure reflects not only immediate changes in response to the surface forcing, but also changes associated with earlier forcing events. These forcing events may have occurred either locally in the region, or remotely at other locations and transferred by ocean currents. This article first defines the major features of the upper ocean vertical structure and discusses what causes and maintains them. We then show numerous examples of the rich variability in the shapes and forms that these vertical structures can assume through variation in the atmospheric forcing.

radiometers. In contrast, in situ sensors generally measure the ‘bulk’ SST over the top few meters of the water column. The cool skin temperature is generally around 0.1–0.5 K cooler than the bulk temperature. As the air–sea fluxes are transported through the molecular layer almost instantaneously, the upper mixed layer can generally be considered to be in direct contact with the atmosphere. For this reason, when defining the depth of the surface layer, the changes in water properties are generally made relative to the bulk SST measurement. The upper mixed layer is the site of active air–sea exchanges. Energy for the mixed layer to change its vertical structure comes from wind mixing or through a surface buoyancy flux. Wind mixing causes vertical turbulence in the upper mixed layer through waves, and by the entrainment of cooler water through the bottom of the mixed layer. Wind forcing also results in advection by upper ocean currents that can change the water properties and thus the vertical structure of the mixed layer. Surface buoyancy forcing is due to heat and fresh water fluxed across the air–sea interface. Cooling and evaporation induce convective mixing and overturning, whereas heating and rainfall cause the mixed layer to restratify in depth and display alternate levels of greater and lesser vertical

Solar radiation

Rainfall Wind

Evaporation

Major Features of the Upper Ocean Vertical Structure The vertical structure of the upper ocean is primarily defined by the temperature and salinity, which together control the water column’s density structure. Within the ocean surface layer, a number of distinct layers can be distinguished that are formed by different processes over different timescales: the upper mixed layer, the seasonal pycnocline, and the permanent pycnocline (Figure 1). Right at the ocean surface in the top few millimeters, a cool ‘skin’ exists with lowered temperature caused by the combined heat losses from long-wave radiation, sensible and latent heat fluxes. The cool skin is only a few millimeters thick, and is the actual sea surface temperature (SST) measured by airborne infrared

Turbulence Entrainment Depth

Mixed layer

Seasonal pycnocline

Main pycnocline

Figure 1 Conceptual diagram of the vertical structure in the surface layer, and the forcing and physics that govern its existence. The depth of the mixed layer, the seasonal pycnocline, and the main pycnocline are indicated.


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property gradients. Thus, if strong enough, the wind and buoyancy fluxes can generate sufficient turbulence so that the upper portion of the surface layer has a thick, homogeneous (low vertical gradient or stratification), well-mixed layer in temperature, salinity, and density. Wind and buoyancy forcing also affect the vertical structure of the velocity or shear (vertical gradient of horizontal velocity) in the upper mixed layer. Upper ocean processes, such as inertial shear, Langmuir circulations, internal gravity waves, and Kelvin–Helmholtz instabilities, that alter the velocity profile in the surface layer are an active area of research, and are more fully discussed in Upper Ocean Mixing Processes. Temporal and spatial variations in the strength and relative contributions of the atmospheric forcing can cause substantial variability in the water properties and thickness of the upper mixed layer. Large temporal variation can occur on daily and seasonal timescales due to changes in the solar radiation. For example, during the daily cycle the sun heats the ocean, causing the upper surface to become increasingly warm and weakly stratified. The ‘classic’ vertically uniform mixed layer, as depicted in Figure 1, may not be present in the upper ocean surface layer. As the sun sets, the surface waters are cooled and sink, generating turbulent convection that causes entrainment of water from below and mixing that produces the vertically well-mixed layer. Similarly, the mixed layer structure can exhibit significant horizontal variations. The large latitudinal differences in solar radiation result in mixed layers that generally increase in depth from the equator to the Poles. Even in the east–west direction, boundary currents and differential surface forcing can result in mixed layers that assume different vertical structures, although generally the annual variations of temperature along any given latitude will be small. Temporal and spatial variability in the vertical structure of the mixed layer, and the physics that govern this variability are covered elsewhere (see Upper Ocean Mean Horizontal Structure, Upper Ocean Time and Space Variability, and Wind- and Buoyancy-Forced Upper Ocean). Separating the upper mixed layer from the deeper ocean is a region typically characterized by substantial vertical gradients in water properties. In temperature, this highly stratified vertical zone is referred to as the thermocline, in salinity it is the halocline, and in density it is the pycnocline. To maintain stability in the water column, lighter (less dense) water must lie above heavier (denser) water. It follows then, that the pycnocline is a region where density increases rapidly with depth. Although the thermocline and the halocline may not always

exactly coincide in their depth range, one or the other property will control the density structure to form the pycnocline. In mid-latitudes during summer, surface heating from the sun can cause a shallow seasonal thermocline (pycnocline) that connects the upper mixed layer to the deeper more permanent thermocline or ‘main pycnocline’ (see Figure 1). Similarly, in the subpolar regions, the seasonal summer inputs of fresh water at the surface through rainfall, rivers, or ice melt can result in a seasonal halocline (pycnocline) separating the fresh surface from the deeper saltier waters. Whereas the seasonal pycnocline disappears every winter, the permanent pycnocline is always present in these areas. The vertical density gradient in the main pycnocline is very strong, and the turbulence within the upper mixed layer induced by the air–sea exchanges of wind and heat cannot overcome the great stability of the main pycnocline to penetrate into the deeper ocean. The stability of the main pycnocline acts as a barrier against turbulent mixing processes, and beneath this depth the water has not had contact with the surface for a very long time. Therefore the main pycnocline marks the depth limit of the upper ocean surface layer. In some polar regions, particularly in the far North and South Atlantic, no permanent thermocline exists. The presence of an isothermal water column suggests that the cold, dense waters are continuously sinking to great depths. No stable permanent pycnocline or thermocline exists as a barrier to the vertical passage of the surface water properties that extend to the bottom. In some cases, such as along the shelf in Antarctica’s Weddell Sea in the South Atlantic, salinity can also play a role in dense water formation. When ice forms from the seawater in this region, it consists primarily of fresh water, and leaves behind a more saline and thus denser surface water that must also sink. The vertical flow of the dense waters in the polar regions is the source of the world’s deep and bottom waters that then slowly mix and spread horizontally via the large-scale thermohaline ocean circulation to fill the deep-ocean basins. In fact, the thermohaline circulation also plays an important role in maintaining the permanent thermocline at a relatively constant depth in the low and middle latitudes. Despite the fact that the pycnocline is extremely stable, it might be assumed that on some long-enough timescale it could be eroded away through mixing of water above and below it. Humboldt recognized early in the nineteenth century that ocean circulation must help maintain the low temperatures of the deeper oceans; the equatorward movement of the cold deep and bottom water masses are continually renewed through



sinking (or ‘convection’) in the polar region. However, it was not until the mid-twentieth century that Stommel suggested that there was also a slow but continual upward movement of this cool water to balance the downward diffusion of heat from the surface. It is this balance, that actually occurs over very small space and timescales that sustains the permanent thermocline observed at middle and low latitudes. Thus, the vertical structure of the upper ocean helps us to understand not only the wind- and thermohaline-forced ocean circulation, but also the response between the coupled air–sea system and the deeper ocean on a global scale.

Definitions Surface Layer Depth

There is no generally accepted definition of the surface layer depth. Conceptually the surface layer includes the mixed layer, where active air–sea exchanges are occurring, plus those waters in the seasonal thermocline that connect the mixed layer and to the permanent thermocline. Note the important detail that the surface layer includes the mixed layer, a fact that has often been blurred in the criteria used to determine their respective depth levels. A satisfactory depth criterion for the surface layer should thus include all the major features of the upper ocean surface layer described above and illustrated in Figure 1. Further, the surface layer depth criterion should be applicable to all geographic regimes, and include those waters that have recently been in contact with the atmosphere, at least on timescales of up to a year. Finally, the definition should preferably be based on readily measurable properties such as temperature, salinity, or density. Ideally then, we could specify the surface layer to be the depth where, for instance, the temperature is equal to the previous winter’s minimum SST. However in practice, this surface layer definition would vary temporally, making it difficult to decipher the year-to-year variability. Oceanographers therefore generally prefer a static criterion, and thus modify the definition to be the depth where the temperature is equal to the coldest SST ever observed using any historical data available at a particular geographic location. This definition is analogous to a local ‘ventilation’ depth: the deepest surface to which recent atmospheric influence has been felt at least over the timescale of the available historical data. The definition suggested for the surface layer is also primarily one-dimensional, involving only the temperature and salinity information from a given location. Lateral advective effects have not been

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included. The roles of velocity and shear, and other three-dimensional processes in the surface layer structure (e.g., Langmuir circulations, internal gravity waves, and Kelvin–Helmholtz instabilities), may on occasion be important. However, their roles are harder to quantify and have not, as yet, been adequately incorporated into a working definition for the depth of the surface layer. Mixed Layer Depth

The mixed layer is the upper portion of the surface layer where active air–sea exchanges generate surface turbulence which causes the water to mix and become vertically uniform in temperature and salinity, and thus density. Very small vertical property gradients can still occur within the mixed layer in response to, for example, adiabatic heating or thermocline erosion. Direct measurements of the upper layer turbulence through dissipation rates provide an accurate and instant measurement of the active ‘mixing’ depth. However, while the technology is improving rapidly, turbulence scales are very small and difficult to detect, and their measurement is not widespread at present. Furthermore, the purpose of defining a mixed layer depth is to obtain more of an integrated measurement of the depth to which surface fluxes have penetrated in the recent past (daily and longer timescales). For this reason, as in the surface layer depth criterion, definitions of the mixed layer depth are most commonly based on temperature, salinity, or density. The mixed layer depth must define the depth of the transition from a homogeneous upper layer to the stratified layer of the pycnocline. Several definitions of the mixed layer depth exist in the literature. One commonly used mixed layer depth criterion determines the depth where a critical temperature or density gradient corresponding to the top of the maximum property gradient (i.e., the thermocline or pycnocline) is exceeded. The critical gradient criteria range between 0.02 and 0.05 1C m 1 in temperature, and 0.005 and 0.015 kg m 3 in density. This criterion may be sensitive to the vertical depth interval over which the gradient is calculated. Another mixed layer depth criterion determines a net temperature or density change from the surface isotherm or isopycnal. Common values used for the net change criterion are 0.2–1 1C in temperature from the surface isotherm, or 0.03–0.125 kg m 3 from the surface isopycnal. Because of the different dynamical processes associated with the molecular skin SST, oceanographers generally prefer the readily determined bulk SST estimate as the surface reference temperature. Ranges of the temperature and density values used in


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UPPER OCEAN VERTICAL STRUCTURE (b) 9503 6° S, 166° W

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Figure 2 Temperature (black line), salinity (blue line), and density (green line) during March 1995 from expendable conductivity– temperature–depth profiles in the Pacific Ocean at (a) 6.91 N, 173.21 W and (b) 61 S, 1661 W. In temperature, mixed layer depth is calculated using criteria of a net temperature change of 0.51 C (crossed box) and 1 1C (circle) from the sea surface; and a temperature gradient criteria of 0.01 1C m1 (small cross). In density, mixed layer depth is determined using criteria of a net density change of 0.125st units from the surface isopycnal (crossed box), a density gradient of 0.01st units m1 (circle), and the thermal expansion method of eqn [1] (cross). Note the barrier layer defined as the difference between the deeper isothermal layer and the shallow density-defined mixed layer in (b).

both mixed layer depth definitions will distinguish weakly stratified regions from unstratified. Another form of the net change criterion used to define the mixed layer depth (mld) takes advantage of the equivalence of temperature and density changes based upon the thermal expansion coefficient (a0 ¼ dTdr/dT, where dT is the net change in temperature from the surface, e.g., 0.2–1 1C, and dr/dT is calculated from the equation of state for seawater using surface temperature and salinity values). This criterion thus determines the depth at which density is greater than the surface density by an amount equivalent to the dT temperature change. In this way, this definition has the advantage of revealing mixed layers where salinity stratification may be important, such as in barrier layers, which are discussed further below. Criteria based on salinity changes, although inherent in the density criterion, are not evident in the literature as typically heat fluxes are large compared to freshwater fluxes, and the gravitational stability of the water column is often controlled by the temperature stratification. In addition, subsurface salinity observations are not as regularly available as temperature. To illustrate the differences between the mixed layer depth criteria, Figure 2(a) shows the mixed

layer depth from an expendable conductivity– temperature–depth (XCTD: see Expendable Sensors) profile, using the net temperature (0.5 1C) and density (0.125 kg m 3) change criteria, the gradient density criterion (0.01 kg m 3), and a net change criterion based on the thermal expansion coefficient with dT ¼ 0.51C. In this particular case, there is little difference between the mixed layer depth determined from any method or property. However, Figure 2(b) shows an XCTD cast from the western Pacific Ocean, and the strong salinity halocline that defines the bottom of the upper mixed layer is only correctly identified using the density-defined criteria. Finally, to illustrate the distinction between the surface layer and the upper mixed layer, Figure 3(a) shows a temperature section of the upper 300 m from Auckland to Seattle during April 1996. The corresponding temperature stratification (i.e., the vertical temperature gradient) is shown in Figure 3(b). The surface layer, determined as the depth of the climatological minimum SST isotherm, and also the mixed layer depth from a 1 1C net temperature change from the surface (i.e., SST – 1 1C) are indicated on both panels. This cross-equatorial north–south section also serves to illustrate the seasonal differences expected in the mixed layer. In the early fall of the Southern



Hemisphere, the net temperature mixed layer depth criterion picks out the top of the remaining seasonal thermocline, as depicted by the increase in temperature stratification in Figure 3(b). The mixed layer depth criterion therefore excludes information about the depth of the prior winter local wind stirring or heat exchange at the air–sea surface that has been successfully captured in the surface layer using the historical minimum SST criterion. In the Northern Hemisphere tropical regions where there is little seasonal cycle, the surface layer and the mixed layer criteria are nearly coincident. The depth of the mixed layer and the surface layer extend down to the main thermocline. Finally, in the early-spring northern latitudes, the mixed layer criterion again mainly picks out the upper layer of increased stratification that was likely caused through early seasonal surface heating. The surface layer definition lies deeper in the water column near the main thermocline, and below a second layer of (a)

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relatively low stratification (Figure 3(b)). The deeper, weakly stratified region indicates the presence of fossil layers, which are defined in the next section.

Variability in Upper Ocean Vertical Structure Fossil Layers

Fossil layers are nearly isothermal layers that separate the upper well-mixed layer from a deeper wellstratified layer (see Figure 3(b), 31–371 N). The fact that these layers are warmer than the local minimum SST defining the surface layer depth, indicates that they have at some time been subject to local surface forcing. The solar heating and reduced wind stirring of spring can cause the upper layer to become thermally restratified. The newly formed upper mixed layer of light, warm water is separated from the

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older, deeper winter mixed layer by a well-stratified thermocline. The fairly stable waters in this seasonal thermocline may isolate the lower isothermal layer and prevent further modification of its properties, so that this layer retains the water characteristics of its winter formation period and becomes ‘fossilized’. Hence, fossil layers tend to form in regions with significant seasonal heating, a large annual range in wind stress, and deep winter mixed layers. These conditions can be found at the poleward edges of the subtropical gyres. In the northeast Pacific Ocean off California and in the southwest Pacific Ocean near New Zealand, particularly deep and thick fossil layers have been associated with the formation of subtropical mode waters. As with the fossil layers, the mode waters are distinguishable by low vertical gradients in temperature and density, and thus a narrow range or ‘mode’ of property characteristics. The isothermal layer or thermostad of winter water trapped in the fossilized layers may be subducted into the permanent thermocline through the action of Ekman pumping, in response to a curl in the wind field. The mode waters are then transported, retaining their characteristic thermostad, with flow in the subtropical gyre. Not all fossil layers are associated with mode water formation regions. Shallow fossil layers have also been observed where there are strong diurnal cycles, such as in the western equatorial Pacific Ocean. Here, the fossil layers are formed through the same alternating processes of heating/cooling and wind mixing as found in the mode water formation regions. Fossil layers have also been observed around areas of abrupt topography, such as along-island chains, where strong currents are found. In this case, the fossil layers are probably formed by the advection of water with properties different from those found in the upper mixed layer. Barrier Layers

In some regions, the freshwater flux can dominate the mixed-layer thermodynamics. This is evident in the Tropics where heavy precipitation can cause a surfacetrapped freshwater pool that forms a shallower mixed layer within a deeper nearly isothermal layer. The region between the shallower density-defined well-mixed layer and the deeper isothermal layer (Figure 2(b)) is referred to as a salinity-stratified barrier layer. Recent evidence suggests that barrier layers can also be formed through advection of fresh surface water, especially in the equatorial region of the western Pacific. In this region, westerly wind bursts can give rise to surface-intensified freshwater jets

that tilt the zonal salinity gradient into the vertical, generating a shallow halocline above the top of the thermocline. Furthermore, the vertical shear within the mixed layer may become enhanced in response to a depth-dependent pressure gradient setup by the salinity gradient and the trapping of the wind-forced momentum above the salinity barrier layer. This increased shear then leads to further surface intensified advection of freshwater and stratification that can prolong the life of the barrier layer. The barrier layer may have important implications on the heat balance within the surface layer because, as the name suggests, it effectively limits interaction between the ocean mixed layer and the deeper permanent thermocline. Even if under light wind conditions water is entrained from below into the mixed layer, it will have the same temperature as the water in this upper layer. Thus, there is no heat flux through the bottom of the mixed layer and other sinks must come into play to balance the solar warming that is confined to the surface, or more likely, the barrier layer is transient in nature. Inversions

Occasionally temperature stratification within the surface layer can be inverted (i.e., cool water lies above warmer water). The temperature inversion can be maintained in a stable water column since it is density-compensated by a corresponding salinity increase with depth throughout the inversion layer. Inversions are a ubiquitous feature in the vertical structure of the surface layer from the equator to subpolar latitudes, although their shape and formation mechanisms may differ. Inversions that form in response to a change in the seasonal heating at the surface are most commonly found in the subpolar regions. They can form when the relatively warmer surface water of summer is trapped by the cooler, fresher conditions that exist during winter. The vertical structure of the surface layer has a well-mixed upper layer in temperature, salinity, and density, lying above the inversion layer that contains the halocline and subsequent pycnocline (Figure 4(a)). Conversely, during summer, the weak subpolar solar heating can trap the very cold surface waters of winter, sandwiching them between the warmer surface and deeper layers. In this case, the vertical structure of the surface layer consists of a temperature minimum layer below the warm stratified surface layer, and above the relatively warmer deeper layer (Figure 4(b)). The density-defined mixed layer occurs above the temperature minimum. With continual but slow summer heating, the cold water found in this inversion layer slowly mixes with the



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Figure 4 Temperature (black line), salinity (blue line), and density (st, green line) from XCTD casts at (a) 58.21 N, 147.31 W in March 1996, (b) 611 S, 63.91 W in January 1997, (c) 11.91 S, 176.11 W in August 1998, and (d) 33.51 N, 134.61 W in May 1995. Note the presence of temperature inversions at the base of the mixed layer in all casts.

warmer water masses above and below, and erodes away. Inversions can also form through horizontal advection of water with different properties known as water-mass interleaving. For example, in the Tropics

where there may be velocity shear between opposing currents, inversions are typically characterized as small abrupt features (often only meters thick) found at the base of a well-mixed upper layer and at the top of the halocline and pycnocline (Figure 4(c)). Just west of


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San Francisco (130–1401 W), the low temperature and salinity properties of the Subantarctic Water Mass found in the California Current transition toward the higher-salinity water masses formed in the evaporative regime of the mid-subtropical gyre. The interleaving of the various water masses results in inversions that are quite different in structure from those observed in the tropical Pacific or the subpolar regions (Figure 4(d)). The surface layer vertical structure may be further complicated by frequent energetic eddies and meanders that perturb the flow and have their own distinctive water properties. In the transition zone, the inversions can be thick, and occur well within the pycnocline and not at the base of the mixed layer (Figure 4(d)). Typically there may be sharp gradients in temperature and salinity, both horizontally and vertically, that are characteristic of water-mass interleaving from the advective penetrations of the currents and eddies.

Other Properties That Define the Upper Ocean Vertical Structure Other water properties, such as dissolved oxygen and nutrients (e.g., phosphates, silica, and nitrates), can also vary in structure in the upper ocean surface layer. These properties are considered to be nonconservative, that is, their distribution in the water column may change as they are produced or consumed by marine organisms. Thus, although they are of great importance to the marine biology, their value in defining the physical structure of the upper ocean surface layer must be viewed with caution. In addition, until recently these properties were not routinely measured on hydrographic cruises. Nonetheless, the dissolved oxygen saturation of the upper ocean has been a particularly useful property for determining the depth of penetration of air–sea exchanges, and also for tracing water masses. For example, in the far North Pacific Ocean, it has been suggested that the degree of saturation of the dissolved oxygen concentration may be a better indicator than temperature or density for determining the surface-layer depth of convective events. During summer, the upper layer may be restratified in temperature and salinity through local warming or freshening at the surface, or through the horizontal advection of less dense waters. However, these surface processes typically do not erode the high-oxygen saturation signature of the deeper winter convection. Thus the deep high-oxygen saturation level provides a clear record of the depth of convective penetration from the air–sea exchange of the previous winter, and a unique signal for defining the true depth of the surface layer.

Conclusions In its simplest form the vertical structure of the upper surface layer can be characterized as having a nearsurface well-mixed layer, below which there may exist a seasonal thermocline, where temperature changes relatively rapidly, connected to the permanent thermocline or main pycnocline. The vertical structure is primarily defined by stratification in the water properties of temperature, salinity, and density, although in some regions oxygen saturation and nutrient distribution can play an important biochemical role. The vertical structure of the surface layer can be complex and variable. There exist distinct variations in the forms and thickness of the upper-layer structure both in time and in space, through transient variations in the air–sea forcing from winds, heat, and fresh water that cause the turbulent mixing of the upper ocean. Understanding the variation in the upper ocean vertical structure is crucial for understanding the coupled air–sea climate system, and the storage of the heat and fresh water that is ultimately redistributed throughout the world oceans by the general circulation.

See also Air–Sea Gas Exchange. Bottom Water Formation. Deep Convection. Ekman Transport and Pumping. Expendable Sensors. Heat and Momentum Fluxes at the Sea Surface. Ocean Circulation: Meridional Overturning Circulation. Ocean Subduction. Open Ocean Convection. Penetrating Shortwave Radiation. Penetrating Shortwave Radiation. Upper Ocean Heat and Freshwater Budgets. Upper Ocean Mean Horizontal Structure. Upper Ocean Mixing Processes. Upper Ocean Time and Space Variability. Water Types and Water Masses. Windand Buoyancy-Forced Upper Ocean. Wind Driven Circulation.

Further Reading Cronin MF and McPhaden MJ (2002) Barrier layer formation during westerly wind bursts. Journal of Geophysical Research 107 (doi:10.1029/2001JC00 1171). Kraus EB and Businger JA (1994) Oxford Monographs on Geology and Geophysics: Atmosphere–Ocean Interaction, 2nd edn. New York: Oxford University Press. Philips OM (1977) The Dynamics of the Upper Ocean, 2nd edn. London: Cambridge University Press. Reid JL (1982) On the use of dissolved oxygen concentration as an indicator of winter convection. Naval Research Reviews 3: 28--39.


UPWELLING ECOSYSTEMS R. T. Barber, Duke University Marine Laboratory, Beaufort, NC, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3128–3135, & 2001, Elsevier Ltd.

Introduction An ecosystem is a natural unit in which physical and biological processes interact to organize the flow of energy, mass, and information. The result of this selforganizing activity is that each kind of ecosystem has a characteristic trophic structure and material cycle, some degree of internal homogeneity, objectively definable boundaries, and predictable patterns of seasonality. Oceanic ecosystems are those ecosystems that exist in the open ocean independently of solid substrates; for example, oceanic ecosystems are fundamentally distinct from coral or intertidal ecosystems. Upwelling ecosystems are those that occupy regions of the ocean where there is a persistent upward motion of sea water that transports subsurface water with increased inorganic plant nutrients into the sunlit surface layer. The upwelling water is not only rich in nutrients, but also frequently cooler than the surface water it replaces; this results in a variety of atmospheric changes, such as coastal deserts or arid zones. The increased nutrient supply and favorable light regime of upwelling ecosystems, however, distinguish them from other oceanic ecosystems and generate characteristic food webs that are both quantitatively and qualitatively different from those of other oceanic ecosystems. For persistent upwelling to take place it is necessary for the surface layer to be displaced laterally in a process physical oceanographers call divergence and then for subsurface water to flow upward to replace the displaced water. The physical concept of upwelling is simple in principle but, as with many ocean processes, it becomes surprisingly complex when real examples are studied. To begin with, there are two fundamental kinds of upwelling ecosystems: coastal and oceanic. They differ in the nature of their divergence. In coastal upwelling, the surface layer diverges from the coastline and flows offshore in a shallow layer; subsurface water flows inshore toward the coast, up to the surface layer, then offshore in the surface divergence. In contrast, oceanic upwelling, which occurs in many regions of the ocean, depends on the divergence of one surface layer of water from

another. One such oceanic divergence is created when an increasing gradient in wind strength forces one surface layer to move faster, thereby leaving behind, or diverging from, another surface layer. Major regions of this kind of oceanic upwelling are found in high latitudes in the Subpolar gyres of the Northern Hemisphere and the Antarctic divergence in the Southern Ocean. The food webs of polar upwelling ecosystems are described elsewhere in the Encyclopedia and this article will focus on coastal and equatorial upwelling ecosystems that occur in low and mid-latitude regions of the world’s oceans. The physical boundary organizing oceanic divergence in equatorial upwelling is the Coriolis force, which changes sign at the equator, causing the easterly Trade Winds to force a northward divergence north of the Equator and a southward divergence south of the Equator. Both coastal and equatorial upwelling ecosystems have been well studied in recent years, so they are among the best known of oceanic ecosystems. The physical processes of equatorial upwelling are described elsewhere in the Encyclopedia. This article describes the quantitative and qualitative character of the food webs of coastal upwelling ecosystems, focusing especially on how their physical forces and chemical conditions affect the way food webs pass organic material to higher trophic level organisms such as fish, birds, marine mammals, and humans.

Why are Upwelling Ecosystem Food Webs different? In low- and mid-latitude oceanic systems where there is annual net positive heat flux, warming of the surface layer produces a density barrier, the pycnocline, that prevents subsurface nutrient-rich water from mixing into the sunlit surface layer. The nutrientdepleted condition of these surface waters severely limits their annual quantity of new primary productivity, and the food webs of these stratified oceanic regions have low phytoplankton biomass, as shown especially clearly in satellite images of ocean color. In the high-latitude polar regions where there is annual net negative heat flux, the surface waters cool, become unstable, and mix with the underlying nutrient-rich subsurface waters. The concentration of inorganic nutrients in well-mixed high-latitude waters is high during polar fall, winter, and spring during periods of strong winds, heat loss to the atmosphere, short day length, and low sun angle. But


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during periods of deep convective mixing the phytoplankton population may spend so much time below the euphotic zone that there is no net positive primary production. The primary producers under these conditions are said to be light-limited. Upwelling is a circulation pattern that overrides both the nutrient limitation of stratified low- and mid-latitude waters and the light limitation of highlatitude polar waters. Upwelling ecosystem food webs are different from those of other oceanic ecosystems because (1) optimal conditions of nutrient supply are provided by the upward flow of cool, nutrient-rich subsurface water into the sunlit surface layer and (2) optimal light conditions are provided for maximal photosynthetic production of new organic matter in the divergent horizontal flow of upwelled water as it gains heat from the sun, producing a well-stabilized, stratified surface flow. Optimal nutrient conditions are formally defined as having nutrient [NO3 , PO4 3 , Si(OH)4] concentrations well above those that saturate the phytoplankton cell’s nutrient uptake mechanism; i.e., [N]bKs, where [N] is nutrient concentration in mole units and Ks in one-half the concentration required for nutrient uptake saturation. Optimal light conditions are formally defined as having a level of irradiance, or photon flux density, in the upper waters that exceeds considerably the irradiance required to saturate the photosynthetic capacity of the phytoplankton assemblage; i.e., [E]bKE where [E] is irradiance in mol photons m 2 s 1 in surface waters and KE is irradiance at saturation. In coastal and equatorial upwelling ecosystems, optimal nutrient and light conditions for high primary production are maintained for several months or longer each year, and in low-latitude Trade Wind regions they persist for the entire year; therefore, the annual quantity of new organic matter generated by primary productivity is much higher in upwelling regions than in other oceanic ecosystems that are nutrient- or light-limited or dependent on one or two seasonal pulses of convective mixing.

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The Physical Setting Upwelling is a response of the ocean to wind-driven divergence of the surface layer. As the wind begins to blow across the surface of the ocean, a thin surface slab of water (25–50 m thick) is set in motion by friction of the wind (Figure 1). This wind-driven layer or Ekman layer (named for the Swedish oceanographer who in 1905 worked out how wind drives ocean currents), as a result of the Coriolis force, has a net movement 901 to the right (left) of

the wind in the Northern (Southern) Hemisphere. Four of the major coastal upwelling systems are located in the eastern boundary of the ocean basins along the west coasts of the continents where equatorward winds are part of stationary or seasonal mid-ocean high-pressure systems. These four coastal upwelling regions off the west coasts of North America, South America, north-west Africa, and south-west Africa are in the four great eastern



boundary current systems, the California Current, Peru Current System, Canary Current, and Benguela Current. The fifth major coastal upwelling region is in a western boundary current, the Somali Current, where strong summer monsoon winds blowing along the coast of the Arabian peninsula set in motion a north-east flow that then diverges from the coast due to Coriolis deflection to the right in the Northern Hemisphere. In all five regions winds blow parallel to the coast for a long enough period of time (months) and over a sufficiently large length of coastline to develop a distinct coastal upwelling ecosystem. Coastal upwelling is a mesoscale (10–100 km) physical response to a large-scale coastal wind field. The major zone of upwelling is relatively small, extending offshore only 25–50 km from the coast, and the water upwelling to the surface layer is coming from a relatively shallow depth of 40–80 m or just below the pycnocline. Because of a basin-wide tilt in the east/west direction, the pycnocline in the eastern boundary current regions is shallower than in other regions of the ocean basin, making nutrient-rich subpycnocline water readily available for entrainment into the upwelling circulation.

The Chemical Environment The sine qua non of coastal upwelling is high concentrations of the new inorganic plant nutrients nitrate (NO3 ), phosphate (PO4 3 ), and silicate or silicic acid (Si[OH]4) that are well in excess of the half-saturation concentrations for nutrient uptake. Typical concentrations are as high as 15–20 mmol l 1 of NO3, with the other macronutrients occurring in appropriate proportional concentrations according to the Redfield ratio. The highest nutrient concentrations and lowest water temperatures are inshore in the most recently upwelled water; there is frequently a strong offshore spatial gradient in nutrient concentration, but the spatial domains of the five great coastal upwelling ecosystems vary remarkably. In the Peruvian upwelling near 151S latitude the onshore/ offshore gradients are steep, with nitrate concentrations decreasing from 20 to 2 mmol l 1 in an offshore distance p50 km; in the Somali Current off the coast of Oman the initial inshore concentrations are lower, about 10 mmol l 1, but remain elevated for 500–700 km offshore. The supply of new nutrients advected into the euphotic zone sets up the highly productive character of upwelling ecosystems, but nutrients regenerated or recycled in the euphotic zone are also unusually abundant in coastal upwelling. High-productivity fuels increased heterotrophic consumption by protozoans, crustaceans, and vertebrates, and these consumers,

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along with heterotrophic bacteria, bring about increased regeneration of nutrients. Regeneration of nutrients from particulate organic matter that sinks out of the offshore surface flow and into the subsurface inshore flow results in nutrient ‘trapping’ that maintains elevated nutrient concentrations in bottom waters of the continental shelf. These regenerated nutrients, together with short-term storage of regenerated nutrients in surficial sediments beneath the upwelling circulation, provide a flywheel to the nutrient supply process that dampens variations in the wind-driven vertical transport of new nutrients from deep water. An additional important chemical consequence of trapping by the two-layered partitioning of organic particles in coastal upwelling is the generation of zones of intense oxygen depletion. The great oxygen minimum zones of the four eastern boundary currents and the Somali Current are fueled by enhanced productivity in the narrow coastal upwelling zone. In addition to water column oxygen depletion, shelf and slope sediments under coastal upwelling are frequently anoxic and colonized by large anaerobic bacterial mats. These benthic hypoxic and anoxic zones are two sites of intense denitrification, a microbial process by which nitrate is converted to nitrogen gas. Occasionally, oceanographers have found complete denitrification in a midwater anoxic layer beneath upwelling systems; these processes of benthic and water column denitrification may be a major global feedback mechanism involved in the regulation of fixed, or biologically available, nitrogen. Another important chemical consequence of the reducing conditions generated in anoxic and hypoxic sediments beneath coastal upwelling involves the cycling of iron. Iron is an essential micronutrient for the maintenance of high rates of primary productivity. Studies in the coastal upwelling ecosystem of the California Current System showed that resuspension and dissolution of iron from sediments generated enhanced concentrations of iron in the bottom boundary currents. Subsequent upwelling of this subsurface water during episodes of strong upwelling resulted in elevated iron concentrations in the euphotic layer. Particle sedimentation to anoxic or hypoxic sediments followed by resuspension and dissolution is a positive feedback that enhances the productive potential of coastal upwelling, especially compared to open ocean equatorial upwelling.

A Milestone in Quantifying Food Web Function The basic food webs of upwelling ecosystems differ in both quantity and quality from those of other


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oceanic ecosystems. A milestone in understanding these differences was made by John Ryther, who in 1969 provided a quantitative explanation of why fish yields vary by about 200-fold from the richest coastal upwelling ecosystems to the poorest ocean gyres. Variations in productivity are, of course, well known from terrestrial ecosystems, but on land a lack of water from either aridity in deserts or freezing in polar regions is responsible for the productive poverty of the poorest regions. Understanding why the food web of the benign low-latitude gyre ecosystem was so poor in fish production was much more difficult. Part of the explanation was proposed in 1955 by Sverdrup who stated simply that reduced physical supply of nutrients to the euphotic zone is the reason for the low productivity, biomass, and fish yields of stratified oceanic gyre ecosystems. Ryther amplified this simple physical explanation by considering, along with the physics and chemistry, the biological properties of the food web that lead to fish production. First, Ryther estimated that about half the fish caught in the world are caught in coastal upwelling ecosystems, the smallest of the ocean ecosystems. Why? To begin, Sverdrup was correct: the physical processes of upwelling and subsequent stratification provide optimal nutrients and light to support high primary productivity. However, more is involved. The phytoplankton, especially diatoms, that thrive in coastal upwelling are large – so large that some portion of the diatoms can be eaten directly by fish or other large grazers such as euphausids. This means that in coastal upwelling the food web leading to fish is often very short, involving only one, or at most two, trophic transfers. Ryther estimated that in the Peru upwelling ecosystem half of the diet of the small pelagic clupeid fish such as anchovies is phytoplankton and the other half is composed of crustacean zooplankton such as euphausids. On average, then, the length of the food web from primary producers to fish had 1.5 transfers: large diatoms to anchovies, or large diatoms to euphausids to larger fish such as mackerel. At each ecological transfer, a large portion (80–90%) of the energy of the food is used to support the organism and that portion cannot be passed up the food web. Ryther also noted that in the phytoplankton-rich waters of the spatially small coastal upwelling regions, grazers do not have to work so hard to get food; therefore, the efficiency of transfer through the food web is increased relative to that of a poor environment such as the low-latitude gyre, where grazers have to cover larger distances and filter large volumes of water to get adequate food. Ryther proposed that fish yields are high in the coastal upwelling ecosystem because of

(1) high initial primary productivity, (2) large phytoplankton that can be grazed directly, (3) short food webs with few transfers, and finally, (4) increased efficiency at each transfer. These effects multiply and lead to high yields of fish that are 200 times the yield of gyre ecosystems. These high yields are exploited by seabirds, marine mammals and, of course, humans.

Food Web Structure and Function Coastal upwelling ecosystems are typically dominated by chain-forming and colonial diatoms with individual cell diameters of 5–30 mm. The growth rates of these large cells are surprisingly as fast as those of the much smaller autotrophic pico- and nanoplankton that are the basis of the microbial loop. The larger diatoms are more effective than pico- or nanoplankton at taking up high concentrations of new nutrients; this property, together with their more favorable photosynthesis/respiration ratio, makes diatoms considerably more efficient at new production. New production uses nutrients carried into the system by upwelling, while regenerated production is based on nutrients recycled in the euphotic zone. The f-ratio measures the proportion of new production; f-ratios of coastal upwelling are as high as any in the oceans, with values ranging from 0.3 to 0.8 and 0.5 being a representative value. Primary productivity values in the most productive portion of the upwelling ecosystem range from 1.0 to 6.0 mg C m 2 d 1. Representative inshore values for the California Current System are 1.0–3.0 mg C m 2 d 1; for the Peru Current System 2.0–6.0 mg C m 2 d 1; for the Canary Current 1.0 – 3.0 mg C m 2 d 1; and for the Somali Current 1.0–2.0 mg C m 2 d 1. High f-ratios and high primary productivity indicate that more organic material can be exported via the food web to higher trophic levels such as fish, birds, and marine mammals or exported vertically as particle flux to deep water or sediments. A second element in Ryther’s hypothesis was that large diatoms could be grazed directly by clupeid fishes. Why are the phytoplankton in coastal upwelling large? One explanation comes from a model study of diatom sinking and circulation in the Peruvian upwelling region. Small phytoplankton that sank slowly or maintained themselves in the euphotic zone were consistently carried in the surface Ekman layer to the oligotrophic offshore waters; large diatoms that sank rapidly fell into the subsurface onshore circulation and were carried back into the upwelling cycle (Figure 1A). Large size that confers



fast sinking is an adaptation that keeps diatoms in the highly productive upwelling habitat for several growth cycles. In addition, newly upwelled water contains large numbers of diatom resting spores, indicating that diatoms sink to sediment, remain there in a resting stage, then become resuspended and transported into the euphotic zone by episodes of strong upwelling. Large size confers rapid sinking, which enhances both recirculation and resuspension, but it also makes the large diatoms efficient prey for fish and large zooplankton like euphausids. The biomass of larger phytoplankton such as diatoms is more variable in time and space than the biomass of pico- and nanophytoplankton. The abundance of small phytoplankton is efficiently controlled by their fast-growing protozoan microzooplankton grazers. The micrograzers can grow as rapidly as their prey, so there is no opportunity for uncoupling of prey and predator abundance; picoand nanoplankton, therefore, rarely form blooms. In contrast, diatoms are grazed by larger organisms with longer reproductive cycles, such as clupeid fish with a 1-year cycle or copepods and euphausids with a cycle of 10–40 days or longer. Clearly, the zooplankton or fish cannot reproduce fast enough to keep up their abundance in pace with a diatom bloom; at times, therefore, large diatoms can accumulate in dense blooms with low initial grazing losses. While fish and zooplankton cannot match growth rates with diatoms, they do have mobility and behavior that enable them to find and move into patches of abundant food. In practice, however, coupling of the growth rates of diatoms and their animal grazers frequently breaks down, and when this happens high biomass blooms become evident in the ocean color satellite images in upwelling regions. Phytoplankton cells, especially large cells that are not grazed or consumed by heterotrophic microorganisms, rapidly sink out of the water column when ungrazed biomass accumulates in a dense bloom (Figure 1A). If nutrients are depleted by the high-biomass bloom, phytoplankton lose the ability to regulate their buoyancy and sink rapidly at rates as high as 100 m d 1. Sediments under coastal upwelling ecosystems are characterized by the highest rate of organic deposition found in the ocean. These high deposition rates indicate that the large diatom/ large grazer food path is relatively more important to the throughput of material than the microbial or picophytoplankton/nanophytoplankton/protozoan grazer path. The microbial path is always present in the two-path upwelling food web and it does increase in absolute productivity during increased upwelling; however, the huge increase in biomass and productivity of the large diatom/large grazer food

229

path dominates export of new organic material. The large diatom food path does not replace the picophytoplankton/nanophytoplankton path, but it becomes so numerically overwhelming that it appears as though there is a shift in the character of the food web. In coastal upwelling ecosystems there is enough time and space constancy in the physical response that macrozooplankton and shoaling pelagic fish have been able to evolve adaptations that enable them to exploit this rich but small habitat, and these adaptations affect the efficiency of transfer of primary production to higher trophic levels. Zooplankton such as copepods and euphausids have limited ability to swim against onshore–offshore currents, but they have considerable ability to migrate up and down rapidly. Some upwelling zooplankton species have evolved behavior that causes them, when saturated with food in the offshore flow, to migrate down into the onshore flow, which then carries them back into the upwelling circulation for another cycle. Other species remain in the food-rich habitat by having eggs or juvenile life stages that sink into the subsurface onshore flow. The adaptations of macrozooplankton to the physics of upwelling are remarkable examples of how the evolution of upwelling organisms differs from the evolution of organisms of other oceanic ecosystems. Parallel adaptations are present in the shoaling pelagic fish that dominate the fish biomass of coastal upwelling ecosystems. These behavioral adaptations have optimized feeding, reproduction, and growth for the sardines, anchovies, and mackerel that make up the bulk of the fish harvested from coastal upwelling ecosystems.

Climatic Forcing and Food Web Responses Adaptations to the specific upwelling circulation pattern confer great fitness advantage to phytoplankton, zooplankton, fish, birds, and marine mammals when the upwelling pattern is prevalent, but the coastal upwelling ecosystems are buffeted by strong interannual and interdecadal climate variability. The El Ninõ–Southern Oscillation (ENSO) phenomenon is the best-known example of largescale, climate-driven biological variability. El Ninõ is defined by the appearance and persistence, for 6–18 months, of anomalously warm water in the coastal and equatorial ocean off Peru and Ecuador. The anomalous ocean conditions of El Ninõ are accompanied by large reductions of plankton, fish, and sea birds in the normally rich upwelling region. To


understand how this climate variability causes these large decreases in abundance, consider how El Ninõ temporarily alters the physical pattern of the upwelling circulation. One discovery of recent decades is that during El Ninõ events the coastal winds that drive coastal upwelling do not stop entirely (Figure 1B). In fact, coastal winds sometimes intensify during El Ninõ because of increased thermal differences between land and sea. Therefore, coastal upwelling as a physical process continues, but because the ENSO process has depressed the thermocline and nutricline to a depth below the depth at which water is entrained into the upwelling circulation (40–80 m), the water upwelled is warm and low in nutrients. As a result, during El Ninõ the upwelling circulation transports only warm, nutrient-depleted water to the surface layer. The physics of upwelling continues, but the chemistry of upwelling stops very dramatically. This conceptual model of El Ninõ forcing and food web response, shown in Figure 1B, indicates that El Ninõ affects the upwelling ecosystem by decreasing the nutrients supplied to the euphotic layer, which causes primary production to decrease proportionally. In this manner the supply of nutrients is reduced as El Ninõ strengthens in intensity, and the decrease in new primary production available to fuel the food web causes proportional reductions in the growth and reproductive success of fish, birds, and marine mammals. Obviously, temperature, nutrients, primary productivity and higher trophic level productivity are tightly linked in coastal upwelling ecosystems, but by far the most dramatic link is the climate variability/fish variability link. That is, the most impressive biological consequence of El Ninõ is its effect on the abundance and catch of Peruvian anchovy (Engraulis ringens), the basis of the world’s largest single-species fishery. Figure 2 shows the covariation of thermal conditions and anchovy harvest from the 1950s to the present. This relationship is causal in the sense that temperature is a proxy for nutrients, and nutrient decreases (temperature increases) are always accompanied by reduction in the productivity of the food web including the catch of anchovies. Note that the temperature/nutrient variability works in both directions. Each local minimum in catch is associated with a warm anomaly and each local maximum is associated with cool, nutrient-rich conditions. The period of very low catch from 1976 to 1985 is often cited as an example of the destruction of a fishery by overfishing, but Figure 2 indicates that the anchovy stock failed to recover from 1972 and 1976 El Ninõ events because there was little upwelling of cool, nutrient-rich waters during that decade. The coastal winds were normal

1955

1965

1975

1985

1995

14 12 10 8 6 4 2 0 1955

1965

1975

1985

1995

_3 _2 _1 0 1 2 3 4 5 6

Monthly SST anomaly (SD) ~ 1+2 for Nino


Annual catch (106 t)

230

Figure 2 The association of sea surface temperature (SST) anomaly along the coast of Peru and Ecuador with the annual catch of Peruvian anchovy, showing that each minimum of catch is associated with a period of anomalously warm water. Note that the SST anomaly scale is inverted, with red showing the warm anomalies. The anomaly is calculated from SST in the Peru coastal area (Ninõ 1) and the eastern equatorial Pacific (Ninõ 2). Warmer water at the sea surface means that warmer water is being entrained into the upwelling cell because the thermocline has deepened owing to large-scale, basin-wide responses to changes in Trade Winds. The nutricline also deepens, so that the warmer water is also lower in nutrient concentration. Temperature is a proxy in this figure for nutrient concentration. The close association of SST anomaly and anchovy catch suggests that natural thermal and nutrient variability, not overfishing, is the process controlling the interannual variability of this particular fish stock.

or even stronger than normal during this decade, but the increased heat storage in the upper ocean apparently kept the thermocline and nutricline anomalously deep from 1976 to 1985. The extreme variability of anchovy abundance sends shock waves into the global economy, because fishmeal from upwelling ecosystems is a commodity that is necessary for a variety of animal production processes. The social hardship of this climate-driven variability affects many people, but the upwelling ecosystem is not in the least damaged by ENSO variability. The food web has evolved to exploit the productive phase of the ENSO cycle and persist through the unproductive phase. Figure 2 shows that as long as a period of cool, high-nutrient conditions follows the warm event, the coastal upwelling system recovers to its previous high productivity. The climate process that appears to have the potential to alter or disrupt this ecosystem is the lower-frequency, decadal anomaly that prevailed from 1976 to 1985 and again in the mid-1990s. A decadal anomaly that causes relative nutrient poverty appears to have greater long-term food web consequences than short



periods of extreme nutrient depletion during El Ninõ events. How is the character of the coastal upwelling ecosystem altered during a strong El Ninõ? When the group of equatorially and coastally trapped waves excited during onset of an ENSO event forces the nutricline below the depth where upwelling entrains water, the coastal system rapidly develops a typical assemblage of tropical plankton. Dense blooms of diatoms are missing, but the tropical pico- and nanophytoplankton-based food web is healthy and has productivity levels typical of tropical waters. The diversity of phytoplankton, zooplankton, and fish is high – as would be expected in tropical waters. The response of the upwelling food web to climate variability emphasizes the resilience of oceanic ecosystems to strong transient perturbations; their resilience to the effects of persistent change, however, is unknown.

Glossary Antarctic divergence The zone of upwelling driven by the Antarctic Circumpolar Current (ACC). Convective mixing Vertical mixing produced by the increasing density of a fluid in the upper layer, especially during winter in temperate and polar regions. Denitrification A microbial process that takes place under anoxic conditions, converting nitrate to N2 gas. Diatom A taxonomic group of phytoplankton that are nonmotile, have silicon frustules, and are capable of rapid growth. Ecosystem A natural unit in which physical and biological processes interact to organize the flow of energy, mass, and information. Ekman layer The surface layer of the ocean that responds directly to the wind. Euphotic zone The surface layer of the ocean where there is adequate sunlight for net positive photosynthesis. Nutrients Dissolved mineral salts necessary for primary productivity and phytoplankton growth; macronutrients are phosphate, nitrate, and silicate; micronutrients are iron, zinc, manganese, and other trace metals. Oxygen minimum zone A mid-water layer along the eastern boundary regions of the oceans in which oxygen concentrations are significantly reduced relative to the layers above and below it. Phytoplankton Photosynthetic single-called plants or bacteria that drift with ocean currents and are the major primary producers for oceanic food webs; very small phytoplankton are called

231

picoplankton and small phytoplankton are called nanoplankton; all of these are o2 mm in diameter. Primary productivity The use of chemical or radiant energy to synthesize new organic matter from inorganic precursors. Pycnocline The layer where density changes most rapidly with depth and separates the surface mixed layer from deeper ocean waters. Southern Ocean The circumpolar ocean in the Southern Hemisphere between the Subtropical Front and the continent of Antarctica. Stratification The formation of distinct layers with different densities (see ‘pycnocline’ above); stratification inhibits mixing or exchange between the nutrient-rich deeper water and the sunlit surface layer. Subpolar gyres Large cyclonic water masses in the Northern Hemisphere between the subtropical front and the polar front. Tropical Pertaining to the regions that, under the influence of the Trade Winds, are permanently stratified. Upwelling Upward vertical movement of water into the surface mixed layer produced by divergence of the surface waters. Zooplankton Animals that float or drift with ocean currents; microzooplankton are protozoan plankton that graze on small phytoplankton; mesozooplankton are crustaceans that graze on larger phytoplankton such as diatoms.

See also Antarctic Circumpolar Current. California and Alaska Currents. Canary and Portugal Currents. Ekman Transport and Pumping. El Ninõ Southern Oscillation (ENSO). El Ninõ Southern Oscillation (ENSO) Models. Fisheries and Climate. Iron Fertilization. Microbial Loops. Network Analysis of Food Webs. Nitrogen Cycle. Satellite Remote Sensing: Ocean Color. Ocean Gyre Ecosystems. Pacific Ocean Equatorial Currents. Pelagic Biogeography. Pelagic Fishes. Plankton. Plankton and Climate. Polar Ecosystems. Primary Production Distribution. Primary Production Processes. Redfield Ratio. Small Pelagic Species Fisheries. Somali Current.

Further Reading Bakun A (1990) Global climate change and intensification of coastal ocean upwelling. Science 247: 198--201. Barber RT and Chavez FP (1983) Biological consequences of El Ninõ. Science 222: 1203--1210.


232


Barber RT and Smith RL (1981) Coastal upwelling ecosystems. In: Longhurst A (ed.) Analysis of Marine Ecosystems, pp. 31--68. New York: Academic Press. Longhurst A (1998) Ecological Geography of the Sea. San Diego: Academic Press. Pauly D and Christensen V (1995) Primary production required to sustain global fisheries. Nature 374: 255--257. Richards FA (ed.) (1981) Coastal Upwelling. Washington, DC: American Geophysical Union. Ryther JH (1969) Photosynthesis and fish production in the sea. Science 166: 72--76.

Smith RL (1992) Coastal upwelling in the modern ocean. In: Summerhayes CP, Prell WL and Emeis K-C (eds) Upwelling Systems: Evolution Since the Early Miocene, Geological Society Special Publication 64, pp. 9–28. London: The Geological Society. Summerhayes CP, Emeis K-C, Angel MV, Smith RL and Zeitschel B (eds.) Upwelling in the Ocean Modern Processes and Ancient Records. Chichester: Wiley Sverdrup HU (1955) The place of physical oceanography in oceanographic research. Journal of Marine Research 14: 287.


URANIUM-THORIUM DECAY SERIES IN THE OCEANS: OVERVIEW M. M. R. van der Loeff, Alfred-Wegener-Institut fu¨r Polar und Meereforschung Bremerhaven, Germany Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3135–3145, & 2001, Elsevier Ltd.

Introduction Natural radioactivity provides tracers in a wide range of characteristic timescales and reactivities, which can be used as tools to study the rate of reaction and transport processes in the ocean. Apart from cosmogenic nuclides and the long-lived radioisotope K-40, the natural radioactivity in the ocean is primarily derived from the decay series of three radionuclides that were produced in the period of nucleosynthesis preceding the birth of our solar system: Uranium-238, Thorium-232, and Uranium-235 (a fourth series, including Uranium-233, has already decayed away). The remaining activity of these socalled primordial nuclides in the Earth’s crust, and the range of half-lives and reactivities of the elements in their decay schemes, control the present distribution of U-series nuclides in the ocean.

The Distribution of Radionuclides of the Uranium Thorium Series in the Ocean Distribution of 238U, 235U, 234U, and 232Th (see Uranium-Thorium Series Isotopes in Ocean Profiles)

Uranium is supplied to the ocean by rivers. In sea water it is stabilized by a strong complexation as uranyl carbonate UO2(CO3)4 3 , causing its long residence time in the ocean. U follows closely the distribution of salinity with 238U (in dpm l1) ¼ 0.0704* salinity. (Note: dpm ¼ disintegrations per minute. The SI Unit Bq, 60 dpm ¼ 1 Bq, is not used in the literature on natural radioactivity in the ocean.) Under anoxic conditions, U is reduced from the soluble (VI) to the insoluble (IV) oxidation state and rapidly removed from sea water. Reductive removal occurs especially in sediments underlying high productivity or low-oxygen bottom waters. Locally this may influence the U–salinity relationship. Salinity-corrected U contents have a variation of 3.8% in the world ocean and are about 1% higher in the

Paci c than in the Atlantic Ocean. At lower salinities in estuaries, salinity-corrected U contents are much more variable as a result of removal and release processes and of interaction with organic complexants and colloids. 235 U is chemically equivalent to 238U and occurs with a 235U/238U activity ratio of 0.046. As a result of the preferential mobilization of 234U during chemical weathering, the river supply of 234U activity exceeds the supply of 238U, causing a 234U/238U ratio in the ocean greater than unity. The isotopic composition of uranium in sea water with salinity 35 is shown in Table 1. Like U, 232Th is a component of the Earth’s crust and is present in the lithogenic fraction of every marine sediment. As a result of its high particle reactivity, Th is rapidly removed from the water column. The 232Th activity in the ocean is very low (around 3 105 dpm l1 or 0.1 ng/kg) and its distribution can be compared to that of other particlereactive elements like Al or Fe. Distribution of Isotopes from the Three Decay Series

In all three decay series, isotopes of relatively soluble elements like U, Ra, and Rn, decay to isotopes of highly particle-reactive elements (Th, Pa, Po, Pb), and vice versa (Figure 1), resulting in widely different distributions in the water column (Table 2) (see Uranium-Thorium Series Isotopes in Ocean Pro les). In a closed system, given enough time, all nuclides in a decay series reach secular equilibrium. This means that growth is balanced by decay, and that all intermediate nuclides have the same activity. In a natural open system, however, reaction and transport

Table 1 Average isotopic uranium composition of sea water with salinity 35 Parameter

Value

235

3.238 ng g1 0.0460 1.14470.002

U þ 238U concentration U/238U activity ratio 234 238 U/ U activity ratio Isotope activity 238 U 234 U 235 U

235


2.46 dpm l1 2.82 dpm l1 0.113 dpm l1

233

234

URANIUM-THORIUM DECAY SERIES IN THE OCEANS: OVERVIEW

Element Uranium

Uranium-238 series U-238 4.5*109 y

Protactinium Thorium

Pa-234 1.2 min

Th-234 24.1 d

Th-232 series

U-234 245500 y

U-235 7.0*108 y

Th-230 75400 y

Th-232 1.4*1010 y

Ra-226 1600 y

Ra-228 5.75 y

Actinium Radium

U-235 series

Ac-228 6.1 h

Th-228 Th-231 1.91 y 25.5 h Ra-224 3.7 d

Pa-231 32800 y

Th-227 18.7 d

Ac-227 21.8 y Ra-223 11.4 d

Francium Rn-222 3.8 d

Radon Astatine

Po-218 3.1 min

Polonium Bismuth

Pb-214 26.8 min

Lead -decay Z: _ 2 N: _ 4

-decay Z: +1 N: +/_ 0

Po-214 0.00014 s Bi-214 19.9 min

Pb-210 22.3 y

Po-210 138 d Bi-210 5.0 d

Decay series of short-lived nuclides

Pb-206 stable

Pb-208 stable

Symbol of the element Pa-231 32500 y

Mass number

Pb-207 stable Particle reactivity

Half-life

Low Intermediate High

Figure 1 The natural uranium-thorium decay series, colored according to particle reactivity. The arrows represent decay with the changes in atomic number (Z) and number of nucleons (N) indicated. All three series end with a stable lead isotope.

Table 2 List of the elements (with isotopes of half-life t1/241 day) in the U decay series with their scavenging residence time in deep and surface ocean and their estimated particle-water partition coefficient Kd, showing the relative mobility of U, Ra, and Rn Element

Scavenging residence time (years) Deep sea

U Pa Th Ac Ra Rn

Pb Po

Surface ocean 450 000 o1 o1

130 30 decays (430) 1000 decays

50–100 decays (42)

Kd (cm3 g1)

500 1 106 1 107 0.4–2 105

0.2–3 104 gas exchange 0 with atmosphere 2 1 107 0.6 2 107

Disequilibrium: The Basis for Flux Calculations (Figure 2) Mobile Parent with Particle-reactive Daughter (Table 3)

Tracers in this group are produced in the water column and removed on sinking particles, a process called scavenging. They allow us to determine particle transport rates in the ocean. In a simple box model the total daughter activity (AtD) is determined by decay (decay constant l ¼ ln(2)/ t1/2), ingrowth from the parent nuclide (activity Ap, production rate of daughter nuclide PD ¼ lAp) and removal on sinking particles J (Figure 3): dAtD ¼ PD lAtD J ¼lðAP AtD Þ J dt

½1

In steady-state the flux is directly related to the depletion of the daughter with respect to the parent:

cause a separation between parent and daughter nuclides. The resulting disequilibria between parent and daughter nuclide can be used to calculate the rate of the responsible processes.

J ¼ lðAP AtD Þ

½2

and the residence time of the daughter nuclide with respect to scavenging, tsc , is given by the quotient of


235


222Rn

238

U

Rivers: U,Ra

222Rn

228Th

228Ra 232Th 238U

210Pb

234Th

228Ra 234U

228Th

230Th

226Ra

226Ra

222Rn

222Rn

210Po

210Pb

238U

234Th

210Pb 234U

232Th

228Ra

235U

230Th

230Th

226Ra

222Rn

231Pa

210Pb

231Pa

227Ac

Figure 2 Schematic diagram of radioactive decay (horizontal arrows) and typical transport processes in ocean and atmosphere that can be traced by the nuclides described here (vertical arrows). (Adapted with permission from Ernst WG and Morin JG (eds.) 1980 The Environment of the Deep Sea. Englewood Cliffs, NJ: Prentice Hall.)

Table 3

Isotope pairs with mobile parents and particle-reactive daughters

Mother

Daughter

Half-life

Source

Oceanographic applications

234

230Th

75200 y

water column

238

234Th

24.1 d

water column

228

228Th

1.9 y

235

231

32500 y

water column, in deep sea and continental shelf water column

sediment trap calibration, reconstruction of past vertical rain, sediment focusing export production, calibration of shallow sediment trap resuspension budgets, bioturbation scavenging in coastal waters, bioturbation

226

210

210

210

22.3 y 138 d

water column, atmosphere water column

U U Ra U Ra Pb

Pa Pb Po

activity and removal rate: tsc ¼

AtD AtD ¼ J lðAP AtD Þ

½3

Elements in this group are described below. Thorium 230Th is produced at a known rate from U in sea water. The highly reactive element is rapidly adsorbed onto particles and transported 234

boundary scavenging, paleoproductivity, refined sediment trap calibration boundary scavenging, bioturbation scavenging in surface ocean

down in the water column when these particles sink out. As the adsorption is reversible, a steady-state distribution is achieved, in which both particulate and dissolved activities increase linearly with depth (see Uranium-Thorium Series Isotopes in Ocean Pro les). At any depth, disregarding horizontal advection, the vertical flux of 230ThXS (‘xs’ meaning in excess of the activity supported by the parent nuclide, in this case the small amount of 234U on the sinking particles) must equal its production from


236

URANIUM-THORIUM DECAY SERIES IN THE OCEANS: OVERVIEW 234

Parent nuclide Ap

PD =AP

Dissolved + particulate daughter nuclide ADt

ADt Sedimentation J Figure 3 Schematic diagram of the scavenging of a particlereactive daughter nuclide (decay constant l) produced in the water column from a soluble parent. 234

U in the overlying water column (depth z in meters), which amounts to: P230 ¼ l230 A234 z ¼ 9:19 106 ðy1 Þ 2820 ðdpm m3 ÞzðmÞ ¼ 0:0259 z ðdpm m2 y1 Þ

½4

This known, constant 230Th flux, depending only on water depth, is a powerful tool to quantify errors in the determination of rain rates of other components of the particle flux, either by sediment traps or through the accumulation rate of a marine sediment. The collection ef ciency of sediment traps, known to be highly variable and dependent on trap design, turbulence, and flow rates, can be derived from a comparison of the intercepted 230Th flux F230 with the theoretical flux P230 (see below for a re nement of this procedure using 231 Pa). The vertical rain rate Ri of any component i of the particle flux can be derived from the ratio of the concentration Ci to the 230Th activity in the particles A230, using: Ri ¼

Ci P230 A230

½5

In a similar way, the past flux of 230Thxs to the sea floor, 0F230, derived from decay-corrected 230Th activities (0A230) in dated sediment core sections, can be compared to the theoretical rain rate. The ratio C ¼ 0F230/P230, the focusing factor, is used to determine to what extent the sediment core location has been subject to focusing or winnowing during certain geological periods. The preserved vertical rain rate of sediment components corrected for such redistribution effects follows in analogy to eqn [5]: Ri ¼ 0

Ci P230 A230

Th is produced from the decay of 238U in sea water. In the deep ocean, approximately 3% of its activity is on particles and removal is so slow compared with its half-life (24.1 days) that total (dissolved þ particulate) 234Th is in secular equilibrium with 238U. In coastal and productive surface waters, however, scavenging (Figure 3) causes a strong depletion of 234Th (Figure 4). Following eqn [2], the depth-integrated depletion in the surface water yields the export flux of 234Th. If required, the calculation can be re ned to include advection and nonsteady-state situations. The resulting flux of 234 Th out of the surface layer of the ocean is the most suitable way to calibrate shallow sediment traps. The export flux of other constituents, like organic carbon or biogenic silica, can be derived from the export flux of 234Th if the ratio of these constituents to particulate 234Th in the vertical flux is known. This ratio is variable and depends, for example, on particle size, and the uncertainty in the determination of this ratio limits the quality of 234 Th-based estimates of export production from the upper ocean. A very similar situation exists near the seafloor, where resuspended sediment particles scavenge 234Th from the bottom water. The resulting depletion of 234 Th in the benthic nepheloid layer (BNL) is a measure of the intensity of the resuspension-sedimentation cycle on a timescale of weeks. The tracer thus shows whether a nepheloid layer is advected over large distances or sustained by local resuspension. Mass balance requires that the activity removed from surface waters and from the BNL is balanced by excess activities below (i.e. activities in excess of the activities supported by 238U). Excess activities have sometimes been observed in mineralization horizons in the water column below the euphotic zone and are common in the surface sediment. The distribution of excess 234Th in the sediment is used to calculate bioturbation rates on short timescales. The half-life of 1.9 years makes 228Th useful as a tracer for particle flux on a seasonal or interannual timescale. However, due to the highly inhomogeneous distribution of its parent 228Ra, the interpretation is much more complicated than in the case of 234Th, for example. As regards multiple Th isotopes as an in situ coagulometer, it has been shown that Th isotopes in the ocean are in reversible exchange between the particulate and dissolved form (Figure 5) and in steadystate, including radioactive decay we have:

½6


Apart A

diss

¼

k1 lþk1

½7


234Th/238U

0.5

0

237

ratio

1.0 0

0.5

1.0

Depth (m)

0

200

400

7 Nov

9 Dec

600 Figure 4 234Th : 238U ratio before (left) and during (right) a plankton bloom in the Bransfield Strait, Antarctic Peninsula. The left profile in each diagram represents dissolved, the right profile total 234Th activities. More 234Th was adsorbed to particles (shaded) in the bloom. Total 234Th was probably in equilibrium with 238U in November, but became depleted in the surface water in December (hatched) due to particle export. (Adapted from Scavenging and particle ux: seasonal and regional variations in the Southern Ocean (Atlandic sector). Marine Chemistry 35, Rutgers van der Loeff and Berger, 553–567, Copyright (1991) with permission from Elsevier.)

Adsorption Dissolved nuclide Adiss

k1

Particulate nuclide Apart

k _1 Desorption

Protactinium 231Pa is produced from the decay of U in sea water. The behavior of 231 Pa is very similar to that of 230Th, and these two uranium daughters are produced throughout the water column in a constant activity ratio, given by the production rate of 231Pa divided by the production rate of 230Th or A235 l231/A234 l230 ¼ 0:093. The major application of 231Pa lies in the combined use of these two tracers, whose exact production ratio is known. The approximately 10 times lower reactivity of 231Pa allows it to be transported laterally over larger distances than 230Th before being scavenged. The resulting basin-wide fractionation between 231Pa and 230Th is the basis for the use of the 231Pa/230Th ratio as a tracer of productivity. In areas of high particle flux the particles have a 231Pa/230Th ratio 40.093, whereas particles sinking in low productivity gyres have a ratio o0.093. The 231Pa/230Th ratio stored in the sediment, after proper correction for decay since deposition, is a powerful tool for the reconstruction of paleoproductivity. The fractionation between Th and Pa depends on particle composition and has been found to be much lower when opal is abundant. The tracer loses much of its value in a diatom-dominated system like the Southern Ocean. A related application of the 231Pa/230Th ratio is a correction to the 230Th-based calibration of sediment trap ef ciency. The removal of both nuclides from sea water can be divided into a vertically scavenged component (V230; V231) and a component 235

Figure 5 Box model of the reversible exchange between the dissolved and particulate form of a nuclide with adsorption and desorption rate constants k1 and k1.

where Apart and Adiss are the particulate and dissolved activities, k1 is the desorption rate constants. If the distribution of two or more isotopes (usually 234Th and 230Th or 228Th and 234Th) between dissolved and particulate forms is known, k1 and k1 can be calculated. Values for 1/k1 derived for thorium are on the order of a month in bloom situations and 41 year in clear deep water, much longer than expected from adsorption theory. This is explained when thorium adsorbs to colloidal-sized particules and the rate limiting steps, which determine the distribution of the tracers over dissolved and lterable form, are the coagulation and disaggregation with rate constants k2 and k2 respectively (Figure 6). Thus, when aggregation is clearly slower than adsorption (ðk2 5k1 Þ thorium isotopes provide a way to derive particle aggregation rates in situ.


238

URANIUM-THORIUM DECAY SERIES IN THE OCEANS: OVERVIEW Adsorption Parent nuclide

Aggregation

k1

Dissolved daughter nuclide

Small particles/ colloids daughter nuclide

k_1 Desorption

k2

Large particles daughter nuclide

k_2 Disaggregation

Sedimentation J Figure 6 Conceptual model, including the models depicted in Figure 3 and Figure 5, of the processes thought to control scavenging of radionuclides. (Adapted from Seasonality in the ux of natural radionuclides and plutonium in the deep Sargasso Sea. Deep-Sea Research 32, Bacon MP et al., 273–286, Copyright 1985 with permission from Elsevier Science.)

Lead 210Pb (half-life 22.3 years) is produced from Rn, the immediate daughter of 226Ra. 222Rn emanation from land is the major source of 210Pb deposition from the atmosphere (Figure 2). 222Rn emanation from surface sea water accounts for only 2% of 222Rn in the atmosphere, but is a signi cant source in remote areas like the Antarctic Ocean. Below the surface water, seawater 226Ra becomes the most important source. The high particle reactivity makes 210Pb a tracer for particle flux. This is shown most clearly by the good correlation between the fluxes of 210Pb and of biogenic material in sediment traps. Thus, low 210Pb activities (or 210Pb/226Ra ratios) in surface and deep water, high 210Pb fluxes in traps, and high inventories in the sediment all point to high particle fluxes and consequently high productivity. (Note, however, that in hemipelagic sediments in productive ocean areas the redox cycling of Mn can cause additional nearbottom scavenging of Pb.) Due to this removal on biogenic particles, 210Pb shows strong boundary scavenging similar to 231Pa, with accumulation rates in the sediments of productive (especially eastern) ocean boundaries that are far above local production and atmospheric deposition, whereas the flux to deep-sea sediments in oligotrophic central gyre regions can be very low. Consequently, the flux of 210 Pb into and its inventory in surface sediments is highly variable in space. But as long as the (yearly averaged) scavenging conditions do not change with time, the 210Pb flux to the sediment at a certain location can be considered constant, a prerequisite for the interpretation of 210Pb pro les to derive sedimentation and bioturbation rates. Due to the relatively well-known production and input rates of 210Pb, the scavenging residence time tsc 222

P230, P231 Production

H230, H231 Horizontal advection

V230, V231 Vertical flux Figure 7 Box model used to derive the vertical ux of

230

Th.

transported horizontally by eddy mixing or advection (H230; H231) (Figure 7). P230 ¼ V230 þH230

½8

P231 ¼ V231 þH231

½9

The calibration of sediment traps is based on the comparison of the intercepted 230Th flux F230 with the predicted vertical flux V230. In the original 230Thbased calibration procedure (eqn [4]), H230 is neglected and F230 is compared directly to the production rate P230. Since P230 (eqn [4]) and P231 are known and the V230/V231 ratio can be measured as the 230ThXS/231 PaXS ratio in the sediment trap material, it is suf cient to estimate the H230/H231 ratio from water column distributions to solve eqns [8] and [9] for V230 and obtain a re ned estimate of trapping ef ciency.



can be derived from the distribution of 210Pb in the ocean (compare eqn[3]). tsc was found to increase from about 2 years in the surface ocean to about 35 years in the deep Atlantic and 150 years in the deep Paci c, a result that is used to understand the behavior of stable lead. This illustrates how 210Pb is a useful analog for stable lead, the study of which is complicated by the extreme risk of contamination (see Anthropogenic Trace Elements in the Ocean). Polonium 210Po, the immediate daughter of 210Pb, is highly particle-reactive. The 138-day half-life of 210 Po makes the 210Po/210Pb tracer pair a suitable extension to 234Th as tracer for seasonal particle flux from the surface ocean. The non-homogeneous distribution and reactivity of the parent 210Pb implies that 210Po can only be used if concurrent accurate measurements are made of 210Pb. As a result of the strong af nity for organic material and cytoplasm, 210Po accumulates in the food chain and 210Po/210Pb activity ratios from around 3 in phytoplankton to around 12 in zooplankton have been reported. A high excess 210Po activity is therefore indicative of a pathway including zooplankton. The preference of Po for organic material in comparison with Pb and Th, which may adsorb on any surface, can be exploited to distinguish between the fluxes of organic carbon and other components of the particle flux. Reactive Parent with Mobile Daughter (Table 4)

This type of tracer is used to quantify diffusion, advection, and mixing rates of water masses, for example, the distribution of 222Rn near the seafloor. The parent, 226Ra, has a far higher activity in marine sediments (222Rn emanation rate As226 of order 100 dpm l1 wet sediment) than in the bottom water 1 (Aw 226 of order 0.2 dpm l ). This gradient causes a Table 4

239

diffusion of the daughter 222Rn from the sediment into the water column, and a typical vertical distribution as shown in Figure 8. The distribution of 222Rn, A222, can be described by the diffusion-reaction equation: dA222 d2 A222 ¼ lðA226 A222 Þ þ D dt dz2

½12

where D is the diffusion coef cient. This yields in steady state: pffiffiffiffiffiffiffiffiffi A222 ¼ A226 ðAo222 A226 Þe ðl=DÞ z ½13 A solution valid for the sediment and the water column (if z is de ned positive as the distance to the interface), where Ao222 signi es the 222Rn activity at the interface (Figure 8). In the sediment, this corresponds to an integrated depletion of: rffiffiffiffi D s 0 ½14 Is ¼ ðA226 A222 Þ l maintained by a

222

Rn release rate of: Fs ¼ lIs

½15

In the water column, this flux causes an excess activity which is transported upwards by turbulent diffusion (coef cient K). The integrated 222Rn excess in the bottom water is given by: rffiffiffiffi K 0 w ½16 Iw ¼ ðA222 A226 Þ l maintained by a supply from the sediment Fw ¼ lIw

½17

Note that mass balance requires that Fs ¼ Fw and that the depletion in the sediment equals the excess in the water column (Is ¼ Iw). The example shows how the

Isotope pairs with a particle-reactive parent and a mobile daughter

Mother

Daughter

Half-life

Source

Oceanographic application

231

227

232

228

22 y 5.8 y

deep-sea sediments all terrigenous sediments

230

226

228

224

1600 y 3.6 d

227

223

11.4 d

226

222

3.8 d

deep-sea sediments 232 Th (sediment) 228 Ra (sediment þ water column) 235 U (sediment) 231 Pa (sediment þ water column) (deep-sea) sediments

ocean circulation, upwelling tracing of shelf water sources, mixing in deep-sea and surface water ocean circulation, ground-water inputs mixing in shelf waters and estuaries

Pa Th Th Th

Th

Ra

Ac Ra Ra Ra

Ra

Rn

mixing in shelf waters and estuaries

mixing in bottom water, air–sea gas exchange, ground-water inputs


240


Bottom water

Height

100 m

Rn-222 A226S Iw

A222O Is

Depth

0

A226W

_ 0.1 m

Sediment 0.2

100 _1

A222, A226 (dpm l ) Figure 8 Generalized distribution of 226Ra and 222Rn in surface sediments and bottom water (note change in horizontal and vertical scales). The cumulative 222Rn depletion in the sediment (Is, shaded) is balanced by the 222Rn excess in the bottom water (Iw, hatched). The vertical extent of the disequilibrium is 3 orders of magnitude larger in the water column than in the sediment, corresponding to the 6 orders of magnitude difference in diffusion coefficient (on the order of 10 cm2 s1 in the bottom water as opposed to 105 cm2 s1 in the sediment).

diffusion coef cient in the sediment and the vertical eddy diffusion coef cient in the bottom water can be derived from measurements of the vertical distribution of this tracer using eqn [13]. Elements in this group are described below. Actinium 227Ac is produced by the decay of 231Pa. Over 99% of 231Pa produced in the water column resides in the sediment, with highest speci c activities in slowly accumulating deep-sea sediments. As actinium is relatively mobile, it is released to the pore water and from there to the overlying water, very similar to the behavior of 226 Ra and 228Ra. This results in a strong signal from the deep seafloor on top of a background concentration, which is given by the distribution of 231 Pa in the ocean. The nuclide is therefore a potential tracer for vertical mixing and advection (e.g. upwelling) on a decennium timescale. Radium Radium is relatively mobile and the major source of the isotope 226Ra is the production from the 230Th in the upper layer of sediments. Just like 227Ac, this source is strongest over deep-sea

sediments with a slow accumulation rate. The intermediate reactivity of radium (Table 2) and its half-life (1600 years) in the order of the ocean mixing time (around 1000 years) explain its distribution as a ‘biointermediate’ element: 226Ra activities are low in surface waters but never become depleted. They increase with depth and with the age of water masses in the conveyor-belt circulation to reach highest values in the deep north Paci c around 340 dpm m3. Extensive attempts in the GEOSECS program to use the isotope as a tracer of ocean circulation and water mass age proved unsuccessful as a result of the diffuse nature of the source. Even a normalization with barium, an element that can to a certain extent be regarded as a stable analog of radium, could not suf ciently account for this variation. Ground waters sometimes have high 226Ra activities. The isotope can then be used to trace groundwater inputs to the coastal ocean. 228 Ra is also produced in marine sediments, but in contrast to 226Ra and 227Ac, its parent 232Th is present in the terrigenous fraction of all sediments irrespective of water depth. In combination with the relatively short half-life (5.8 years), this results in a distribution in the open ocean with enhanced concentrations near the seafloor of the deep ocean and near the continental slope, while the activities can accumulate to highest values over extensive continental shelf areas. The vertical distribution in the deep sea (Figure 9) resembles the exponential decay that would be expected in a one-dimensional (1-D) model with the source in the seafloor, vertical mixing, and radioactive decay (eqn [13]). This would allow the tracer to be used to derive the vertical mixing rate in the deep ocean. However, it has been shown that even in a large ocean basin like the north-east Atlantic, horizontal mixing is so strong that the vertical distribution is influenced by inputs from slope sediments, making the 1-D model inadequate. The inputs of shelf waters to the open ocean cause the high activities in the surface waters, illustrated by a typical pro le in Figure 9. This surface water signal has a strong gradient from the continental shelf to the inner ocean, which has been used to derive horizontal eddy diffusion coef cients in a way analogous to eqn [13]. As the distribution of 228Ra has been shown to vary with time, a steady-state distribution can usually not be assumed, and a repeated sampling is required. Moreover, the horizontal distribution is affected by advection and vertical diffusion, making the interpretation rather complicated. The combination of various radium isotopes (see below), can alleviate some of these problems.



_2

0

0

1.0

228Ra Activity (10 2.0

241

_

dpm kg 1) 3.0

4.0

1

Depth (km)

2

3

4

5

6 Figure 9 Typical 228Ra activity profiles in the water column in the North Atlantic, showing the enrichment near the sea oor and in the surface water. (Adapted with permission from Ivanovich M and Harmon RS (eds) 1992 Uranium-series Disequilibrium, 2nd edn).

In surface current systems away from the continents, 228Ra becomes a powerful tracer for waters that have been in contact with the continental shelf. The 228 Ra enrichment in surface waters in the equatorial Paci c point to shelf sources off New Guinea, from where the isotope is carried eastward in the North Equatorial Counter Current. In this plume, the vertical distribution of the isotope has been used to derive vertical mixing rates. A very high accumulation of 228Ra is observed in the transpolar drift in the central Arctic Ocean, a signal derived from the extensive Siberian shelves. Due to their short half lives, 224Ra (3.4 days) and 223 Ra (11.4 days) are interesting only in the immediate vicinity of their sources. In the open ocean they are close to secular equilibrium with their parents 228 Th and 227Ac, but in coastal waters these tracers

are being developed to study mixing rates. Their distribution is controlled here by sources in the estuary and on the shelf, mixing and decay. Horizontal mixing rates have been obtained from the distribution of 223Ra and 224Ra across the shelf using eqn [13]. As with 228Ra, this procedure is limited to cases where the mixing can be considered to be one-dimensional, but the steady-state requirement is more easily met at these short timescales. The 223Ra/224Ra activity ratio, which decays with a half-life of 5.4 days, yields the age of a water mass since its contact with the source, irrespective of the nature of the mixing process with offshore waters. Radon With its half-life of 3.8 days, the readily soluble gas 222Rn is in secular equilibrium with its parent 226Ra in the interior ocean. At the


242


Table 5 Summary of the processes that can be investigated using the natural uranium-thorium decay series Processes Particle fluxes Boundary scavenging (Paleo) productivity Export production Scavenging, trace metal behavior Sediment trap efficiencies Aggregation rates of particles and colloids Sediment redistribution in bottom water Resuspension near sea oor Water masses Shelf interaction/horizontal mixing rates Vertical mixing rates Upwelling Ground-water inputs Gas exchange Exchange with atmosphere

Tracers

231

Pa/230Th, 210Pb Pa/230Th, 210Pb 234 Th 234 Th, 230Th, 210Pb, 210 Po 234 Th, 230Th, 231 Pa Joint Th isotopes 231

230

Th

234

210

228

224

223

222

228

227

Th,

Ra,

Rn, Ac 226 Ra,

Pb

Ra, Ra,

Ra Ac

227

222

Rn

222

Rn

boundaries of the ocean, however, inputs from sediments and release to the atmosphere create concentration gradients carrying useful kinetic information. The distribution of excess 222Rn near the seafloor is used to quantify vertical diffusion (see above, Figure 8) and ground-water inputs; the depletion of 222Rn in surface waters has been used to quantify the air–sea gas exchange rate.

Summary The accurate clocks provided by the uranium-thorium decay series enable us to extract rate information from the measurement of radioactive disequilibria in the ocean. Among the wide spectrum of available tracers, a mother–daughter pair with appropriate reactivities and half-lives can be found for a multitude of processes related to particle transport, water mass transport and mixing, and gas exchange (Table 5).

Glossary Adiss dissolved activity AP parent activity Apart particulate activity A222 222Rn activity Ao222 222Rn activity at sediment–water interface

A226 226Ra activity 226 Ra activity in the bottom water Aw 226 s A226 radon emanation rate in sediment A230 230Th activity in the particles A234 activity of 234U 0 A230 decay-corrected 230Th activities A235 235U activity AtD total daughter activity Ci concentration of component i D diffusion coef cient Fs 222Rn release rate Fw 222Rn input rate F230 intercepted 230Th flux 0 F230 past flux of 230Thxs to the seafloor H230 horizontal flux of 230Th H231 horizontal flux of 231Pa Is 222Rn depletion in the sediment Iw 222Rn excess in the bottom water J sedimentaion rate K turbulent diffusion coef cient Kd particle-water partition coef cient l decay constant k1 adsorption rate constant k 1 desorption rate constant k2 coagulation rate constant k 2 disaggregation rate constant N number of nucleons PD production rate P230 production rate of 230Th 231 Paxs excess activity of 231Pa P231 production rate of 231Pa t time t1/2 half-life 230 Thxs excess activity of 230Th Ri rain rate of component i V230 vertical flux of 230Th V231 vertical flux of 231Pa z depth Z atomic number l230 decay constant of 230Th l231 decay constant of 231Pa tsc scavenging residence time C focusing factor

See also Air–Sea Gas Exchange. Anthropogenic Trace Elements in the Ocean. Dispersion and Diffusion in the Deep Ocean. Hydrothermal Vent Fluids, Chemistry of. Nepheloid Layers. Ocean Margin Sediments. Sediment Chronologies. Sedimentary Record, Reconstruction of Productivity from the. Tracers of Ocean Productivity. Uranium-Thorium Series Isotopes in Ocean Profiles.



Further Reading Bacon MP and Anderson RF (1982) Distribution of thorium isotopes between dissolved and particulate forms in the deep sea. Journal of Geophysical Research 87: 2045--2056. Broecker WS and Peng T-H (1982) Tracers in the Sea. Columbia University, New York: Lamont-Doherty Geological Observatory. Eldigio Press. Cochran JK (1992) The oceanic chemistry of the Uranium and Thorium-series nuclides. In: Ivanovich M and Harmon RS (eds.) Uranium-series Disequilibrium: Applications to Earth, Marine, and Environmental

243

Sciences, 2nd edn. pp. 334--395. Oxford: Clarendon Press. Firestone RB (1998) Table of Isotopes, 8th edn. In: Baglin CM (ed) and Chu SYF (CD-ROM ed) New York: Wiley. Grasshoff K, Kremling K, and Ehrhardt M (1999) Methods of Seawater Analysis, 3rd edn, pp. 365–397. Weinheim: Wiley-VCH. Santschi PH and Honeyman BD (1991) Radioisotopes as tracers for the interactions between trace elements, colloids and particles in natural waters. In: Vernet J-P (ed.) Trace Metals in the Environment 1. Heavy Metals in the Environment, pp. 229--246. Amsterdam: Elsevier..


URANIUM-THORIUM SERIES ISOTOPES IN OCEAN PROFILES S. Krishnaswami, Physical Research Laboratory, Ahmedabad, India Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3146–3156, & 2001, Elsevier Ltd.

Natural radioactivity in the environment originates from two sources. First, primordial radionuclides which were incorporated into the Earth at the time of its formation are still present in it because of their long half-lives. 238U, 235U, 232Th and their decay series (Figure 1), 40K, 87Rb and 187Re are examples of this category. Second, cosmic ray-produced

238

U Series

238

U

232

Th Series

232

Th

4.5 × 109 y 234

Th 24.1 d

228

Ra 5.75 y

Th

U 2.5 × 105 y

230Th

7.5 × 104 y 226

Ra 1600 y

222

Rn 3.83 d

U Series

U

Ra 3.66 d

208

Pb

7.04 × 108 y

231

Pa 3.28 × 104 y

227

Ac 21.8 y

1.91 y 224

Supply of U/Th Isotopes to the Sea

235

1.4 × 1010 y

228

234

235

227

Th 18.7 d

223

Ra 11.4 d

207

Pb

210

Pb 22.3 y

210

Po 138 d

206

Pb

Figure 1 238U, 232Th and 235U decay series: Only the isotopes of interest in water column process studies are shown.

244

isotopes which are generated continuously in the atmosphere and earth’s crust through interactions of cosmic rays with their constituents. 3H, 14C and 10Be are some of the isotopes belonging to this group. The distribution of all these isotopes in the oceans is governed by their supply, radioactive decay, water mixing and their biogeochemical reactivity (the tendency to participate in biological and chemical processes) in sea water. Water circulation plays a dominant role in the dispersion of isotopes which are biogeochemically ‘passive’ (e.g. 3H, Rn), whereas biological uptake and release, solute–particle interactions and chemical scavenging exert major control in the distribution of biogeochemically ‘active’ elements (e.g. C, Si, Th, Pb, Po). Systematic study of the isotopes of these two groups in the sea can yield important information on the physical and biogeochemical processes occurring in sea water.

These nuclides enter the oceans through three principal pathways. Fluvial Transport

This is the main supply route for 238U, 235U, 234U and 232Th to the sea. These isotopes are transported both in soluble and suspended phases. Their dissolved concentrations in rivers depend on water chemistry and their geochemical behavior. In rivers, uranium is quite soluble and is transported mainly as uranyl carbonate, UO2(CO3)4 3 , complex. The dissolved uranium concentration in rivers is generally in the range of 0.1–1.0 mg l1. During chemical weathering 235U is also released to rivers in the same 235 U/238U ratio as their natural abundance (1/137.8). This is unlike that of 234U, a progeny of 238U (Figure 1) which is released preferentially to solution due to a-recoil effects. As a result, the 234U/238U activity ratios of river waters are generally in excess of that in the host rock and the secular equilibrium value of 1.0 and often fall in the range of 1.1–1.5. The concentration of dissolved 232Th in rivers, B0.01 mg l1 is significantly lower than that of 238U, although their abundances in the upper continental crust are comparable. This is because 232Th (and other Th isotopes) is more resistant to weathering and is highly particle-reactive (the property to be


URANIUM-THORIUM SERIES ISOTOPES IN OCEAN PROFILES

associated with particles) in natural waters and hence is rapidly adsorbed from solution to particles. It is likely that even the reported dissolved 232Th concentrations are upper limits, as recent results, based on smaller volume samples and high sensitivity mass-spectrometric measurements seem to show that dissolved 232Th in rivers is associated with smaller particles (o0.45 mm size). Similar to 232Th, the bulk of 230Th and 210Pb is also associated with particles in rivers and hence is transported mainly in particulate form from continents. 226 Ra and 228Ra are two other members of the UTh series (Figure 1) for which dissolved concentration data are available for several rivers, these show that they are present at levels of B0.1 d.p.m. l1. The available data show that there are significant differences between the abundances of U, Ra isotopes and 232Th in the host rocks and in river waters. The various physicochemical processes occurring during the mobilization and transport of these nuclides contribute to these differences. Rivers also transport U/Th series nuclides in particulate phase to the sea. These nuclides exist in two forms in the particulate phase, one as a part of their lattice structure and the other as surface coating resulting from their adsorption from solution. Analysis of suspended particulate matter from rivers shows the existence of radioactive disequilibria among the members of the same radioactive decay chain. In general, particulate phases are characterized by 234U/238U, 226Ra/230Th activity ratios o1 and 230Th/234U and 210Pb/226Ra41, caused by preferential mobilization of U and Ra over Th and Pb isotopes. Soluble and suspended materials from rivers enter the open ocean through estuaries. The interactions of sea water with the riverine materials can modify the dissolved concentrations of many nuclides and hence their fluxes to the open sea. Studies of U/Th series isotopes in estuaries show that in many cases their distribution is governed by processes in addition to simple mixing of river and sea water. For example, in the case of U there is evidence for both its addition and removal during transit through estuaries. Similarly, many estuaries have 226Ra concentration higher than that expected from water mixing considerations resulting from its desorption from riverine particles and/or its diffusion from estuarine sediments. Estuaries also seem to act as a filter for riverine 232Th. The behavior of radionuclides in estuaries could be influenced by their association with colloids. Recent studies of uranium in Kalix River show that a significant part is bound to colloids which is removed in the estuaries through flocculation. Similarly, colloids

245

seem to have a significant control on the 230Th–232Th distribution in estuarine waters.

In situ Production

Radioactive decay of dissolved radionuclides in the water column is an important supply mechanism for several U/Th series nuclides. This is the dominant mode of supply for 234Th, 228Th, 230Th, 210Po, 210Pb, and 231Pa. The supply rates of these nuclides to sea water can be precisely determined by measuring the concentrations of their parents. This is unlike the case of nuclides supplied via rivers whose fluxes are relatively more difficult to ascertain because of large spatial and temporal variations in their riverine concentrations and their modifications in estuaries.

Supply at Air–Sea and Sediment–Water Interfaces

A few of the U/Th nuclides are supplied to the sea via atmospheric deposition and diffusion through sediment pore waters. Decay of 222Rn in the atmosphere to 210Pb and its subsequent removal by wet and dry deposition is an important source of dissolved 210Pb to the sea. As the bulk of the 222Rn in the atmosphere is of continental origin, the flux of 210Pb via this route depends on factors such as distance from land and aerosol residence times. 210Po is also deposited on the sea surface through this source, but its flux is o10% of that of 210Pb. Leaching of atmospheric dust by sea water can also contribute to nuclide fluxes near the air–sea interface, this mechanism has been suggested as a source for dissolved 232Th. Diffusion out of sediments forms a significant input for Ra isotopes, 227Ac and 222Rn into overlying water. All these nuclides are produced in sediments through a-decay (Figure 1). The recoil associated with their production enhances their mobility from sediments to pore waters from where they diffuse to overlying sea water. Their diffusive fluxes depend on the nature of sediments, their accumulation rates, and the parent concentrations in them. 234U is another isotope for which supply through diffusion from sediments may be important for its oceanic budget. In addition to diffusion out of sediments, 226Ra and 222Rn are also introduced into bottom waters through vent waters associated with hydrothermal circulation along the spreading ridges. The flux of 226 Ra from this source though is comparable to that from rivers; its contribution to the overall 226Ra budget of the oceans is small. This flux, however, can overwhelm 226Ra diffusing out of sediments along the ridges on a local scale.


246


Distribution in the Oceans Uranium 238

U and 235U are progenitors of a number of particle-reactive nuclides in sea water which find applications in the study of several water column and sedimentary processes. The study of uranium distribution in the sea is therefore essential to a better understanding of the radioactive disequilibrium between 238U–234Th, 234U–230Th, 238U–234U, and 235 U–231Pa in sea water. Uranium in sea water is almost entirely in solution as UO2(CO3)4 3 . Considerable data on its concentration and 234U/238U activity ratios are available in the literature, most of which are based on a-spectrometry. These results show that uranium concentration in salinity normalized open ocean sea water (35%) are the same within experimental uncertainties, 3.370.2 mg l1. Measurements with highly sensitive mass-spectrometric techniques also yield quite similar values, but with a much better precision (B0.2%) and narrower range, 3.162–3.282 ng g1 35% salinity water (Figure 2). The B3.8% spread even in the recent data is intriguing and is difficult to account for as uranium is expected to be uniformly distributed in the oceans because of its long residence time, B(2–4) 105 years. More controlled sampling and analysis of uranium in sea water are needed to address this issue better. The mass-spectrometric measurements of uranium have also provided data showing that the 238 U/235U atomic ratio in sea water is 137.17– 138.60, identical within errors to the natural abundance ratio of 137.88.

0

Depth (m)

2000

4000

140 150 δ234U

160

Figure 2 238U concentration (ng g1 35% salinity water) and d(234U) in the Pacific ( ) and the Atlantic (J) waters. Data from Chen et al. (1986).

dð234 UÞ ¼ ½ðRs =Re Þ 1 103

½1

where Rs and Re are 234U/238U atomic ratios in sample and at radioactive equilibrium respectively. The d(234U) in the major oceans (Figure 2) are same within analytical precision and average 14472. Coralline CaCO3 and ferromanganese deposits forming from sea water incorporate 234U/238U in the ratio of 1.144, the same as that in seawater. The decay of excess 234U in these deposits has been used as a chronometer to determine their ages and growth rates. Th Isotopes

Pacific Atlantic

6000 3.10 3.15 3.20 3.25 3.30 130 Uranium (ng g–1)

Studies of uranium distribution in anoxic marine basins (e.g., the Black Sea and the Saanich Inlet) have been a topic of interest as sediments of such basins are known to be depositories for authigenic uranium. These measurements show that even in these basins, where H2S is abundant, uranium exists predominantly in þ 6 state and its scavenging removal from the water column forms only a minor component of its depositional flux in sediments. The preferential mobilization of 234U during weathering and its supply by diffusion from deep-sea sediments causes its activity in sea water to be in excess of that of 238U. The 234U/238U activity ratio of sea water, determined by a-spectrometry, indicates that it is quite homogenous in open ocean waters with a mean value of 1.1470.02. Mass-spectrometric measurements have confirmed the above observations of 234U excess with a much better precision and have also led to the use of ‘d notation’ to describe 234U–238U radioactive disequilibrium.

Among the U/Th series nuclides, the Th isotopes (232Th, 230Th, 228Th, and 234Th), because of their property to attach themselves to particles, are the most extensively used nuclides to investigate particle cycling and deposition in the oceans, processes which have direct relevance to carbon export, solute-particle interactions and particle dynamics. 232Th, 230Th and 228Th are generally measured by a-spectrometry and 234Th by b or g counting. Highly sensitive massspectrometric techniques have now become available for precise measurements of 232Th and 230Th in sea water. Dissolved 232Th concentration in sea water centers around a few tens of picograms per liter. It is uncertain if the measured 232Th is truly dissolved or is associated with small particles/colloids. Some 232Th profiles show a surface maximum which has been attributed to its release from atmospheric dust.


URANIUM-THORIUM SERIES ISOTOPES IN OCEAN PROFILES 234

Th is continuously produced in sea water from the decay of 238U at a nearly uniform rate of B2.4 atoms l1 min1. It has been observed that 234Th activity in the surface B200 m is generally deficient relative to its parent 238U suggesting its removal by particles, the mechanism of how this is accomplished, however, is not well understood. This result has been attested by several studies (Figure 3). The residence time of Th in the upper layers of the ocean is determined based on 234Th–238U disequilibrium and the relation; t¼

R tl ð1 RÞ

½2

where R is the 234Th/238U activity ratio and tl is the radioactive mean life of 234Th (36.8 days). More complex models considering reversible Th exchange, particle remineralization, aggregation and breakup have also been used to treat the 234Th data which allow better understanding of processes regulating both particle and Th cycling. All these studies demonstrate that Th removal by particle scavenging is ubiquitous in surface water and occurs very rapidly, on timescales of a few days to a few months. Much of this variability in the residence time of Th appears to be dictated by particle concentration, short residence times are typical of coastal and biologically productive areas where particles are generally more abundant. These observations have prompted the use of the 234Th–238U pair as a survey tool to determine the export fluxes of carbon from the euphotic zone. The results, though encouraging, suggest the need for a more rigorous validation of the assumptions and parameters used. 0

238

11°N

228

Th activity in the sea exhibits significant lateral and depth variations with higher concentration in the surface and bottom waters and low values in the ocean interior (Figure 4). This pattern is governed by the distribution of its parent 228Ra, which determines its production (see section on Ra isotopes). Analogous to 234Th, the distribution of 228Th in the upper layers of the sea is also determined by particle scavenging which causes the 228Th/228Ra activity ratio to be o1, the disequilibrium being more pronounced near coasts where particles are more abundant. The residence time of Th in surface waters calculated from 234Th–228U and 228Th–228Ra pairs yields similar values. Profiles of 228Th activity in bottom waters show a decreasing trend with height above the sediment–water interface. In many of these profiles 228Th is in radioactive equilibrium with 228Ra and in a few others it is deficient. Some of these profile data have been used as a proxy for 228Ra to derive eddy diffusion rates in bottom waters. Systematic measurements of 230Th activity–depth profiles in soluble and suspended phases of sea water have become available only during the past two decades. 230Th is produced from 234U at a nearly uniform rate of B2.7 atoms l1 min1. The dissolved 230 Th activity in deep waters of the North Atlantic is B(5–10) 104 d.p.m. l1 and in the North Pacific it is B2 times higher. In comparison, the particle 230 Th concentrations are about an order of magnitude lower (Figure 5). These values are far less than would be expected if 230Th were in radioactive

0

U 2000

15°N

Depth (m)

Depth (m)

100

238

U

247

200

4000

300 6000 0

1

2 3 0 1 Activity (d.p.m. kg–1)

2

3

Figure 3 234Th –238U profiles from the Arabian Sea. Note the clear deficiency of 234Th in the upper layers relative to 238U. (Modified from Sarin et al., 1996.)

2

0 228Th

(d.p.m. (1000 kg)–1)

4

Figure 4 228Th distribution in the Pacific. The higher activity levels of 228Th in near-surface and near-bottom waters reflect that of its parent 228Ra. Data from Nozaki et al. (1981).


248


Dissolved

0

Particulate

2000 Depth (m)

Depth (m)

2000

4000

6000

4000

0

1

2 230Th

3 0.00 0.06 0.12 0.18

0.2

0

(d.p.m. (1000 kg)–1)

0.4

231Pa

Figure 5 Water-column distributions of dissolved and particulate 230Th. Dissolved 230Th data from the North Pacific (Nozaki et al. 1981) and particulate 230Th from the Indian Ocean (Krishnaswami et al. 1981). The steady increase in the 230Th activities in both the phases is evident.

equilibrium with 234U, B2.7 d.p.m. l1, reinforcing the intense particle-reactive nature of Th isotopes and the occurrence of particle scavenging throughout the seawater column. More importantly, these studies showed that both the soluble and particulate 230 Th activities increase steadily with depth (Figure 5), an observation which led to the hypothesis of reversible exchange of Th between soluble and suspended pools to explain its distribution. In this model the equations governing the distribution of Th in the two phases are: Suspended Th:

S

6000

¯ k1 C ¼ ðl þ k2 ÞC

½3

dC¯ þ k1 C ðl þ k2 ÞC¯ ¼ 0 dz

½4

P þ k2 C¯ ¼ ðl þ k1 ÞC

½5

Soluble Th:

¯ are where P is the production rate of 230Th, C and C the 230Th concentrations in soluble and suspended phases, k1 and k2 are the first order adsorption and desorption rate constants, respectively, and S is the settling velocity of particles. Analysis of Th isotope data using this model suggests that adsorption of Th occurs on timescales of a year or so, whereas its release from particles to solution is much faster, i.e. a few months, and that the particles in sea are at

0.6

0.8

–1

(d.p.m. (1000 kg) )

Figure 6 231Pa distribution in the north-west Pacific. Data from Nozaki and Nakanishi (1985).

equilibrium with Th in solution. Modified versions of the above model include processes such as particle aggregation and breakup, remineralization and release of Th to solution. The timescales of some of these processes also have been derived from the Th isotope data. 231

Pa,

210

Po, and

210

Pb

These three isotopes share a property with Th, in that all of them are particle reactive. 231Pa is a member of the 235U series (Figure 1) and is produced in sea water at a rate of B0.11 atoms l1 min1. Analogous to 230Th, 231Pa is also removed from sea water by adsorption onto particles, causing its activity to be quite low and deficient relative to 235U (Figure 6). The 231Pa/235U activity ratio in deep waters of the western Pacific is B5 103. Measurements of 230Th/231Pa ratios in dissolved, suspended, and settling particles have led to a better understanding of the role of their scavenging by vertically settling particles in the open ocean in relation to their removal on continental margins. The dissolved 230Th/231Pa in sea water is B5, less than the production ratio of B10.8 and those in suspended and settling particles of B20, indicating that 230 Th is preferentially sequestered onto settling particles. This, coupled with the longer residence time of 231 Pa (a few hundred years) compared to 230Th (a few tens of years), has led to the suggestion that 231 Pa is laterally transported from open ocean areas to more intense scavenging regimes such as the continental margins, where it is removed. The



0

Depth (m)

chemical homologues. 210Pb occurs in excess over 226 Ra in surface water (Figure 9) resulting from its supply from the atmosphere. This excess, however, is less than that would be expected from the known

4 Arabian Sea Off Mexico Mediterranean 2 Bay of Bengal N. Atlantic

210

Po Removal rate constant (y–1)

Red Sea

Caribbean S. China Sea N. Pacific

0 0

0.2

0.4

0.6

Chlorophyll a (µg l–1) Figure 8 Interrelation between 210Po scavenging rate and chlorophyll a concentrations in various oceanic regions. (Modified from Nozaki et al., 1998.) 210

0

Pb excess (d.p.m. (100 kg)–1) 10 20 30

0

Depth (m)

measurements of settling fluxes of 230Th and 231Pa using sediment traps and 230Th/231Pa ratios in sediments from various oceanic regions support this connection. 210 Po is supplied to sea almost entirely through its in situ production from the decay of 210Pb (Figure 1), a minor contribution comes from its atmospheric deposition at the air–sea interface. 210Po is deficient relative to 210Pb in surface waters (210Po/210Pb B0.5, Figure 7), the deficiency being more pronounced in biologically productive regimes. The residence time of 210Po in surface waters of the world oceans is in the range of 170.5 years. The 210 Po/210Pb ratio at the base of the euphotic zone falls between 1.0 and 2.0 and often exceeds the secular equilibrium value of unity (Figure 7), below B200 m 210Po and 210Pb are in equilibrium. The 210 Po profiles in the upper thermocline have been modeled to obtain eddy diffusion coefficients and derive fluxes of nutrients into the euphotic zone from its base. The nature of 210Po profiles in the thermocline and the observation that it is enriched in phytoand zooplankton indicates that it is a ‘nutrient like’ element in its behavior and organic matter cycling significantly influences its distribution in the sea. The strong dependence of 210Po removal rate on chlorophyll a abundance in various oceans (Figure 8) is another proof for the coupling between 210Po and biological activity. In deep and bottom waters, 210Po and 210Pb are generally in equilibrium except in areas of hydrothermal activity where Fe/Mn oxides cause preferential removal of 210Po resulting in 210Po/210Pb activity ratio o1. The studies of 210Pb–226Ra systematics in the oceans have considerably enhanced our understanding of scavenging processes, particularly in the deep sea and the marine geochemistries of lead and its

249

1000

400

2000 800

6 9 12 0.4 Activity (d.p.m. (100 kg)–1)

1.0

1.6

210Po/210Pb

Figure 7 210Po–210Pb disequilibrium in the Indian Ocean. 210Po ( ) is deficient relative to 210Pb (J) near the surface and is in excess at 100–200 m. Data from Cochran et al. (1983).

Figure 9 210Pb excess over 226Ra in the upper thermocline from several stations of the Pacific. This excess results from its atmospheric deposition. (Modified from Nozaki et al., 1980.)



supply rate of 210Pb from the atmosphere if it is removed only through its radioactive decay. This led to the proposal that 210Pb is scavenged from surface to deep waters on timescales of a few years. In many profiles, excess 210Pb shows exponential decrease with depth (Figure 9), which has been modeled to derive apparent eddy diffusion coefficients. Measurements of 210Pb–226Ra in the deep sea produced a surprise result in that 210Pb was found to be deficient relative to 226Ra with 210Pb/226Ra of B0.5 (Figure 10). This was unexpected from the available estimates of the residence time of lead in the deep sea, i.e., a few thousands of years, orders of magnitude more than 210Pb mean-life. Numerous subsequent studies have confirmed this deficiency of 210Pb, though with significant variability in its extent and has led to the conclusion that 210Pb is rapidly and continuously removed from the deep sea on timescales of B50–200 years. The residence time is much shorter, B2–5 years, in anoxic basins such as the Cariaco Trench and the Black Sea. Two other important findings of these studies are that the extent of 210 Pb–226Ra disequilibrium increases from open ocean regimes to continental margins and topographic highs and that there is a significant concentration gradient in 210Pb activity from ocean interior to ocean margins. These results coupled with 210Pb data in suspended and settling particles form the basis for the proposal that 210Pb is removed from deep sea both by vertically settling particles and by lateral transport to margins and subsequent uptake

at the sediment–water interface. Processes contributing to enhanced uptake in continental margins are still being debated; adsorption on Fe/Mn oxides formed due to their redox cycling in sediments and the effect of higher particle fluxes, both biogenic and continental, have been suggested. It is the 210Pb studies which brought to light the role of continental margins in sequestering particle-reactive species from the sea, a sink which is now known to be important for other nuclides such as 231Pa and 10Be. 222

Rn

The decay of 226Ra in water generates the noble gas Rn; both these are in equilibrium in the water column, except near the air–sea and sea–sediment interfaces. 222Rn escapes from sea water to the atmosphere near the air–sea boundary, causing it to be deficient relative to 226Ra, whereas close to the sediment–water interface 222Rn is in excess over 226 Ra due to its diffusion out of bottom sediments (Figure 11). These disequilibria serve as tracers for mixing rate studies in these boundary layers. In addition, the surface water data have been used to derive 222Rn emanation rates and parameters pertaining to air–sea gas exchange. 222 Rn excess in bottom waters decreases with height above the interface, however, the 222Rn activity profiles show distinct variations. Commonly 222

5450

0

5450

Depth (m)

250

5650

5850

2000

0

1

2

Depth (m)

Depth (m)

Log excess radon

4000

5650

K = 440 ± 140 cm2 s−1 Excess radon

6000

0

20 40 0 20 Activity (d.p.m. (100 kg)–1)

40

5850 20

Figure 10 210Pb ( )–226Ra (J) disequilibrium in sea water. The deficiency of 210Pb in the ocean interior is attributed to its removal by vertically settling particles and at the ocean margins. Data from Craig et al. (1973), Chung and Craig (1980) and Nozaki et al. (1980).

36

24

48

60

Rn (d.p.m. (100 kg)−1)

222

Figure 11 Example of bottom water 222Rn profile in the Atlantic. The calculated vertical eddy diffusion coefficient is also given. (Modified from Sarmiento et al., 1976.)


251


the 222Rn activity decreases exponentially with height above bottom (Figure 11) which allows the determination of eddy diffusion coefficient in these waters. In these cases the 222Rn distribution is assumed to be governed by the equation: d2 C lC ¼ 0 dz2

40

60

80

100 223

Ra

1

½6

where K is the eddy diffusion coefficient and z height above bottom with 222Rn activity C. The values of K calculated from the 222Rn data span about two orders of magnitude, 1–100 cm2 s1. Other types of 222 Rn profiles include those with a two-layer structure and those without specific trend suggesting that its transport via advection and eddy diffusion along isopycnals and non-steady-state condition also need to be considered while describing its distribution. These studies also demonstrated a strong dependence between 222Rn-based eddy diffusion and the stability of bottom water column. Ra Isotopes

Ra isotopes, particularly, 226Ra and 228Ra have found extensive applications in water circulation studies. All the Ra isotopes, 224Ra, 223Ra, 228Ra, and 226 Ra enter the oceans mainly through diffusion from sediments and by desorption from river particulates and are commonly measured by a and g counting techniques. 224Ra and 223Ra, because of their very short half-lives (Figure 1), are useful for studying mixing processes occurring on timescales of a few days to a few weeks which restricts their utility to regions close to their point of injection such as coastal and estuarine waters (Figure 12). The halflife of 228Ra is also short, 5.7 years, and hence its concentration decreases with increasing distance from its source, the sediment–water interface, e.g., from coast to open sea (Figure 13) surface waters to ocean interior and height above the ocean floor (Figure 14). These distributions have been modeled, by treating them as a balance between eddy diffusion and radioactive decay (eqn [6]), to determine the rates of lateral and vertical mixing occurring on timescales of 1–30 years in the thermocline and near bottom waters. 226 Ra is the longest lived among the Ra isotopes, with a half-life comparable to that of deep ocean mixing times. The potential of 226Ra as a tracer to study large-scale ocean mixing was exploited using a one-dimensional vertical advection–diffusion model to describe its distribution in the water column. Subsequent studies brought to light the importance of biological uptake and cycling in influencing 226Ra

−1 In Activity (d.p.m. (100 l)−1)

K

20

−3

224

Ra

3

1

−1

−3 0

20

40

60

80

Distance offshore (km) Figure 12 Distributions of 223Ra and 224Ra activities as a function of distance off-shore from Winyah Bay off Carolina Coast, USA. These profiles have been modeled to yield horizontal eddy diffusion coefficients. (Modified from Moore, 1999.)

distribution, processes which were later included in the 226Ra model. Figure 15 shows typical profiles of 226Ra in the oceans. Its concentration in surface waters falls in the range of 0.0770.01 d.p.m. l1 which steadily increases with depth such that its abundance in the deep waters of the Pacific>Indian>Atlantic (Figure 15). 226Ra concentration in the North Pacific bottom water is B0.4 d.p.m. l1, some of the highest in the world’s oceans. 226 Ra distribution in the ocean has been modeled to derive eddy diffusivities and advection rates taking into consideration its input by diffusion from sediments, loss by radioactive decay, and dispersion


252


0

Ky = 4 ×105 cm2 s−1

0.5

Ky = 4 ×107 cm2 s−1

2000 Depth (m)

0.2

228

Ra (d.p.m. (100 kg)−1)

2.0

0.1 4000

0

400

800 Distance offshore (km)

1200

Figure 13 228Ra distribution as a function of distance from the coast off California. Values of horizontal eddy diffusion coefficient can be derived from these profiles. Note that 228Ra mixes farther into the open sea than 223Ra and 224Ra (Figure 12) because of its longer half-life. (Modified from Cochran, 1992.)

227

0

2.0 228

4.0 −1

Ra (d.p.m. (100 kg) )

Figure 14 Example of 228Ra depth profile in the North Atlantic. The high concentrations near the surface and near the sediment– water interface is due to its supply by diffusion from sediments. Lateral transport also plays an important role in determining surface water concentrations. (Modified from Cochran, 1992.)

0

2000 Depth (m)

through water mixing, particulate scavenging and regeneration. It has been shown that particulate scavenging and regeneration plays a crucial role in contributing to the progressive increase in 226Ra deep water concentration from the Atlantic to the Pacific. Attempts to learn more about particulate transport processes in influencing 226Ra distribution using Ba as its stable analogue and Ra–Ba and Ra–Si correlations have met with limited success and have clearly brought out the presence of more 226Ra in deep waters than expected from their Ba content (Figure 16). This ‘excess’ is the nascent 226Ra diffusing out of deep sea sediments and which is yet to take part in particulate scavenging and recycling. Such excesses are quite significant and are easily discernible in the bottom waters of the eastern Pacific.

6000

4000

Ac

The first measurement of 227Ac in sea water was only reported in the mid-1980s. These results showed that its concentration increases steadily from surface to bottom water (Figure 17) and that its activity in ocean interior and deep waters is considerably in excess of its parent 231Pa (Figure 17). The diffusion of 227Ac out of bottom sediments is the source of its

6000

0 226Ra

20 (d.p.m. (100 kg)−1)

40

Figure 15 Typical distributions of 226Ra in the water column of the Pacific ( ) and Atlantic (J) oceans. Data from Broecker et al. (1976) and Chung and Craig (1980).



excess in bottom waters, analogous to those of Ra isotopes. Measurements of 227Ac in pore waters have confirmed this hypothesis. 227Ac distribution can serve as an additional tracer in studies of water mixing processes occurring on decadal timescales, thus complementing the 228Ra applications.

226

Ra (d.p.m. (kg)−1)

0.4

0.3

0.2

253

Summary The distribution of U/Th series nuclides in the sea is regulated by physical and biogeochemical processes occurring in the water column and at the air–sea and sea–sediment interfaces. These processes often create radioactive disequilibria among the members of the U/Th decay chains. These disequilibria serve as powerful ‘tools’ to examine and quantify several processes in the sea, such as water circulation on various timescales (days to thousands of years), particle-scavenging, solute–particle interactions, particle dynamics and transformation and air–sea gas exchange. The understanding of these processes and elucidation of their timescales have direct relevance to studies such as dispersal of chemical species in the sea, contaminant transport and sites of their removal and particulate carbon fluxes through the water column. Recent advances in sampling and measurements of U/Th series nuclides have considerably enhanced the scope of their application in the study of water column processes.

See also

0.1

Estuarine Circulation. River Inputs. UraniumThorium Decay Series in the Oceans Overview. 0

40

80

120

160

Ba (nm kg−1)

Further Reading

Figure 16 Ra–Ba correlation in the north-east Pacific. The presence of ‘excess Ra’ (enclosed in ellipses) is clearly discernible in bottom waters. (Modified from Ku et al., 1980.)

0 231

Pa

Depth (m)

2000

4000

6000

0

1

2 3 Ac (d.p.m. (100 kg)−1)

4

227

Figure 17 227Ac profile in the Pacific Ocean. Its large excess over 231Pa is due to its diffusion out of sediments. (Modified from Nozaki, 1984.)

Anderson RF, Bacon MP, and Brewer PG (1983) Removal of Th-230 and Pa-231 from the open ocean. Earth and Planetary Science Letters 62: 7--23. Anderson PS, Wasserburg GJ, Chen JH, Papanastassiou DA, and Ingri J (1995) 238U–234U and 232Th–230Th in the Baltic sea and in river water. Earth and Planetary Science Letters 130: 218--234. Bacon MP and Anderson RF (1982) Distribution of thorium isotopes between dissolved and particulate forms in the deep sea. Journal of Geophysical Research 87: 2045--2056. Bhat SG, Krishnaswami S, Lal D, and Rama and Moore WS (1969) Thorium-234/Uranium-238 ratios in the ocean. Earth and Planetary Science Letters 5: 483--491. Broecker WS, Goddard J, and Sarmiento J (1976) The distribution of 226Ra in the Atlantic Ocean. Earth and Planetary Science Letters 32: 220--235. Broecker WS and Peng JH (1982) Tracers in the Sea. New York: Eldigio Press, Lamont-Doherty Geological Observatory. Chen JH, Edwards RL, and Wesserburg GJ (1986) 238U, 234 U and 232Th in sea water. Earth and Planetary Science Letters 80: 241--251. Chen JH, Edwards RL, and Wasserburg GJ (1992) Mass spectrometry and application to uranium series disequilibrium. In: Ivanovich M and Harmon RS (eds.)


254


Uranium Series Disequilibrium: Applications to Earth, Marine and Environmental Sciences, 2nd edn pp. 174– 206. Oxford: Clarenden Press. Chung Y and Craig H (1980) 226Ra in the Pacific Ocean. Earth and Planetary Science Letters 49: 267--292. Coale KH and Bruland KW (1985) Th-234 : U-238 disequilibria within the California Current. Limnology and Oceanography 30: 22--33. Cochran JK (1992) The oceanic chemistry of the uranium and thorium series nuclides. In: Ivanovich M and Harmon RS (eds.) Uranium Series Disequilibrium Applications to Earth, Marine and Environmental Sciences, 2nd edn, pp. 334–395. Oxford: Clarenden Press. Cochran JK, Bacon MP, Krishnaswami S, and Turekian KK (1983) 210Po and 210Pb distribution in the central and eastern Indian Ocean. Earth and Planetary Science Letters 65: 433--452. Craig H, Krishnaswami S, and Somayajulu BLK (1973) 210 Pb–226Ra radioactive disequilibrium in the deep sea. Earth and Planetary Science Letters 17: 295. Dunne JP, Murray JW, Young J, Balistrieri LS, and Bishop J (1997) 234Th and particle cycling in the central equatorial Pacific. Deep Sea Research II 44: 2049--2083. Krishnaswami S (1999) Thorium: element and geochemistry. In: Marshall CP and Fairbridge RW (eds.) Encyclopedia of Geochemistry, pp. 630--635. Dordrecht: Kluwer Academic. Krishnaswami S, Sarin MM, and Somayajulu BLK (1981) Chemical and radiochemical investigations of surface and deep particles of the Indian ocean. Earth and Planetary Science Letters 54: 81--96. Krishnaswami S and Turekian KK (1982) U-238, Ra-226 and Pb-210 in some vent waters of the Galapagos spreading center. Geophysical Research Letters 9: 827--830. Ku TL, Huh CA, and Chen PS (1980) Meridional distribution of 226Ra in the eastern Pacific along GEOSECS cruise track. Earth and Planetary Science Letters 49: 293--308. Ku TL, Knauss KG, and Mathieu GG (1977) Uranium in open ocean: concentration and isotopic composition. Deep Sea Research 24: 1005--1017.

Moore WS (1992) Radionuclides of the uranium and thorium decay series in the estuarine environment, In: Ivanovich M and Harmon RS (eds.) Uranium Series Disequilibrium. Applications to Earth, Marine and Environmental Sciences, 2nd edn. pp. 334--395. Oxford: Clarenden Press. Moore WS (1999) Application of 226Ra, 228Ra, 223Ra and 224 Ra in coastal waters to assessing coastal mixing rates and ground water discharge to the oceans. Proceedings of the Indian Academy of Sciences (Earth and Planetary Sciences) 107: 109--116. Nozaki Y (1984) Excess, Ac-227 in deep ocean water. Nature 310: 486--488. Nozaki Y, Dobashi F, Kato Y, and Yamamoto Y (1998) Distribution of Ra isotopes and the 210Pb and 210Po balance in surface sea waters of the mid-northern hemisphere. Deep Sea Research I 45: 1263--1284. Nozaki Y and Nakanishi T (1985) 231Pa and 230Th profiles in the open ocean water column. Deep Sea Research 32: 1209--1220. Nozaki Y, Turekian KK, and Von Damm K (1980) 210Pb in GEOSECS water profiles from the north Pacific. Earth and Planetary Sciences Letters 49: 393--400. Nozaki Y, Horibe Y, and Tsubota H (1981) The water column distributions of thorium isotopes in the western north Pacific. Earth and Planetary Sciences Letters 54: 203--216. Roy-Barman M, Chen JH, and Wasserburg GJ (1996) 230 Th–232Th systematics in the central Pacific Ocean: the sources and fate of thorium. Earth and Planetary Science Letters 139: 351--363. Sarin MM, Rengarajan R, and Ramaswamy V (1996) 234 Th scavenging and particle export fluxes from upper 100 m of the Arabian Sea. Current Science 71: 888--893. Sarmiento JL, Feely HW, Moore WS, Bainbridge AE, and Broecker WS (1976) The relationship between vertical eddy diffusion and buoyancy gradient in the deep sea. Earth and Planetary Letters 32: 357--370.


VEHICLES FOR DEEP SEA EXPLORATION S. E. Humphris, Woods Hole Oceanographic Institution, Woods Hole, MA, USA & 2009 Elsevier Ltd. All rights reserved.

Introduction Exploring the deep sea has captured the imagination of humankind ever since Leonardo da Vinci made drawings of a submarine more than 500 years ago, and Jules Verne published 20 000 Leagues under the Sea in 1875. Since the early twentieth century, people have been venturing into the ocean in bathyspheres and bathyscaphs. However, it was not until 1960 that the dream to go to the bottom of the deepest part of the ocean was realized, when Jacques Piccard and a US Navy lieutenant, Don Walsh, descended to the bottom of the Mariana Trench (10 915 m or 6.8 mil) in Trieste (Figure 1). This vehicle consisted of a float chamber filled with gasoline for buoyancy, and a separate pressure sphere for the personnel, allowing for a free dive rather than a tethered one. Containers filled with iron shot served as ballast to make the submersible sink. After a 5-h trip to the bottom, and barely 20 min of observations there, the iron shot was released and Trieste floated back to the surface. Since that courageous feat almost 50 years ago, dramatic advances in deep submergence vehicles and technologies have enabled scientists to routinely

explore the ocean depths. For many years, researchers have towed instruments near the seafloor to collect various kinds of data (e.g., acoustic, magnetic, and photographic) remotely. With the development of sophisticated acoustic and imaging systems designed to resolve a wide range of ocean floor features, towed vehicle systems have become increasingly complex. Some now use fiber-optic, rather than coaxial, cable as tethers and hence are able to transmit imagery as well as data in real time. Examples of deep-towed vehicle systems are included in Table 1 and Figure 2, and they tend to fall into two categories. Geophysical systems, such as SAR (IFREMER, France), TOBI (National Oceanography Centre, Southampton, UK), and Deep Tow 4KS (JAMSTEC, Japan), collect sonar imagery, bathymetry, sub-bottom profiles, and magnetics data, as they are towed tens to hundreds of meters off the bottom. Imaging systems, such as TowCam (WHOI, USA), Scampi (IFREMER, France), and Deep Tow 6KC (JAMSTEC, Japan), are towed a few meters off the bottom and provide both video and digital imagery of the seafloor. However, since the 1960s, scientists have been transported to the deep ocean and seafloor in submersibles, or human-occupied vehicles (HOVs), to make direct observations, collect samples, and deploy instruments. More recently, two other types of deep submergence vehicles – remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) – have been developed that promise to greatly expand our capabilities to map, measure, and sample in remote and inhospitable parts of the ocean, and to provide the continual presence necessary to study processes that change over time.

Human-occupied Vehicles

Figure 1 The bathyscaph Trieste hoisted out of the water in a tropical port, around 1959. Photo was released by the US Navy Electronics Laboratory, San Diego, California (US Naval Historical Center Photograph). Photo #NH 96801: US Navy Bathyscaphe Trieste (1958–63).

The deep-sea exploration vehicles most familiar to the general public are submersibles, or HOVs. This technology allows a human presence in much of the world’s oceans, with the deepest diving vehicles capable of reaching 99% of the seafloor. There exist about 10 submersibles available worldwide for scientific research and exploration that can dive to depths greater than 1000 m (Table 2 and Figure 3). All require a dedicated support ship. These battery-operated vehicles allow two to four individuals (pilot(s) and scientist(s)) to descend into the ocean to make observations and gather data and samples. The duration of a dive is limited by battery life, human


255

256

VEHICLES FOR DEEP SEA EXPLORATION

Table 1 Examples of deep-towed vehicle systems for deep-sea research and exploration (systems that can operate at depths Z1000 m) Vehicle

Operating organization

Maximum operating depth (m)

Purpose

TowCam

WHOI, USA

6500

Deep-Tow Survey System

COMRA, China

6000

DSL-120A IMI-30

HMRG, USA HMRG, USA

6000 6000

Scampi Syste`me Acoustique Remorque´ (SAR) SHRIMP TOBI BRIDGET Deep Tow 6KC Deep Tow 4KC Deep Tow 4KS

IFREMER, France IFREMER, France

6000 6000

NOC, UK NOC, UK NOC, UK JAMSTEC, Japan JAMSTEC, Japan JAMSTEC, Japan

6000 6000 6000 6000 4000 4000

Photo imagery; CTD; volcanic glass samples; water samples Sidescan, bathymetry; sub-bottom profiling Sidescan; bathymetry Sidescan; bathymetry; sub-bottom profiling Photo and video imagery Sidescan; sub-bottom profiling; magnetics; bathymetry Photo and video imagery Sidescan; bathymetry; magnetics Geochemistry Photo and video imagery Photo and video imagery Sidescan; sub-bottom profiling

WHOI, Woods Hole Oceanographic Institution, USA; COMRA, China Ocean Mineral Resources R&D Association; HMRG, Hawai’i Mapping Research Group, USA; IFREMER, French Research Institute for Exploration of the Sea; NOC, National Oceanographic Centre, Southampton, UK; JAMSTEC, Japan Marine Science & Technology Center. (a)

(b)

(c)

(d)

Figure 2 Examples of deep-towed vehicle systems. (a) SHRIMP, (b) Deep Tow, (c) Tow Cam, and (d) DSL-120A. (a) Courtesy of David Edge, National Oceanography Centre, UK. (b) & JAMSTEC, Japan, with permission. (c) Photo by Dan Fornari, WHOI, USA. (d) Courtesy of WHOI, USA.



Table 2

HOVs for deep-sea research and exploration (vehicles that can operate at depths Z1000 m)

Vehicle



HOV (under construction) Shinkai 6500 Replacement HOV (in planning stages) MIR I and II Nautile Alvin Pisces IV Pisces V Johnson-Sea-Link I and II

COMRA, China JAMSTEC, Japan NDSF, WHOI, USA P.P. Shirshov Institute of Oceanology, Russia IFREMER, France NDSF, WHOI, USA HURL, USA HURL, USA HBOI, USA

7000 6500 6500 6000 6000 4500 2170 2090 1000

257

Abbreviations as in Table 1; NDSF, National Deep Submergence Facility; HURL, Hawaii Undersea Research Laboratory; HBOI, Harbor Branch Oceanographic Institution, USA.

endurance, and safety protocols, and typically does not exceed 8–10 h, including transit time to and from the working depth (about 4 h for a seafloor depth of 4000 m). (The Russian MIR submersibles are an exception; they operate on a 100-kWh battery that can accommodate dive times in excess of 12 h.) Housed in a personnel sphere (Figure 4), the divers are maintained at atmospheric pressure despite the everincreasing external pressure with depth (1 atm every 10 m). Cameras on pan and tilt mounts with zoom and focus controls are located on the exterior of the vehicles, as well as quartz iodide and/or metal halide lights to illuminate the area. Submersibles are also equipped with robotic arms that can be used to manipulate equipment or pick up samples, and a basket, usually mounted on the front of the vehicle, to transport instruments, equipment, or samples. These vehicles can handle heavy payloads, maintaining neutral buoyancy as their weight changes through a variable ballast control system. All these capabilities, together with their slow speeds (1–2 knots), make submersibles best suited to detailed observations, imaging, and sampling in localized areas, rather than operating in a survey mode. Many significant discoveries during the past four decades of marine research have resulted from observations and samples taken from submersibles. Through direct observations from submersibles, biologists have discovered many previously unknown animals, and have documented that gelatinous animals (cnidarians, ctenophores, etc.) form a dominant ecological component of mid-water communities. These soft-bodied, fragile animals would have been destroyed by the trawl nets used in earlier days to sample these depths. Submersibles have enabled geologists to explore the global mid-ocean ridge system, and have provided them with a detailed view of the nature of volcanic and tectonic activity during the formation of oceanic crust. Submersibles played an

important role in the discovery of hydrothermal vents and their exotic communities of organisms, and continue to be used extensively for investigation of these extreme deep-sea environments. HOVs will continue to provide important capabilities for deep-sea research at least for the foreseeable future. Although rapid progress is being made in videography and photography to develop capabilities that match those of the human eye, there is still no substitute for the direct, three-dimensional view that allows divers to make contextual observations and integrate them with the cognitive ability of the human brain. In recognition of this continuing need, there are two submersibles that are under construction or in the planning stages. The China Ocean Mineral Resources R&D Association (COMRA) is constructing their first submersible that will have a maximum operating depth of 7000 m. It is expected to be operational in 2007. In the United States, over 40 years after the submersible Alvin was delivered in 1964, the National Deep Submergence Facility at Woods Hole Oceanographic Institution is in the planning stages for a new and improved replacement HOV with an increased operating depth of 6500 m.

Remotely Operated Vehicles Over the past 20 years, marine scientists have begun to routinely use ROVs to collect deep-sea data and samples. ROVs were originally developed for use in the ocean by the military for remote observations, but were adapted in the mid-1970s by the offshore energy industry to support deep-water operations. There are many ROVs commercially operated today, ranging from small, portable vehicles used for shallowwater inspections to heavy, work-class, deepwater ROVs used by the offshore oil and gas industry in support of subsea cable laying, retrieval, and repair.


258

VEHICLES FOR DEEP SEA EXPLORATION (a)

(b)

(c)

Figure 3 Examples of HOVs used to conduct scientific research. (a) Shinkai 6500, (b) Sea Link, and (c) Nautile. (a) & JAMSTEC, Japan, with permission. (b) Courtesy of Harbor Branch Oceanographic Institution, USA. (c) & IFREMER, France, with permission; O. Dugornay.


VEHICLES FOR DEEP SEA EXPLORATION Personnel hatch

Main ballast vent

Communication transducer

High pressure air spheres (2)

259

Thrusters (1 of 6)

Lifting T

Video light

Sonar

Video camera

Light bar Variable ballast spheres (4)

35-mm cameras Video cameras

Strobes Motor controller for relay pressure vessels

Starboard manipulator

Batteries

Variable ballast sphere Descent weights Pilot view port Port manipulator Ski

Sample basket

Figure 4 Cutaway illustration of the submersible Alvin showing the major components of an HOV. Illustration by E. Paul Oberlander, WHOI, USA.

There are about a dozen ROVs that are available to the international scientific community (Table 3 and Figure 5). While some of these have dedicated support ships, many can operate in the ‘flyaway’ mode; that is, they can be shipped to, and operated on, a number of different ships. Unlike the HOVs, ROVs are unoccupied, and are tethered to a support ship usually by a fiber-optic cable that has sufficient bandwidth to accommodate a wide variety of oceanographic sensors and imaging tools. The cable provides power and communications from the ship to the ROV, allowing control of the vehicle by a pilot on board the ship. The pilot can also use the manipulator arm(s) to collect samples and perform experiments. The cable transmits images and data from the ROV to the control room on board the ship where monitors display the images of the seafloor or water column in real time. These capabilities, together with their excellent power and lift, allow ROVs to perform many of the same operations as HOVs. Obvious advantages of using ROVs are that they remove the human risk factor from deep-sea research

and exploration and, through the shipboard control room (Figure 6), allow a number of scientists and engineers to discuss the incoming data and make collective decisions about the operations. Another distinct advantage is their ability to remain underwater for extended periods of time because power is provided continuously from the ship. This endurance means that scientists can make observations over periods of many days, instead of a few hours a day, and gives them the flexibility to react to unexpected events. The disadvantage of an ROV is that its tether constrains operations because the range of the vehicle with respect to the ship cannot exceed a few hundred meters. Movement of the ship must therefore be carefully coordinated with the movements of the vehicle – this requires a ship equipped with a dynamic positioning system. In addition, the tether is heavy and produces drag on the vehicle, making it less maneuverable and vulnerable to entanglement in rugged terrain. However, with careful tether management, ROVs are well suited to mapping and surveying small areas, as well as to making


260


Table 3

ROVs for deep-sea research and exploration (vehicles that can operate at depths Z1000 m)

Vehicle



Nereus (hybrid) (under construction) Kaiko 7000 Isis Jason II ATV CV (Wireline Reentry System) Victor 6000 ROV (on order) ROPOS Tiburon Quest Hercules Sea Dragon 3500 Hyper Dolphin Aglantha Ventana Cherokee

NDSF, WHOI, USA JAMSTEC, Japan NOC, UK NDSF, WHOI, USA SIO, USA SIO, USA IFREMER, France NOAA Office of Ocean Exploration, USA CSSF, Canada MBARI, USA Research Centre Ocean Margins, Germany Institute for Exploration, USA COMRA, China JAMSTEC, Japan Institute of Marine Research, Norway MBARI, USA Research Centre Ocean Margins, Germany

11 000 7000 6500 6500 6000 6000 6000 6000 5000 4000 4000 4000 3500 3000 2000 1500 1000

Abbreviations as in Tables 1 and 2; SIO, Scripps Institution of Oceanography; CSSF, Canadian Scientific Submersible Facility; MBARI, Monterey Bay Aquarium Research Institute; SIO, Scripps Institution of Oceanography.

more detailed observations, imaging, and sampling of specific features. While many of the ROVs available to the scientific community have a wide range of capabilities, a few are purpose-built. For example, the Wireline Reentry System known as CV, and operated by Scripps Institution of Oceanography, is a direct hang-down vehicle designed specifically for precision placement of heavy payloads on the seafloor or in drill holes (Figure 7). Unlike conventional, near-neutrally buoyant ROVs, the Wireline Reentry System can handle payloads of a few thousand kilograms, depending on the water depth. It has been used, for example, to install seismometer packages in, and recover instruments packages from, seafloor drill holes in water depths up to 5500 m, as well as to deploy precision acoustic ranging units on the axis of the mid-ocean ridge. Another ROV being built at Woods Hole Oceanographic Institution for a specific purpose is Nereus (Figure 8). More correctly referred to as a hybrid remotely operated vehicle, or HROV, because it will be able to switch back and forth to operate as either an AUV or an ROV on the same cruise, Nereus will be capable of exploring the deepest parts of the world’s oceans, as well as bringing ROV capabilities to ice-covered oceans, such as the Arctic. The HROV will use a lightweight fiber-optic micro-cable, only 1/32 of an inch in diameter, allowing it to operate at great depth without the high-drag and expensive cables typically used with ROV systems. Once the HROV reaches the bottom, it will conduct its

mission while paying out as much as 20 km (about 11 mi) of micro-cable. Once the mission is complete, the HROV will detach from the micro-cable and guide itself to the sea surface for recovery, while the micro-cable is recovered for reuse. In 2008–09, almost 50 years after the dive of the Trieste, Nereus will dive to the bottom of the Mariana Trench.

Autonomous Underwater Vehicles Although the concept of AUVs has been around for more than a century, it is only in the last decade or two that AUVs have been applied to deep-sea research and exploration. AUV technology is in a phase of rapid growth and expanding diversity. There are now more than 50 companies or institutions around the world operating AUVs for a variety of purposes. For example, the offshore gas and oil industry uses them for geologic hazards surveys and pipeline inspections, the military uses them for locating mines in harbors among other applications, and AUVs have been used to search for cracks in the aqueducts that supply water to New York City. There are currently about a dozen AUVs being used specifically for deep-sea exploration (Table 4 and Figure 9), although the numbers continue to increase. These unoccupied, untethered vehicles are preprogrammed and deployed to drift, drive, or glide through the ocean without real-time intervention from human operators. All power is supplied by energy systems carried within the AUV. Data are



(a)

(b)

(c)

(d)

261

Figure 5 Examples of ROVs used for deep-sea research. (a) ROV Kaiko, (b) ROV Jason II, (c) ROV Tiburon, and (d) ROV Victor 6000. (a) & JAMSTEC, Japan, with permission. (b) Photo by Tom Bolmer, WHOI, USA. (c) Photo by Todd Walsh & 2006, MBARI, USA, with permission. (d) & IFREMER, France, with permission; M. Bonnefoy.

recorded and are then either transmitted via satellite when the AUV comes to the surface, or are downloaded when the vehicle is recovered. They are generally more portable than HOVs and ROVs and can be deployed off a wide variety of ships. By virtue of their relatively small size, limited capacity for scientific payloads, and autonomous nature, AUVs do not have the range of capabilities of HOVs and ROVs. They are, however, much better suited than HOVs and ROVs to surveying large areas of the ocean that would take years to cover by any other means. They can run missions of many hours or days on their battery power and, with their streamlined shape, can travel many kilometers collecting data of various types depending on which sensors they are carrying.

Hence, AUVs are frequently used to identify regions of interest for further exploration by HOVs and ROVs. Unlike HOVs and ROVs that are designed with the flexibility to carry different sensors and equipment for different purposes, AUV system design and attributes are driven by the specific research application. Some, such as the autonomous drifters and gliders (essentially drifters with wings and a buoyancy change mechanism that allow the vehicle to change heading, pitch, and roll, and to move horizontally while ascending and descending in the water column), are designed for research in the water column to better understand the circulation of the ocean and its influence on climate. While satellites provide


262


Figure 6 Portable control van for the ROV Jason II constructed from two shipping containers assembled on board the R/V Knorr. & Dive and Discover, WHOI, USA.

global coverage of conditions at the sea surface, AUVs are likely to be the only way to continuously access data from the ocean depths. Equipped with oceanographic sensors that measure temperature, salinity, current speed, and phytoplankton abundance, drifters and gliders profile the water column by sinking to a preprogrammed depth, and then rising to the surface where they transmit their data via satellite back to the scientist on shore. By deploying hundreds to thousands of these vehicles, scientists will achieve a long-term presence in the ocean, and will be able to make comprehensive studies of vast oceanic regions. Other, more sophisticated AUVs are also used to investigate water column characteristics, and ephemeral or localized phenomena, such as algal blooms. The first of the Dorado Class of AUVs, operated by Monterey Bay Aquarium Research Institute, was deployed in late 2001 to measure the inflow of water into the Arctic basin through the Fram Strait. Autosub, operated by the National Oceanography Centre, Southampton, UK, was deployed to measure flow over the sills in the Strait of Sicily. The REMUS (Remote Environmental Monitoring UnitS) class of AUVs is extremely versatile and they have been used on many types of missions. The standard configuration includes an up- and downlooking acoustic Doppler current profiler (ADCP), sidescan sonar, a conductivity–temperature (CT) profiler, and a light scattering sensor. However, many other instruments have been integrated into it for specific missions, including fluorometers, bioluminescence sensors, radiometers, acoustic modems, forward-looking sonar, altimeters, and acoustic Doppler velocimeters. REMUS can also carry a video plankton recorder, a plankton pump, video cameras,

Figure 7 The Wireline Reentry System, known as the CV, operated by Scripps Institution of Oceanography. This specialized ROV can precisely place heavy payloads on the seafloor and in drill holes. The control vehicle, which weighs about 500 kg in water, is deployed at the end of a 17.3-mm (0.68’’) electromechanical (coax) or electro-optico-mechanical (three copper conductors, three optical fibers) oceanographic cable. The vehicle consists of a steel frame equipped with two horizontal thrusters mounted orthogonal to each other to control lateral position. The vertical position is controlled by winch operation. Instrumentation includes a compass, pressure gauge, lights, video camera, sonar systems, and electronic interfaces to electrical releases and to a logging probe. Courtesy of Scripps Institution of Oceanography – Marine Physical Laboratory, USA.

electronic still cameras, and, most recently, a towed acoustic array. Still other AUVs are designed specifically for nearbottom work. They have proved particularly useful for near-bottom surveying and mapping, which can be accomplished autonomously while the support ship simultaneously conducts other, more traditional, operations. One of the earliest vehicles to provide this capability was the Autonomous Benthic Explorer (ABE) developed at Woods Hole Oceanographic Institution. ABE was designed to be extremely stable in pitch and roll and to be reasonably efficient in forward travel. All the buoyancy is built into the two



upper pods, while the majority of the weight (the batteries and the main pressure housing) is in the central lower section. The three-hull structure also allows the seven vertical and lateral thrusters to be

Figure 8 Schematic illustration of the HROV, Nereus, currently under construction at Woods Hole Oceanographic Institution, USA, in its autonomous mode (upper) and its ROV mode (lower). Illustration by E. Paul Oberlander, WHOI, USA.

Table 4

263

placed between the hulls where they are protected. ABE is most efficient traveling forward, but it can also move backward, up or down, left or right, and can hover and turn in place. Equipment that it usually carries includes temperature and salinity sensors, an optical backscatter sensor, a magnetometer to measure near-bottom magnetic fields, and an acoustic altimeter to make bathymetric measurements and for its automated bottom following. ABE can dive to depths of 5500 m for 16–34 h, and it uses acoustic transponder navigation to follow preprogrammed track lines automatically. Its capability to maintain a precise course over rugged seafloor terrain gives it the ability to make high-precision seafloor bathymetric maps with features a few tens of centimeters tall and less than a meter long being identifiable. Other AUVs have been specifically developed for high-resolution optical and acoustic imaging of the seafloor. For example, SeaBED, also developed at Woods Hole Oceanographic Institution, was designed specifically to further the growing interests in seafloor optical imaging – specifically, high-resolution color imaging and the processes of photo-mosaicking and three-dimensional image reconstruction. In addition to requiring high-quality sensors, this imposes additional constraints on the ability of the AUV to carry out structured surveys, while closely following the seafloor. The distribution of the four thrusters, coupled with the passive stability inherent in a twohulled vehicle with a large metacentric height, allows SeaBED to survey close to the seafloor, even in very rugged terrain. In the future, AUVs will play an important role in the development of long-term seafloor observatories.

Examples of AUVs for deep-sea research and exploration (vehicles that can operate at depths Z1000 m)

Vehicle



Dorado Class CR-01, CR-02 Sentry REMUS Class Autosub 6000 Autonomous Benthic Explorer Explorer 5000 Jaguar/Puma Urashima (hybrid) Aster x Bluefin AUV Bluefin 21 AUV Odyssey Class SeaBED Autosub 3 Spray Gliders Seaglider

MBARI, USA COMRA, China WHOI, USA WHOI, USA NOC, UK NDSF, WHOI, USA Research Centre Ocean Margins, Germany WHOI, USA JAMSTEC, Japan IFREMER, France Alfred Wegener Institute, Germany SIO, USA MIT, USA WHOI, USA National Oceanography Centre, UK WHOI, USA Univ. of Washington, USA

6000 6000 6000 6000 6000 5500 5000 5000 3500 3000 3000 3000 3000 2000 1600 1500 1000

Abbreviations as in Tables 1–3.


264

VEHICLES FOR DEEP SEA EXPLORATION (a)

(b)

(c)

(d)

(e)

(f)

Figure 9 Examples of AUVs used in oceanographic research. (a) The Spray Glider, (b) Urashima, (c) Autosub, (d) SeaBED, (e) Dorado Class, and (f) ABE. (a) Photo by Jane Dunworth-Baker, WHOI, USA. (b) & JAMSTEC, Japan, with permission. (c) Courtesy of Gwyn Griffiths, National Oceanography Centre, Southampton, UK. (d) Photo by Tom Kleindinst, WHOI, USA. (e) Photo by Todd Walsh & 2004, MBARI, USA, with permission. (f) Photo by Dan Fornari, WHOI, USA.

Apart from providing the high-resolution maps needed to optimally place geological, chemical, and biological sensors as part of an observatory, AUVs will also operate in a rapid response mode. It is envisaged that deep-sea observatories will include

docking stations for AUVs, and there are a number of research groups currently working on developing this technology. When an event – most likely a seismic event – is detected, scientists on shore will be able to program the AUV, via satellite and a cable to



a surface buoy, to leave its dock and conduct surveys in the vicinity of the event. The AUV will then return to its dock and return the data to shore for assessment by scientists as to whether further investigation with ships is warranted.

Navigating Deep-sea Vehicles Unlike glider and drifter AUVs that can come to the sea surface and determine their positions using a Global Positioning System (GPS), deep-sea vehicles working at the bottom of the ocean have no such reference system because the GPS system’s radio frequency signals are blocked by seawater. The technique that has been the standard for threedimensional acoustic navigation of deep-sea vehicles is long-baseline (LBL) navigation – a technique developed more than 30 years ago. LBL operates on the principle that the distance between an underwater vehicle and a fixed acoustic transponder can be related precisely to the time of flight of an acoustic signal propagating between the vehicle and transponder. Two or more acoustic transponders are dropped over the side of the surface ship and anchored at locations selected to optimize the acoustic range and geometry of planned seafloor operations. Each transponder is a complete subsurface mooring comprised of an anchor, a tether, and a buoyant battery-powered acoustic transponder. The positions of the transponders on the seafloor are determined by using the GPS on board the ship and ranging to them acoustically while the ship circles the point where each transponder was dropped. The positions of the transponders on the seafloor can be determined this way with an accuracy of about 10 m. Transponders have accurate clocks to measure time very precisely, and they are synchronized with the clocks on the vehicle and on the ship. Each transponder is set to listen for acoustic signals (or pings) transmitted either from the deep-sea vehicle or the ship at a specific frequency. When each transponder hears these acoustic signals, it is programmed to transmit an acoustic signal back to the vehicle and the ship. Each transponder pings at a different frequency, so the ship and the vehicle can discern which transponder sent it. The time of flight of the acoustic signals gives a measure of distance to each transponder, and using simple triangulation, the unique point in three-dimensional space where all distances measured from all the transponders and the ship intersect can be calculated. More recently, conventional LBL navigation has been combined with Doppler navigation data, which measures apparent bottom velocity of the vehicle, for better short-term accuracy.

265

The Future The technological breakthroughs in deep-sea vehicle design over the last 40 years have resulted in unprecedented access to the deep ocean. While each type of vehicle has its own advantages and disadvantages, the complementary capacities of all types of deep-submergence vehicles provide synergies that are revolutionizing how scientists conduct research in the deep ocean. They are learning how to exploit those synergies by using a nested survey strategy that employs a combination of tools in sequence for investigations at increasingly finer scales: ship-based swath-mapping systems and towed vehicle systems for reconnaissance over large areas to identify features of interest, followed by more detailed, high-resolution mapping, imagery, and chemical sensing with AUVs, and finally, seafloor observations and experimentation using HOVs and ROVs. A demonstration of the power of such an approach occurred on a cruise to the Gala´pagos Rift in 2002. The investigative strategy was directed toward ensuring that all potential sites of hydrothermal venting in the rift valley were identified and investigated visually with the HOV Alvin. The AUV ABE was deployed at night to conduct highresolution mapping of the seafloor and collect conductivity–temperature–depth (CTD) data in the lower water column to detect sites of venting. Upon its recovery in the morning, micro-bathymetry maps and temperature anomaly maps were quickly generated, compiled with previous data, and then given to the scientists diving in Alvin that day for their use in directing the dive. Today, the vehicles are being deployed in various combinations to attack a range of multidisciplinary problems. Deep-sea vehicles will also play indispensable roles in establishing and servicing long-term seafloor observatories that will be critical for time-series investigations to understand the dynamic processes going on beneath the ocean. AUVs will undertake a variety of mapping and sampling missions while using fixed observatory installations to recharge batteries, offload data, and receive new instructions. They will be used to extend the spatial observational capability of seafloor observatories through surveying activities, and will document horizontal variability in seafloor and water column properties – necessary for establishing the context of point measurements made by fixed instrumentation. HOVs and ROVs will be required to install, service, and repair equipment and instrumentation on the seafloor and in drill holes, as well as collect samples as part of time-series measurements. The additional capabilities that these vehicles will need for service


266


and repair activities will likely build on ROV tools that are currently being used in the commercial undersea cable industry. Deep-sea vehicles will clearly have a role to play in deep-sea research for the foreseeable future, and they will be at the vanguard of a new era of ocean exploration.

See also Dispersion from Hydrothermal Vents. Gliders. Hydrothermal Vent Biota. Hydrothermal Vent Deposits. Hydrothermal Vent Ecology. Hydrothermal Vent Fauna, Physiology of. Hydrothermal Vent Fluids, Chemistry of. Platforms: Autonomous Underwater Vehicles.

National Research Council (2004) Future Needs of Deep Submergence Science. Washington, DC: National Academies Press. Reves-Sohn R (2004) Unique vehicles for a unique environment. Oceanus 42: 25--27. Rona P (2001) Deep-diving manned research submersibles. Marine Technology Society Journal 33: 13--25. Rudnick DL, Davis RE, Eriksen CC, Fratantoni DM, and Perry MJ (2004) Underwater gliders for ocean research. Marine Technology Society Journal 38: 48--59. Shank T, Fornari D, Yoerger D, et al. (2003) Deep submergence synergy: Alvin and ABE explore the Gala´pagos Rift at 86 1 W. EOS, Transactions of the American Geophysical Union 84(425): 432--433. Yoerger D, Bradley AM, Walden BB, Singh H, and Bachmayer R (1998) Surveying a subsea lava flow using the Autonomous Benthic Explorer (ABE). International Journal of Systems Science 29: 1031--1044.

Relevant Websites Further Reading Bachmayer R, Humphris S, Fornari D, et al. (1998) Oceanographic exploration of hydrothermal vent sites on the Mid-Atlantic Ridge at 371 N 321 W using remotely operated vehicles. Marine Technology Society Journal 32: 37--47. Davis RE, Eriksen CE, and Jones CP (2002) Autonomous buoyancy-driven underwater gliders. In: Griffiths G (ed.) The Technology and Applications of Autonomous Underwater Vehicles, pp. 37--58. London: Taylor and Francis. De Moustier C, Spiess FN, Jabson D, et al. (2000) Deep-sea borehole re-entry with fiber optic wireline technology. Proceedings of the 2000 International Symposium on Underwater Technology, Tokyo, 23–26 May 2000, pp. 379–384. Fornari D (2004) Realizing the dreams of da Vinci and Verne. Oceanus 42: 20--24. Fornari DJ, Humphris SE, and Perfit MR (1997) Deep submergence science takes a new approach. EOS, Transactions of the American Geophysical Union 78: 402--408. Fryer P, Fornari DJ, Perfit M, et al. (2002) Being there: The continuing need for human presence in the deep ocean for scientific research and discovery. EOS, Transactions of the American Geophysical Union 83(526): 532--533. Funnell C (2004) Jane’s Underwater Technology 2004– 2005, 800pp, 23rd edn. Alexandria, VA: Jane’s Information Group. National Research Council (2004) Exploration of the Seas: Voyage into the Unknown. Washington, DC: National Academies Press.

http://auvlab.mit.edu – AUV Lab Vehicles, AUV Lab at MIT Sea Grant. http://www.ropos.com – Canadian Scientific Submersible Facility. http://www.comra.org – China Ocean Mineral Resources R&D Association. http://divediscover.whoi.edu – Dive and Discover: Expeditions to the Seafloor. http://www.soest.hawaii.edu – Hawai’i Undersea Research Laboratory (HURL), School of Ocean and Earth Science and Technology. http://www.ifremer.fr – IFREMER Fleet. http://www.mbari.org – Marine Operations: Vessels and Vehicles, Monterey Bay Aquarium Research Institute. http://www.mpl.ucsd.edu – Marine Physical Laboratory, Scripps Institution of Oceanography. http://www.noc.soton.ac.uk – National Oceanography Centre, Southampton. http://www.jamstec.go.jp – Research Vessels, Facilities, and Equipment, JAMSTEC. http://www.apl.washington.edu – Seaglider, Applied Physics Laboratory, University of Washington. http://www.whoi.edu – Ships and Technology: National Deep Submergence Facility, Woods Hole Oceanographic Institution. http://www.rcom.marum.de – Technology page, MARUM.


VIRAL AND BACTERIAL CONTAMINATION OF BEACHES J. Bartram, World Health Organization, Geneva, Switzerland H. Salas, CEPIS/HEP/Pan American Health Organization, Lima, Peru A. Dufour, United States Environmental Protection Agency, OH, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3157–3166, & 2001, Elsevier Ltd.

Introduction Interest in the contamination of beaches by microbes is driven by concern for human health. The agents of concern are human pathogens, microorganisms capable of causing disease. Most are derived from human feces; therefore disposal of excreta and waterborne sewage are of particular importance in their control. Pathogens derived from animal feces may also be significant in some circumstances. The human population of concern constitutes primarily the recreational users, whether local residents, visitors, or tourists. Recreational use of natural waters (including coastal waters) is common worldwide and the associated tourism may be an important component of local and/or national economy. Scientific underpinning and insight into public health concern for fecal pollution of beaches developed rapidly from around 1980. Approaches to regulation and control (including monitoring) have yet to respond to the increased body of knowledge, although some insights into potential approaches are available. This article draws heavily on two recent substantial publications: the World Health Organization Guidelines for Safe Recreational Water Environment, released as a ‘draft for consultation’ in 1998, and Monitoring Bathing Waters by Bartram and Rees, published in 2000.

Public Health Basis for Concern Recreational waters typically contain a mixture of pathogenic (i.e., disease-causing) and nonpathogenic microbes derived from multiple sources. These sources include sewage effluents, non-sewage excreta disposals (such as septic tanks), the recreational user population (through defecation and/or shedding), industrial processes (including food processing, for

example), farming activities (especially feed lots and animal husbandry), and wildlife, in addition to the indigenous aquatic microflora. Exposure to pathogens in recreational waters may lead to adverse health effects if a suitable quantity (infectious dose) of a pathogen is ingested and colonizes (infects) a suitable site in the body and leads to disease. What constitutes an infectious dose varies with the agent (pathogen) concerned, the form in which it is encountered, the conditions (route) of exposure, and host susceptibility and immune status. For some viruses and protozoa, this may bevery few viable infectious particles (conceptually one). The infectious dose for bacteria varies widely from few particles (e.g., some Shigella spp., the cause of bacillary dysentery) to large numbers(e.g. 108 for Vibrio cholerae, the cause of cholera). In all cases it is important to recall that microorganisms rarely exist as homogeneous dispersions in water and are often aggregated on particles, where they may be partially protected from environmental stresses and as a result of which the probability of ingestion of an infectious dose is increased. Transmission of disease through recreational water use is biologically plausible and is supported by a generalized dose–response model and the overall body of evidence. For infectious disease acquired through recreational water use, most attention has been paidto diseases transmitted by the fecal–oral route, in which pathogens are excreted in feces, are ingested by mouth, and establish infection in the alimentary canal. Other routes of infectious disease transmission may also be significant as a result of exposure though recreational water use. Surface exposure can lead to ear infections and inhalation exposure may result in respiratory infections. Sewage-polluted waters typically contain a range of pathogens and both individuals and recreational user populations are rarely limited to exposure to a single encounter with a single pathogen. The effects of multiple and simultaneous or consecutive exposure to pathogens remain poorly understood. Water is not a natural ambient medium for the human body, and use of water (whether contaminated or not) for recreational purposes may compromise the body’s natural defenses. The most obvious example of this concerns the eye. Epidemiological studies support the logical inference that


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recreational water use involving repeated immersion will increase the likelihood of eye infection through compromising natural resistance mechanisms, regardless of the quality of the water. On the basis of a review of all identified and accessible publications concerning epidemiological studies on health outcomes associated with recreational water exposure, the WHO has recently concluded the following:

•

•

• •

The rate of occurrence of certain symptoms or symptom groups is significantly related to the count of fecal indicator bacteria. An increase in outcome rate with increasing indicator count is reported in most studies. Mainly gastrointestinal symptoms (including ‘highlycredible’ or ‘objective’ gastrointestinal symptoms) are associated with fecal indicator bacteria such as enterococci, fecal streptococci, thermotolerant coliforms and Escherichia coli. Overall relative risks for gastroenteric symptoms of exposure to relatively clean water lie between 1.0 and 2.5. Overall relative risks of swimming in relatively polluted water versus swimming in clean water vary between 0.4 and 3.

•

•

Many studies suggest continuously increasing risk models with thresholds for various indicator organisms and health outcomes. Most of thesuggested threshold values are low in comparison with the water qualities often encountered in coastal waters used for recreation. The indicator organisms that correlate best with health outcome are enterococci/fecal streptococci for marine and freshwater, and E.coli for freshwater. Other indicators showing correlation are fecal coliforms and staphylococci. The latter may correlate with density of bathers and were reported to be significantly associated with ear, skin, respiratory, and enteric diseases.

In assessing the adequacy of the overall body of evidence for the association of bathing water quality and gastrointestinal symptoms, WHO referred to Bradford Hill’s criteria forcausation in environmental studies (Table 1). Seven of the nine criteria were fulfilled. The criterion on specificity of association was considered inapplicable because the etiological agents were suspected to be numerous and relatively outcome-nonspecific. Results of experiments on the impact of preventive actions on health outcome frequency have not been reported.

Table 1 Criteria for causation in environmental studies (according to Bradford Hill, 1965). Application to bathing water quality and gastrointestinal symptoms



This degree of fulfillment suggests that the association is causal. Because of the study areas used, especially for the available randomized controlled trials, the results are primarily indicated for adult populations in temperate climates. Greater susceptibility among younger age groups has been shown and the overall roles of endemicity and immunity in relation to exposure and response are inadequately understood. The overall conclusions of the work of WHOconcerning fecal contamination of recreational waters and the different potential adverse health outcomes among user groups were as follows:

•

•

•

•

• •

The overall body of evidence suggests a casual relationship between increasing exposure to fecal contamination and frequency of gastroenteritis. Limited information concerning the dose–response relationships narrows the ability to apply cost–benefit approaches to control. Misclassification of exposure is likely to produce artificially lowthreshold values in observational studies. The one randomized trial indicated a higher threshold of 33 fecal streptococci per 100 ml for gastrointestinal symptoms. A cause–effect relationship between fecal pollution or bather-derived pollution and acute febrile respiratory illness is biologically plausible since associations have been reported and a significant exposure–response relationship with a threshold of 59 fecal streptococci per 100 ml was reported. Associations between ear infections and microbiological indicators of fecal pollution and bather load have been reported. A significant dose–response effect has been reported in one study. A cause–effect relationship between fecal or bather derived pollution and ear infection is biologically plausible. Increased rates of eye symptoms have been reported among bathers and evidence suggests that bathing, regardless of water quality, compromises the eye’s immune defenses. Despite biological plausibility, no credible evidence is available for increased rates of eye ailments associated with water pollution. No credible evidence is available for an association of skin disease with either water exposure or microbiological water quality. Most investigations have either not addressed severe health outcomes such as hepatitis, enteric fever, or poliomyelitis or have not been undertaken in areas of low or zero endemicity. By inference, transmission of enteric hepatitis viruses and of poliomyelitis – should exposure of

269

susceptible persons occur – is biologically plausible, and one study reported enteric fever (typhoid) causation. The WHO work of 1998 led to the derivation of draft guideline values as summarized in Table 2.

Sources and Control The principal sources of fecal pollution are sewage (and industrial) discharges, combined sewer overflows, urban runoff, and agriculture. These may lead to pollution remote from their source or point of discharge because of transport in rivers or through currents in coastal areas or lakes. The public health significance of any of these sources may be modified by a number of factors, some of which provide management opportunities for controlling human health risk. With regard to public health, most attention has, logically, been paid to sewage as the source of fecal pollution. Pollution abatement measures for sewage may be grouped into three disposal alternatives, although there is some variation within and overlap between these: treatment, dispersion through sea outfalls, and discharge not to surface water bodies(e.g., to agriculture or ground water injection). Where significant attention has been paid to sewage management, it has often been found that other sources of fecal contamination are also significant. Most important among these are combined sewer overflows (and ‘sanitary sewer’ overflows) and riverine discharges to coastal areas and lakes. Combined sewer overflows(CSOs) generally operate as a result of rainfall. Their effect is rapid and discharge may be directly to areas used for recreation. Riverine discharge may derive from agriculture, from upstream sewage discharges (treated or otherwise), and from upstream CSOs. The effect may be continuous (e.g., from upstream sewage treatment) or rainfallrelated (agricultural runoff, urban runoff, CSOs). Where it is rainfall-related, the effect on downstream recreational water use areas may persist for several days. In river systems the decrease in microbiological concentrations downstream of a source (conventionally termed ‘die-off’) largely reflects sedimentation. After settlement in riverbed sediments, survival times are significantly increased and re-suspension will occur when river flow increases. Because of this and the increased inputs from sources such as CSOs and urban and agricultural runoff during rainfall events, rivers may demonstrate a close correlation between flow and bacterial indicator concentration.


270


Table 2

Draft guideline values for microbiological quality of marine recreational waters (fecal streptococci per 100 ml)

The efficiency of removal of major groups of microorganisms of concern in various types of treatment processes is described in Table 3. Advanced sewage treatment (for instance based upon ultrafiltration or nanofiltration) can also be effective in removal of viruses and other pathogens. The role and efficiency of ultraviolet light, ozone, and other disinfectants are being critically re-evaluated. Treatment in oxidation ponds may remove significant numbers of pathogens, especially the larger protozoan cysts and helminth ova. However, short-circuiting due to poor design, thermal gradients, or hydraulic overload may reduce residence time from the typical design range of 30–90 days.

During detention in oxidation ponds, pathogens are removed or inactivated by sedimentation, sunlight, temperature, predation, and time. Disposal of sewage through properly designedlong-sea outfalls provides a high degree of protection for human health, minimizing the risk that bathers will come into contact with sewage. In addition, long-sea outfalls reduce demand on land area in comparison with treatment systems, but they may be considered to have unacceptable environmental impacts (for instance, nutrient discharge into areas wheredilution or flushing is limited). They tend to have high capital costs, although these are comparable to those of land-based treatment systems



Table 3

271

Pathogen removal during sewage treatment

depending on the degree of treatment, whereas recurrent costs are relatively much lower. Ludwig (1988) has presented a comparison of costs and ecological impacts of long-sea outfalls versus treatment levels. Diffuser length, depth, and orientation, as well as the area and spacing of ports are key design considerations. Pathogens are diluted and dispersed and suffer die-off in the marine environment. These are major considerationsin length of outfall and outfall locations. Pretreatment by screening removes large particulates and ‘floatables’. Grease and oil removal are also often undertaken. Re-use of wastewater and groundwater recharge are two methods of sewage disposal that have minimal impact upon recreational waters. Especially in arid areas, sewage can be a safe and important resource (of water and nutrients) used for agricultural purposes such as crop irrigation. Direct injection or infiltration of sewagefor ground water recharge generally presents very low risk for human healththrough recreational water use. Control of human health hazards associated with recreational use of the water environment may be achieved through control of the hazard itself (that is, pollution control) or through control of exposure. Fecal pollution of recreational waters may be subject to substantial variability whether temporally (e.g., time-limited changes in response to rainfall) or spatially (e.g., because, as aresult of the effects of discharge and currents, one part of a beach may behighly contaminated while another part is of good quality). This temporaland spatial variability

provides opportunities to reduce human exposure while pollution control is planned or implemented or in areas where pollution control cannot or will not be implemented for reasons such as cost. The measures used may include public education, control/ limitation of access, or posting of advisory notices; they are often relatively affordable and can be implemented relatively rapidly.

Monitoring, Assessment and Regulation Present regulatory schemes for the microbiological quality of recreational water are primarily or exclusively based upon percentage compliance with fecal indicator counts(Table 4). These regulations and standards have had some success in driving cleanup, increasing public awareness, and contributing to improved personal choice. Not withstanding these successes, a number of constraints are evident in established approaches to regulation and standardsetting:

• • •

Management actions are retrospective and can only be deployed after human exposure to the hazard. The risk to human health is primarily from human feces, the traditional indicators of which may also derive from other sources. There is poor interlaboratory and international comparability of microbiological analytical data.


272


Table 4

Microbiological quality of water guidelines/standards per100 mli



Table 5

273

Risk potential to human health through exposure tosewage

Treatment

Discharge type

Nonec Preliminary Primary (including septic tanks) Secondary Secondary plus disinfection Tertiary Tertiary plus disinfection Lagoons

Directly on beach

Short outfalla

Effective outfallb

Very high Very high Very high High Medium Medium Very Low High

High High High High Medium Medium Very Low High

NA Low Low Low Very Low Very Low Very Low Low

a

The relative risk is modified by population size. Relative risk is increased for discharges from large populations and decreased for discharges from small populations. b This assumes that the design capacity has not been exceeded and that climatic and oceanic extreme conditions are considered in the design objective (i.e., no sewage on the beach zone). c Includes combined sewer overflows. NA, not applicable. Reproduced with permission from Bartram and Rees (2000).

Table 6

Risk potential to human health through exposure to sewage through riverine flow and discharge

Dilution effecta,

b

High population with low river flow Low population with low river flow Medium population with medium river flow High population with high river flow Low population with high river flow

Treatment level None

Primary

Secondary

Secondary plus disinfection

Lagoon

Very high Very high High High High

Very high High Medium Medium Medium

High Medium Low Low Very low

Low Very Very Very Very

Medium Medium Low Low Very low

low low low low

a The population factor includes all the population upstream from the beach to be classified and assumes no instream reduction in hazard factor used to classify the beach. b Stream flow is the 10% flow during the period of active beach use. Stream flow assumes no dispersion plug flow conditions to the beach. Reproduced with permission from Bartram and Rees (2000).

•

While beaches are classified as ‘safe’ or ‘unsafe’, there is a gradient of increasing frequency and variety of adverse health effects with increasing fecal pollution and it is desirable to promote incremental improvements by prioritizing ‘worst failures’.

The present form of regulation also tends to focus attention upon sewage, treatment, and outfall management as the principal or only effective solutions. Owing to high costs of these measures, local authorities may be effectively disenfranchised and few options for effective local intervention in securing bather safety appear to be available. A modified approach to regulation of recreational water quality could provide for improved protection of public health, possibly with reduced monitoring effort and greater scope for interventions, especially

within the scope for local authority intervention. This was discussed in detail at an international meeting of experts in 1998 leading to the development of the ‘Annapolis Protocol’. Table 7 Risk potential to human health through exposure to sewage from bathers Bather shedding

Category

High bather density, high dilutiona Low bather density, high dilution High bather density, low dilutiona, b Low bather density, low dilutionb

Low Very low Medium Low

a Move to next higher category if no sanitary facilities are available at beach site. b If no water movement. Reproduced with permission from Bartram and Rees (2000).


274


Table 8

Possible sewage contamination indicators and their functions



The ‘Annapolis Protocol’requires field-testing and improvement based upon the experience gained before application. Its application leads to a classification scheme through which a beach may be assigned to a class related to health risk. By enabling local management to respond to sporadic or limited areas of pollution (and thereby to upgrade the classification of a beach), it provides significant incentive for local management action as well as for pollution abatement. The protocol recognizes that a large number of factors can influence the safety of a given beach. In order to better reflect risk to public health, the classification scheme takes account of three aspects: 1. Counts of fecal indicator bacteria in samples collected from the water adjoining the beach. 2. An inspection-based assessment of the susceptibility of the area to direct influence from human fecal contamination. 3. Assessment of the effectiveness of management interventions if they are deployed to reduce human exposure at times or in places of increased risk. The process of beach classification is undertaken in two phases: 1. Initial classification based upon the combination of inspection-based assessment and the results of microbiological monitoring. 2. Taking account of the management interventions. Inspection-based assessment takes account of the three most important sources of human fecal contamination for public health: sewage (including CSO and storm water discharges); riverine discharges where the river is receiving water from sewage

275

discharges and is used either directly for recreation or discharges near a coastal or lake area used for recreation; and bather-derived contamination. The result of assessment is an estimate of relative risk potential in bands as outlined in Tables 5, 6 and 7 Use of microbial and nonmicrobial indicators of fecal pollution requires an understanding of their characteristics and properties and their applicability for different purposes. Some very basic indicators such as sanitary plastics and grease in marine environments may be used for some purposes under some circumstances. Some newer indicators areunder extensive study, but conventional fecal indicator bacteria remain those of greatest importance. Indicators of fecal contamination and their principal uses are summarized in Table 8. By combining the results of microbiological testing with those of inspection, it is possible to derive a primary beach classification using a simple lookup table of the type outlined in Table 9. This primary classification may be modified to take account of management interventions that reduce or prevent exposure at times when or in areas where pollution is unusually high. Such ‘reclassification’ requires a database adequate to describe the times or locations of elevated contamination and demonstration that management action is effective. Since this ‘reclassification’ may have significant economic importance, independent audit and verification may be appropriate. Implementation of a monitoring and assessment scheme of the type envisaged in the Annapolis Protocol would belikely to have a significant impact upon the nature and cost of monitoring activities. In comparison with established practice, it would typically involve a greater emphasis on inspection and relatively less on sampling and analysis than


276


Table 9

Primary classification matrix

Sanitary inspection category (susceptibility to fecal influence)

Very low Low Moderate High Very high

Microbiological assessment category (indicator counts)

A

B

C

D

E

Excellent Excellent Gooda Gooda Faira

Excellent Good Good Faira Faira

Good Good Fair Fair Poora

Good ( þ ) Fair Fair Poor Very poor

Fair ( þ ) Fair ( þ ) Poor Very poor Very poor

a

Unexpected result requiring verification. ( þ ) implies non-sewage sources of fecal indicators (e.g., livestock) and this should be verified. Reproduced with permission from Bartram and Rees (2000).

is presently common place. At the level of an administrative area with a number of diverse beaches, it would imply an increased short-term monitoring effort when beginning monitoring, but a decreased overall workloadin the medium to longterm. Recreational use of the water environment provides benefits as well as potential dangers for human health andwell-being. It may also create economic benefits but can add tocompeting local demands upon a finite and sometimes already over-exploited local environment. Regulation, monitoring, and assessmentof areas of coastal recreational water use should be seen or undertaken not in isolation but within this broader context. Integrated approaches to management that take account of overlapping, competing, and sometimes incompatible uses ofthe coastal environment have been increasingly developed and applied in recent years. Extensive guidance concerning integrated coastal management is now available. However, recreational use of coastal areas is also significantly affected by river discharge and therefore upstream discharge and land use practice. While the need to integrate management around the water cycle is recognized, no substantial experience has yet accrued and tools for its implementation remain unavailable.

See also Pollution: Approaches to Pollution Control. Sandy Beaches, Biology of.

Further Reading Bartram J and Rees G (eds.) (2000) Monitoring Bathing Waters. London: EFN Spon. Bradford-Hill A (1965) The environment and disease: association or causation? Proceedings of the Royal Society of Medicine 58: 295--300. Esrey S, Feachem R, and Hughes J (1985) Interventions for the control of diarrhoeal diseases among young children: improving water supplies and excreta disposal facilities. Bulletin of the World Health Organization 63(4): 757--772. Ludwig RG (1988) Environmental Impact Assessment. Siting and Design of Submarine Outfalls. An EIA Guidance Document. MARC Report No. 43. Geneva: Monitoring and Assessment Research Centre/World Health Organization. Mara D and Cairncross S (1989) Guidelines for the Safe Use of Wastewater and Excreta in Agriculture and Aquaculture. Geneva: WHO. WHO (1998) Guidelines for Safe Recreational-water Environments: Coastal and Freshwaters. Draft for Consultation. Document EOS/DRAFT/98.14 Geneva: World Health Organization.


VOLCANIC HELIUM J. E. Lupton, Hatfield Marine Science Center, Newport, OR, USA Copyright & 2001 Elsevier Ltd. This article is reproduced from the 1st edition of Encyclopedia of Ocean Sciences, volume 6, pp 3166–3173, & 2001, Elsevier Ltd.

Introduction Volcanic activity along the global mid-ocean ridge system and at active seamounts introduces a heliumrich signal into the ocean basins that can be used to trace patterns of ocean circulation and mixing. Helium is extracted from oceanic volcanic rocks by circulating sea water and then injected into the ocean as helium dissolved in submarine hydrothermal vent fluids. Hydrothermal venting produces plumes in the ocean that are highly enriched in a variety of tracers, including heat, helium, manganese, iron, methane, and hydrogen. Among these, volcanic helium is a particularly useful tracer because it has such a high concentration in hydrothermal fluids relative to the background values ofhelium in sea water, and because it is stable and conservative, i.e., helium does not decay radioactively and is not affected by any chemical or biological processes. By making careful measurements of the relative abundance of heliumisotopes, it is possible to trace hydrothermal helium plumes for thousands of kilometers from the source regions. There are two stable isotopes of helium, 3He and 4 He, which vary in their ratio by over three orders of magnitude in terrestrial samples. The Earth’s atmosphere is well mixed with respect to helium and contains helium with a uniform isotopic composition of 3He/4He ¼ 1.39 106. Atmospheric helium is a convenient standard for helium isotope determinations, and terrestrial 3He/4He ratios are usually normalized to the air ratio and expressed in units of R=RA , where R ¼ 3He/4He and RA ¼ ð3 He=4 HeÞair . In contrast to atmospheric helium (R=RA ¼ 1), the radiogenic helium produced by a-decay of U and Th series isotopes has a much lower ratioof R=RA 0:1, while the volcanic helium that is derived from the Earth’s mantle is highly enriched in 3He (R=RA ¼ 5230). Thus volcanic helium has an isotopic composition distinct from other sources such as atmospheric helium or the helium produced by radioactive decay. This 3He-rich mantle helium is sometimes called ‘primordial’ helium, since it is thought to be the remnant of a primitive component

trapped in the Earth’s interior since the time of its formation. This trapped component probably had 3 He/4He ¼ 1 104or 100 RA , similar to the helium found trapped in meteorites or in the solar wind, but has been modified to R ¼ 30RA by dilution with radiogenic helium since the time the Earth was formed. Although there is a wide variety of volcanic sources in the oceans, including subduction zone volcanoes and hot spot volcanoes, most of the oceanic volcanic helium is derived from activity along the global mid-oceanridge system. While the 3 He/4He ratio of mantle helium shows a wide range of variation, the helium from mid-ocean ridgesfalls in a much narrower range of R=RA ¼ 729. In order of decreasing importance, the most abundant forms of helium in sea water are dissolved atmospheric helium, volcanic helium, and to a lesser degree radiogenic helium from sediments. Thereis also an input of pure 3He into the oceans from tritium(3H), the radioactive isotope of hydrogen, which decays to3He with a half-life of 12.4 years. Because tritium isgenerally found only in the upper ocean, 3 He from tritium decay(tritiogenic helium) is only significant at depths less than about 1000 m. Although there are only two isotopes of helium, it is still possible to clearly distinguish submarine volcanic helium from the other components because of its high 3He/4He ratio and because volcanic helium is introduced at mid-depth rather thanat the ocean surface or on the abyssal plain. Units

For samples highly enriched in helium such as volcanic rocks and hydrothermal vent fluids, the helium isotope ratio is usually expressed in the R=RA notation described above. However, for the relatively small variations observed in sea water samples, the 3 He/4He variations are usually expressed as dð3 HeÞ, which is the percentage deviation from the ratio in air, defined as in eqn [1]. dð3 HeÞ ¼ 100½ðR=RA Þ 1

½1

Here again R ¼3 He=4 He and RA ¼ ð3 He=4 HeÞair . Thus R=RA ¼ 1:50 is equivalent to dð3 HeÞ ¼ 50%.

History and Background The first attempt to detect nonatmospheric helium in the oceans was made by Suess and Wa¨nke in 1965, who predicted that the deep oceans should contain


277

278

VOLCANIC HELIUM

excess 4He due to U and Th decay in sediments and in the ocean crust. Although they were correct about the existence of radiogenic helium in the oceans, their measurements were of insufficient precision to detect any 4He enrichment above the dissolved air component. It is now known that the input of3Herich volcanic helium has a greater effect on both the3He/4He ratio and the 4He concentration insea water than does the input of radiogenic helium. Mantle or volcanic helium was first detectedon the Earth as an excess in the 3He/4He ratio indeep Pacific waters. Although this oceanic 3He excess is derived from the helium residing in oceanic volcanic rocks, it was not until about five years later that mantle helium was directly measured in the volcanic rocks themselves. Clarke et al. in 1969 reported a 21% excess in the 3He concentration at mid-depth above that expected forair-saturated water, and correctly attributed this excess to a flux ofprimordial helium leaking from the Earth’s interior into the oceans and inturn into the atmosphere (see Figure 1). Using a box model for oceanic helium, they were able to estimate the global 3He flux from the oceans into the atmosphere at 2 atoms 3He cm2, a number that isstill in reasonable agreement with more recent flux estimates of 4–5atoms 3He cm2. The discovery of excess 3He inthe oceans from localized sources distributed along the global midocean ridge system led immediately to the use of this tracer for oceanographic studies. The Geochemical Ocean Sections Study (GEOSECS), which began in 1972, provided the first maps of the global distribution of helium in the oceans. Since then, several

other oceanographic programs, including the World Ocean Circulation Experiment (WOCE), have added to our knowledge of the global helium distribution. To illustrate the presence of volcanic helium in the oceans, a typical helium profile in the north Pacific Ocean is shown in Figure 2. The figure shows thevertical variation in the 3He/4He ratio expressed as dð3 HeÞ in%, and the 4He concentration in nmol kg1. The values expected for air-saturated water (dashed lines) are shown for comparison. For the calculation of air-saturated values it is assumed that each water parcel equilibrated with the atmosphere at the potential temperature of the sample. This profile exhibits a broad maximum in the deep water, reaching avalue of dð3 HeÞ ¼ 25:0% at B1850 m depth. Although this station is located at a distance of over 1500 km from the nearest active spreading center, the profile still exhibits a clear excess in 3 He/4He in the 1500–3500 m depth range due to input of volcanic helium from the mid-ocean ridge system. The secondary maximum in the dð3 HeÞ profile at B350 m depth is due to excess 3He produced by tritium decay. That this peak is tritiogenichelium is evident because the peak in dð3 HeÞ at 350 m depth is absent from the 4He profile, indicating input of pure 3He as would be expected for tritium decay. At the ocean surface dð3 HeÞ ¼ 1:4%, which is very close to the expected value of dð3 HeÞ ¼ 1:35% for water in equilibriumwith air (3He is slightly less soluble in water than4He). The absolute 4He concentration (Figure 2B) also increases with depth, but not as dramatically as the 3 He/4He ratio. Part of the 4He increase is due to the

Helium escape

3

Interplanetary space

_6

4

He/ He = 10

Atmosphere

Oceans U, Th decay 3

4

_7

He/ He = 10

Ocean current Crust

3

4

_5

He/ He = 10

Mantle

Figure 1 A schematic of the terrestrial helium budget, indicating the flux of helium from the Earth’s mantle into the oceans, and in turn into the atmosphere.


VOLCANIC HELIUM 3

0

He (%) 10

4

20

30

1.6

279

_1

He (nmol kg ) 1.9 1.7 1.8

2.0

0

Depth (m)

_1000

_2000

_3000

_ 4000

_ 5000 (B)

(A)

Figure 2 A typical helium profile collected at 28.51N, 121.61W in the north Pacific Ocean. (A) The 3He/4Heratio expressed as dð3 HeÞ% plotted versus depth. The sharppeak at B350 m depth is due to tritium decay, while the broad maximum centered at B2000 m depth is due to volcanic helium introduced along themid-ocean ridge system. The dashed line represents the dð3 HeÞ for sea water in equilibrium with air. (B) The 4He concentration plotted versus depth for the same samples. The dashed line represents the 4He concentration expected for sea water in equilibrium with air.

higher solubility of heliumin the colder deep waters, as shown by the expected solubility values for airsaturated water (dashed line). However, much of the4He excess above solubility equilibrium is due to the finite amountof 4He present in the volcanic helium signal. At B2500 m depth, the profile has 4 He ¼ 1.92 nmol kg1, about 10%higher than the value of 1.75 nmol kg1 for air-saturated water at those conditions. The distinct isotopic signature of oceanic volcanic helium can be seen by plotting the 3He concentration versus the 4He concentration as shown in Figure 3. In this plot the slope of the trends corresponds to the isotopic ratio of the end-member helium that has been added to the water samples. The thin solid line corresponds to the atmospheric ratio (3He/4He ¼ 1.39 106 or R=RA ¼ 1), and addition of air would cause the values to migrate along this line. As expected, the range of equilibrium solubility values falls directly on the atmospheric line. Although the measured samples (filled circles) near the ocean surface also fall on this line, the deeper samples fall off the atmospheric trend, defining a much steeper slope. This steeper slope is direct evidence that the helium

that has been added tothe deep ocean has a higher 3 He/4He ratio than air.

Mid-ocean Ridge Helium The input of volcanic helium has affected the helium content of all the major ocean basins, although the magnitude of this effect varies greatly. To a large degree, the amount of the excess volcanic helium in each of the ocean basins is controlled by the relative strength of the hydrothermal input, which is in turn roughly proportional to the spreading rate of the ridges. In the Pacific Ocean, where the fastest ridge-crest spreading rates are found, the 3He/4He values at mid-depth average dð3 HeÞ ¼ 20% for the entire Pacific basin (Figure 4). The Indian Ocean, which has ridges spreading at intermediate rates, has dð3 HeÞ values averaging about 10–15%. Finally, the Atlantic Ocean, which is bisected by the slow-spreading Mid-Atlantic Ridge, has the lowest 3He enrichments, averaging dð3 HeÞ ¼ 025% (Figure 4).


280

VOLCANIC HELIUM

3.5

3.0

Range of equilibrium solubility

4228 m

_1

He (fmol kg )

It has been recognized for several decades that the distribution of mantle 3He has great potential for delineating the patterns of circulation and mixing of deep and intermediate water masses. This potential is probably greatest in the Pacific Ocean, becauseof the strong 3He signal in that ocean. The helium field atmid-depth in the Pacific has been mapped in considerable detail (Figure 5). This work has identified several distinct helium plumes emanating from active hydrothermal systems distributed along the midocean ridges. In the eastern equatorial Pacific, two jets of helium-rich water originate at latitude 101N and at 141S on the crest of the East Pacific Rise (EPR)and protrude westward into the interior of the basin. Between these two helium jets there is a minimum in the 3He signal on the Equator. This distinct pattern in the helium distribution requires westward transport atmid-depth in the core of these helium plumes, and suggests eastward transport on the Equator (see dashed arrows in Figure 5). A separate helium plume is present in the far northeast Pacific produced by input on the Juan de Fuca and Gorda Ridges(JdFR). Although this helium signal is weaker than the helium plumes from the EPR, the JdFR helium is still traceable as a distinct plume that trends south-west into the interior of the north Pacific basin. Farther south at B201N, a low-3He tongue penetrates from the west, implying eastward transport at this latitude. Thus the helium field defines a cyclonic (clockwise) circulation pattern at B2000 mdepth in the northeast Pacific.

1852 m

R =RA

2.5

3

13 m 2.0

1.5 1.5

1.6

1.7 4

1.9

1.8

_1

2.0

He (nmol kg )

Figure 3 The 3He concentration (in fmol kg1 or 1015 mol kg1) plotted versus 4He concentration (in nmol kg1or 109 mol kg1) for the samples shown in Figure 2. In this plot the slope of any trend corresponds to the isotopic ratio of the end-member helium that has been added to the water samples. The depths in meters of three representative samples are indicated. The thick solid line represents the range of equilibrium solubility values expected for air saturated water (Weiss, 1970; 1971). As expected, the equilibrium solubility values fall on the thin solid line, which is the mixing relation expected for air helium (R ¼ RA ). The steep slope of the dashed line, which isa best fit to the sea water samples, indicates that helium with an elevated 3He/4He ratio (R4RA ) has been added.

30

Slow Intermediate Fast

25

15

30 35

40

19

21

30°S

19

19

17 6

0°

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