Cirrus

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cirrus

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cirrus

Edited by DAVID K. LYNCH KENNETH SASSEN DAVID O'C. STARR GRAEME STEPHENS

OXPORD UNIVERSITY PRESS 2002

OXFORD UNIVERSITY PRESS Oxford New York Athens Auckland Bangkok Bogota Buenos Aires Cape Town Chennai Dar es Salaam Delhi Florence Hong Kong Istanbul Karachi Kolkata Kuala Lumpur Madrid Melbourne Mexico City Mumbai Nairobi Paris Sao Paulo Shanghai Singapore Taipei Tokyo Toronto Warsaw and associated companies in Berlin Ibadan

Copyright © 2002 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 Oxford is a registered trademark of Oxford University Press. All rights reserved. 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 permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Cirrus / edited by David K. Lynch ... [et al.]. p. cm. Includes bibliographical references and index. ISBN 0-19-513072-3 1. Cirrus clouds. I. Lynch, David K., 1946QC921.43.C57 C58 2000 551.57'6-dc21 00-021315

987654321 Printed in the United States of America on acid-free paper

Preface

Until the 1980s, cirrus clouds were viewed with curious disinterest by most atmospheric scientists. They were interesting to look at, sometimes foretelling the coming of storms, and occasionally producing pretty optical phenomena such as halos and sun dogs. But cirrus did not rain or snow and were therefore perceived as having no impact on commerce, agriculture, transportation, recreation, or the ability to wage war. Thus, cloud research largely ignored cirrus and focused on the denser clouds associated with weather systems, rain, snow, and damaging winds. With the growing recognition that global climate change was a subject worthy of scientific inquiry and potentially a public concern, scientific interest in cirrus clouds began to increase. This interest was founded in the early works of scientists such as Julius London, Fritz Moller, and Kiril Yakolevich Kondratyev in the late 1950s and Sukyo Manabe in the 1960s, who demonstrated the important radiative effect of cirrus clouds on the global heat budget and therefore in the climate system. In the late 1960s and 1970s, meteorologists began to acquire and analyze quantitative measurements of cirrus cloud radiative and microphysical properties. The efforts of these early cirrus scientists, along with the continuing growth in scientific and public concern with global climate change and the advent of improved observation technology, built the momentum for creation of significant research programs with a focus on cirrus cloud systems. The first of these was the FIRE program (First ISCCP Regional Experiment; ISCCP is the International Satellite Cloud Climatology Project), initiated in the early 1980s and led by NASA with the participation of other U.S. agencies such as the National Science Foundation and the National Oceanographic and

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Preface

Atmospheric Administration. Later, a comparable program was developed in Europe. A series of increasingly comprehensive field experiments was conducted in the United States beginning with FIRE Cirrus-I in 1986 and then FIRE CirrusII in 1991, a pilot tropical cirrus experiment as part of TOGA-COARE in 1993, and SUCCESS (Subsonic Aircraft: Contrail and Cloud Effects Special Study) in 1996. A similar, though somewhat smaller scale, series of experiments (EUCREX/ICE) were conducted in Europe during the mid-1990s under the leadership of Ehrhard Raschke. More recent activity there has focused on study of contrails under the leadership of Ulrich Schumann. The U.S. Department of Energy's Atmospheric Measurements Program (ARM) has recently made significant contributions, though the programmatic interest is much broader. Since 1994, intensive observation periods have been conducted on a roughly biennial basis at the Southern Great Plains ARM site in Oklahoma, during which extensive cirrus observations have been obtained. Extensive surface-based remote sensing cloud measurements are also obtained on a continuous basis. Interest in cirrus clouds has continued to grow, motivated by the need to understand cirrus impacts on the global radiation budget, climate and spacebased remote sensing systems, and now also by the possibility that contrails and associated anthropogenic effects, specifically effluent from aircraft, may be altering the regional and global occurrence and properties of cirrus. Recent concern that processes occurring on ice crystal surfaces may be significant in regulating upper tropospheric chemistry is an additional motivating factor. The next major planned cirrus experiment is an international multiagency tropical cirrus experiment, CRYSTAL, in the tropical western Pacific. Other smaller scale field work, airborne and surface based, is on-going. In 1997, we realized that there had never been an international scientific meeting devoted to cirrus clouds, and, worse yet, there was no single source of information about cirrus. The literature was scattered throughout journals and government reports. What was needed was a meeting where the world's cirrus experts could gather and produce a book that covered all aspects of cirrus clouds. The Optical Society of America (OSA) agreed to sponsor the meeting in cooperation with the American Meteorological Society (AMS) and the American Geophysical Union (AGU). The meeting was held in Baltimore on October 6-8, 1998 and was an intense, upbeat conference. There were more than 100 attendees, with 18 invited speakers and 30 contributed poster papers. Oxford University Press enthusiastically agreed to publish the invited papers from the meeting in book form. All of the invited speakers at the OSA meeting submitted manuscripts, and each was reviewed and revised. It was not enough, however, simply to collect the invited papers. They had to be edited so that the book was a more or less homogeneous monograph in which one chapter logically led to the next and in which there was little or no duplication of coverage. Notation had to be made uniform throughout the book, and figures had to be made to the same standard, at least to the extent possible when drawn from the technical literature. This book is a technical survey of cirrus clouds. It is intended to fill the large gap between elementary treatments of cirrus and advanced forefront research papers. It will be useful for cirrus researchers and scientists in any field who are

Preface

vii

interested in cirrus but who have not worked in the field before: students, meteorologists, atmospheric chemists, nucleation specialists, crystallographers, aerodynamicists, and so on. Being a review, most of the material has been previously published in one form or another. Thus it has passed the test of accuracy and reliability. But there are also many areas of uncertainty. We took particular care to highlight these areas so that people new to the field can gain some perspective on the important areas for future study. The editors are indebted to many people who took the time and effort to carefully review the manuscripts and make suggestions for improvements: Kenneth A. Campana, Allan I. Carswell, William Cotton, Anthony DelGenio, Andrew Detwiler, Stanley Gedzelman, Leo J. Donner, Ismael Gultepe, George Isaac, Eric Jensen, Richard Lindzen, David K. Lynch, Paul Menzel, Robert T. Menzies, Michael Mishchenko, Steven Ou, Martin Platt, Sergey Matrosov, William Rossow, Ken Sassen, Cynthia Twohy, Gabor Vali, and Donald Wiley. We also thank Joyce Berry at Oxford University Press, Rosemary Dwyer at the OSA, and Steve Moss of The Aerospace Corporation for help and encouragement with this project. We hope that readers will find CIRRUS to be a useful and enjoyable book. Los Angeles Salt Lake City Greenbelt Ft. Collins June 2001

D.K.L. K.S. D.S. G.S.


Contents

First Authors, xiii 1. Cirrus: History and Definition, 3 David K. Lynch 2. Cirrus: A Modern Perspective, 11 Kenneth Sassen 3. Ice Crystals in Cirrus, 41 John Hallett, William P. Arnott, Matthew P. Bailey, and Joan T. Hallett 4. Mid-latitude and Tropical Cirrus: Microphysical Properties, 78 Andrew J. Heymsfield and Greg M. McFarquhar 5. Laboratory Studies of Cirrus Cloud Processes, 102 Paul DeMott 6. Cirrus and Weather: A Satellite Perspective, 136 Donald P. Wylie 7. Satellite Remote Sensing of Cirrus, 147 Patrick Minnis

x

Contents

8. Ground-based Remote Sensing of Cirrus Clouds, 168 Kenneth Sassen and Gerald Mace 9. Molecular-Backscatter Lidar Profiling of the Volume-Scattering Coefficient in Cirrus, 197 Albert Ansmann 10. Structural and Optical Properties of Cirrus from LIRAD-type Observations, 211 C. Martin R. Platt 11. Contrail Cirrus, 231 Ulrich Schumann 12. Subvisual Cirrus, 256 David K. Lynch and Kenneth Sassen 13. Radiative Transfer in Cirrus Clouds: Light Scatting and Spectral Information, 265 K.N. Liou, Y. Takano, P. Yang, and Y. Gu 14. On Cirrus Modeling for General Circulation and Climate Models, 297 Hilding Sundqvist 15. GCM Simulations of Cirrus for Climate Studies, 310 Anthony D. Del Genio 16. Ice Clouds in Numerical Weather Prediction Models: Progress, Problems, and Prospects, 327 Christian Jakob 17. Dynamic Processes in Cirrus Clouds: A Review of Observational Results, 346 Markus Quante and David O'C. Starr 18. Dynamic Processes in Cirrus Clouds: Concepts and Models, 375 David O'C. Starr and Markus Quante 19. Microphysical Processes in Cirrus and Their Impact on Radiation: A Mesoscale Modeling Perspective, 397 Vitaly I. Khvorostyanov and Kenneth Sassen 20. Cirrus, Climate, and Global Change, 433 Graeme Stephens

Contents

21. Cirrus: The Future, 449 David K. Lynch, Kenneth Sassen, Anthony Del Genio, Andrew Heymsfield, Patrick Minnis, Martin Platt, Markus Quante, Ulrich Schumann, and Hilding Sundqvist Appendix: Chapter 2 Plates - Cirrus Case Studies, 457 Index, 469

xi


First Authors

David K. Lynch The Aerospace Corp. P.O. Box 92957 Los Angeles, CA 90009 [email protected]

Andrew J. Heymsfield NCAR P.O. Box 3000 Boulder, CO 80307 heyms 1 @ucar.edu

Kenneth Sassen Dept. of Meteorology 135 S 146OE 819 WBB University of Utah Salt Lake City, UT 84112 [email protected]

Kuo-Nan Liou Dept. of Atmospheric Sciences UCLA Los Angeles, CA [email protected]

David O'C. Starr Code 913 NASA Goddard Space Flight Center Greenbelt, MD 20771 [email protected]

Patrick Minnis NASALRC ASD/RSB Hampton, VA 23665-5225 [email protected]

Graeme Stephens Dept. of Atmospheric Science Colorado State University Fort Collins, CO 80523 [email protected]

C. Martin R. Platt 47 Koetong Parade Mt. Eliza, Victoria, 3930, Australia [email protected] xiii

xiv

Contributors

Donald Wylie CIMSS 1225 West Dayton Street University of Wisconsin—Madison Madison, WI 53706 [email protected]

Hilding Sundquist Dept. of Meteorology Univ. of Stockholm Arrheiniuslab, S-106 91 Stockholm, Sweden [email protected]

John Hallett Atmospheric Ice Physics Laboratory Desert Research Institute P.O. Box 60220 5625 Fox Ave. Reno, NV 89506-0220 [email protected]

Vitaly Khvorostyanov Bldg.8,Corpl,Apt.473rd Mikhalkovsky per. Moscow 125008, Russia [email protected]

Markus Quante GKSS Research Center Institute of Atmospheric Physics Max-Planck-Strasse D-21502 Geesthacht, Germany [email protected]

Christian Jakob ECMWF Shinfield Park Reading Berkshire RG2 9AX, UK [email protected]

Albert Ansmann Deutscher Wetterdienst Meteorologisches Observatorium Albin-Schwaiger-Weg 10 82383 Hohenpeibenberg, Germany albert. ansmann@tropos. de

Anthony Del Genio Goddard Institute for Space Studies 2880 Broadway New York, NY 10025 [email protected]

Paul J. DeMott Research Scientist Department of Atmospheric Science Colorado State University Fort Collins, CO 80523-1371 [email protected]

Ulrich Schumann DLR-Institut fur Physik der Atmosphere Oberpfaffenhofen D-82234 Wessling, Germany [email protected]

Acronyms

To avoid interrupting the text flow, frequently used acronyms are defined only here. ADEOS AERI AGU AMS ARES ARM ATSR AVHRR AVIRIS CAPE CART CAT CCN CCOPE CEPEX CERES COARE CPMC CRYSTAL CSIRO DMSP

Advanced Earth Observing Satellite Atmospheric Emittance Radiance Interferometer American Geophysical Union American Meteorological Society Airborne Remote Earth Sensing System Atmospheric Radiation Measurement Along Track Scanning Radiometer Advanced Very High Resolution Radiometer Airborne Visible Infrared Imaging Spectrometer convective available potential energy Clouds and Radiation Testbed clear air turbulence cloud condensation nuclei Cooperative Convection Precipitation Experiment Central Equatorial Pacific Experiment Clouds and the Earth's Radiant Energy System Coupled Ocean-Atmosphere Response Experiment Cirrus Parcel Model Comparison Project Cirrus Regional Study of Tropical Anvils and Layers (FIRE IV) Commonwealth Scientific and Industrial Research Organization (Australia) Defense Meteorological Satellite System xv

xvi

Acronyms

DOC DOE ECLIPS ECMWF EOS EOSP ERBE EUCREX PARS FDTD FIRE FTIR GARP GASP GATE GCM GCSS GEWEX GOES GPS HAT HIRS HIS HSRL HYVIS ICE ICMC IFO ISCCP ITCZ IWC JPL LANDSAT LIRAD LITE MISR MODIS MODTRAN MOZAIC NASA NCAR NCEP NOAA NWP NWS OLR PICASSOCENA OSA

Department of Commerce US Department of Energy Experimental Cloud Lidar Pilot Study European Centre for Medium-Range Weather Forecasts Earth Observation Satellites Earth Observing Scanning Polarimeter Earth Radiation Budget Experiment European Cloud and Radiation Experiment Facility for Atmospheric Remote Sensing finite-difference time domain First ISCCP Regional Experiment Fourier transform infrared spectroscopy Global Atlantic Research Program Global Atmospheric Sampling Program Global Atmospheric Tropical Experiment general circulation model Global Cloud System Study Global Energy and Water Cycle Experiment Geostationary Operational Environmental Satellite Global Positioning System high altitude tropical cirrus High Resolution Infrared Radiation Sounder High Spectral Resolution Infrared Spectrometer high spectral resolution lidar hydrometeor videosonde International Cirrus Experiment Idealized Cirrus Model Comparision Project Intensive Field Observations International Satellite Cloud Climatology Project Intertropical convergence zone ice water content Jet Propulsion Laboratory Land Satellite combined Lidar & RADar Lidar In-Space Technology Experiment Multi-angle Imaging Spectroradiometer Moderate Resolution Imaging Spectroradiometer MODerate resolution TRANsmittance code Measurement of Ozone by Airbus In-Service Aircraft National Aeronautics and space Administration National Center for Atmospheric Research National Center for Environmental Prediction National Oceanic and Atmospheric Administration numerical weather prediction National Weather Service outgoing longwave radiation Pathfinder Instruments for Cloud and Aerosol Spacebourne Observations—Climatologie Etendue des Nuages et des Aerosols Optical Society of America

Acronyms

POLDER POLINAT PROBE SAGE SHEBA SUCCESS TEFLUN TIROS TOA TOGA TOGA/ COARE TRMM VAS VIPS WMO

Polarization and Directionality of the Earth's Reflectances Pollution from Aircraft Emissions in the North Atlantic Flight Corridor Pilot Radiation Observation Experiment Stratospheric Aerosol and Gas Experiment Surface Heat Budget of the Arctic Ocean Subsonic Aircraft: Contrails and Cloud Effects Special Study Texas Florida Underflights Television and Infrared Observations Satellite Top-of Atmosphere Tropical Ocean Global Atmosphere Tropical Ocean/Global Atmosphere Coupled Ocean/Atmosphere Response Experiment Tropical Rain Measurement Mission Visible Atmospheric Sounder Video Ice Particle Sampler World Meteorological Organization

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cirrus


I Cirrus History and Definition

DAVID K. LYNCH

I.I. The Historical View of Cirrus The most distinguishing physical property of cirrus (cirrostratus and cirrocumulus) is their composition. Cirrus are made predominantly or wholly of ice, whereas the majority of clouds (both in name and number) are composed of water droplets. That most clouds were composed of water droplets was probably well known to the ancients, who must surely have encountered fog in valleys and mountains. Yet the presence of ice in cirrus is not easily experienced in everyday life. To answer the question Who discovered that cirrus are made of ice? we have to trace developments in meteorology back almost 2500 years. Anaxagoras of Clazomenae (c. 500-428 B.C.) might have deduced that cirrus were made of ice. Using an inductive approach based on measurements and observations, Anaxagoras knew that clouds were made of water and that air was colder aloft. He believed that warm, moist air convected upward and that the water vapor cooled, condensed, and ultimately froze at great heights to become hail. We do not know if Anaxagoras considered cirrus explicitly because what little is left of his writings do not mention any cloud recognizable as cirrus (Gershenson and Greenberg 1964). Two thousand years passed before any substantial progress was made on cirrus. In 1637 Descartes (1596-1650) published Discours de la methode (Descartes 1637) in three parts: Dioptrics, Meteorology, and Geometry. In Dioptrics he set forth the law of refraction (Snell's law) and in Meteorology he applied the law to the rainbows by performing numerical ray traces. Although he 3

4

Cirrus

almost certainly knew the principle of minimum deviation, there is nothing in his writings that explicitly refers to it. In the ninth discourse on Meteorology, Descartes conjectures that the common 22° halo was due to refraction through ice crystals. around the heavenly bodies there sometimes appear certain circles... they are round . . . and always surround the sun or some other heavenly body . . . they are colored, which shows that there is refraction. But the circles are never seen where it rains, which shows that they are not caused by the refraction which occurs in drops of water or in hail, but by that which is caused in those small little stars of transparent ice ... those that we have observed most often have had their diameters at around 45°... (Olscamp 1965) Descartes was obviously referring to the common 22° (radius) halo, whose diameter is about 45°. He recognized that the circles were visible on clear days and were not related in any way to rainbows. There is no evidence that Descartes actually ray traced an ice crystal. Still, Descartes almost surely recognized the existence of thin clouds when the halos were present, and he probably deserves the credit for identifying ice in cirrus. In 1681, Edme Mariotte (1620-1684) explained several halos as being due to refraction through crystals (Mariotte 1681).This confirmed Descartes' conjecture and later led Venturi (1794) and Young (1802) to set forth the modern basis of halo theory. Mariotte's framework of geometrical optics is still in place today (Pernter and Exner 1910, Tricker 1970, Greenler 1980, Tape 1994). Curiously, however, the notion of ice high in the atmosphere and the implication for temperature did not take root in the minds of seventeenth-century meteorologists. Around 1600, Fludd, Drebbel, Santorio, and Galileo were inventing what was to become the thermometer (Middleton 1966). Although the historical record does not tell us who was the first person to take a thermometer up in the air and record freezing temperatures, such an outcome would surely have been a reasonable expectation and possibly a known fact by the early seventeenth century. Thus, by the middle of the seventeenth century, all the supporting evidence was available: Descartes had optically linked cirrus with ice, even though cirrus had not yet been named or classified as a cloud type. Galileo and Santorio had established the quantitative relation between altitude and temperatures; what was necessary to complete the concept was empirical evidence that cirrus clouds were very high in the atmosphere. This had to wait almost two centuries before systematic cloud classifications began.

1.2. Modern Cloud Classification In 1802 Jean-Baptist Lamarck (1744-1829) published the first scientific cloud classification based on morphology. Though not intended as a complete system, one of his classes was nuages en balayures, or "sweep clouds," referring to what we now call "cirrus uncinus." Lamarck's French terminology was never adopted. The year after Lamarck's work, Luke Howard (1803) published a cloud classifi-

History and Definition

5

cation using Latin names. He was the first person to use the term "cirrus" to refer to wispy, fibrous clouds. Both Lamarck and Howard had biological backgrounds, and it is not surprising that "cirrus" was already used in taxonomy to describe various "dangling" or "prehensile" appendages. Howard's cloud classification is still in use. The next advance in understanding cirrus came more than 50 years after Howard's work. In 1855 Renou recognized the importance of cloud height, and researchers began triangulation measurements. Later, Hildebrandsson (1887) and Abercrombie (1887) firmly established height as an important classification parameter when they introduced 5 families of 10 cloud genera. Their work was immediately adopted as the standard for cloud classification. In 1879 Hildebrandsson published an atlas of 16 cloud photographs. A few years later he and Abercrombie published a more extensive cloud classification stressing cloud vertical height and structures and established "low," "middle," and "high" clouds as a useful classification overlay. Their work became the standard in cloud classification and was closely followed by Hildebrandsson, Riggenbach and Teisserenc de Bort (1896). Since this time, our understanding of the composition, height, and temperature of cirrus clouds was not changed significantly, nor, in general, has our definition of cirrus, except for the addition of several varieties. The period 1957-1964 was pivotal in cirrus research because satellites, lidars, and new forecasting tools became available. The first infrared cloud satellite, Television and Infrared Observations Satellite (TIROS-I), was launched April 1,1960 (Vaeth 1965). The first geosynchronous weather satellite was the Geostationary Operational Environmental Satellite (GOES), launched in May 1974 (Berlin 1988). Both TIROS and GOES had the ability to make visible and thermal infrared measurements, and thus the concept of a two-parameter classification was born. Clouds could be bright or dark in the visible and hot or cold in the infrared. Cirrus clouds were classified as "dark and cold," indicating both their relatively low optical thickness and low temperatures (i.e., thin and cold; Schiffer and Rossow 1983; Rossow and Gardner 1993). This classification, however, is not a definition, but rather a convenient way of classifying clouds based purely on their radiative properties. The fact that radiative properties correlate to some degree with ice phase, temperature, morphology, and so on makes sense but in no way is a perfect means to identify only cirrus clouds. Radiative properties may be considered a definition within the confines and restrictions of a particular satellite program, but they are in no way relevant to the morphological or physical definitions. Along with satellites came lasers (Maiman et al. 1960), and shortly thereafter, the first lidars were in operation (Ligda 1963; Fiocco and Grams 1964). Lidar provided height, density, polarization, and, eventually, velocity information. All added dramatically to our understanding of cirrus but did little to change the definition. Until the satellite age, cirrus prediction was limited to old weather sayings. In 1957 Stone reviewed all aspects of cirrus and began formulating prediction rules. A few years later Appleman (1961) reported the first cirrus climatology and was

6

Cirrus Table I . I . Cirrus cloud properties Property Thickness Altitude Concentration Ice content Size (length) Shape3

Mean 1.5km 9km 30/L 0.025 gm~3 250 urn variable

Range

Ratio3

0.1-8 km 4-20 km Kr*-10*/L IffM. 2 g/m3 1-8000 um highly variable

80:1

5:1 108:1 104:1 8000:1 large

Adapted from Dowling and Radke (1990). * Added by author.

able to identify the conditions under which cirrus formed. Their work laid the foundation for much of the modern prognostic schemes (Schmidt and Lynch 1995; chapter 2, this volume). The average properties of cirrus and their differences from water drop clouds are discussed by Dowling and Radke (1990) and summarized in table 1.1. 1.3. Definition of Cirrus By international agreement, the World Meteorological Organization (WMO) has the responsibility and authority to classify clouds. Clouds are classified primarily by morphology. Their most recent classification system (1975 [1995], p. 16,1987) gives the following definitions: Cirrus: Detached clouds in the form of white, delicate filaments or white or mostly white patches or narrow bands. These clouds have a fibrous (hair-like) appearance, or a silky sheen, or both. Cirrocumulus: Thin, white patch, sheet or layer of cloud without shading, composed of very small elements in the form of grains, ripples, etc., merged or separate, and more or less regularly arranged; most of the elements have an apparent width of less than one degree. Cirrostratus: Transparent, whitish cloud veil of fibrous (hair-like) or smooth appearance, totally or partly covering the sky, and generally producing halo phenomena.

These definitions are entirely morphological and are based on visual appearance during the day time. Properties such as ice content, temperature, altitude, color, and optical depth are not explicitly part of the definition, although all are recognized as relevant. The three genera are further divided into species, again based on morphology. These are listed in table 1.2. "Subvisual cirrus" (Uthe and Russell 1977; Barnes 1980,1982; Heymfield 1986; Sassen et al. 1989; Sassen and Cho 1992; see review by Lynch 1993) and "contrail cirrus" (see review by Schumann, this volume) are well recognized in meteorology but as yet they have no Latin designation and are not currently included in the WMO classification. Subvisual cirrrus are defined as cirrus with optical depths in the visible (0.694 urn) of less than 0.03. Such clouds are often found at the

History and Definition

7

Table 1 .2. Cirrus cloud names Family

Genus

Species

Varieties

High

Cirrus

castellanus

duplicatus intortus radiatus vertebratus

floccus

Cirrostratus Circocumulus

spissatus uncinus fibratus nebulosus fibratus lenticularis castellanus floccus stratiformis

undulatus duplicatus undulatus lacunosus

tropopause and in some parts of the world (namely, the tropics) they may be nearly ubiquitous (see review by Wylie, this volume). Within the conventional meteorological community, associated properties of cirrus include ice content (large), height (high), and optical depth (small). Yet these cloud properties have not been quantified. The high etage usually refers to clouds whose mean lower level is 6km or higher, although there is some latitudinal variation. Altitude, of course, is correlated with temperature in the troposphere, and a high cloud would be expected to be cold, perhaps even colder than the homogeneous nucleation temperature for water of -41 °C. Although cirrus are relatively transparent, no upper limit to the optical depth has yet been offered which has a physical basis. Sassen and Cho (1993) place the upper limit of 0.03 on optical depth for subvisual cirrus. 1.4. Ice as a Classification Indicator

In any scientific field, morphology is the first guide to classification. Such an approach is simple and obvious. But with later physical insight, morphology usually gives way, at least in part, to physical classifications. Were this not the case, then whales would still be called fish and planets would be classified as stars. We now know that a fundamental physical property of cirrus and certain other clouds is ice content. This suggests that ice may be a good basis for classification. The presence of ice, as opposed to water, has tremendous significance. It means that the temperature is probably well below freezing. If the temperature is below -41 °C, then homogeneous nucleation takes place and no liquid can exist. The vapor pressure of water over ice is so low compared to that over water that most vapor is locked up in the crystals, and even small vertical excursions may force the vapor far out of equilibrium with the particles, at least for a while. Ice crystals are usually much larger than water droplets and therefore have much larger fall velocities, a factor in determining cloud morphology and evolution. Crystal habit is determined by at least three parameters: temperature, humidity, and ventilation. Thus, the existence of ice in a cloud can and often is the dominating factor

8

Cirrus

Table 1.3. Principle cloud types that can contain ice Cirrus

Cirrus clouds are composed of ice crystals.

Cirrostratus

Cirrostratus is composed mainly of ice crystals.

Cirroculumlus

Cirrocumulus is composed, almost exclusively, of ice crystals.

Stratus

Generally grey cloud layer with a fairly uniform base, which may give drizzle, ice prisms, or snow grains. Stratus does not produce halo phenomena, except possibly at very low temperatures.

Nimbostratus

Grey cloud layer, often dark, the appearance of which is rendered diffuse by more or less continuosly falling rain or snow . . .

Altocumulus

Altocumulus is, at least in the main, almost invariably composed of water droplets. At very low temperatures, however, ice crystals may form.

Cumulonimbus

At least part of its upper portion is usually smooth, or fibrous or striated, and nearly always flattened; this part often spreads out in the shape of an anvil or vast plume.

From the WMO International Cloud Atlas (1987).

in both the cloud's evolution and its interactions with its surroundings. Table 1.3 lists those clouds that are not classified as cirrus (or Cirrostratus or cirroculumlus) which can contain ice. From table 1.3, we see that 7 of the 10 cloud genera can contain ice or sometimes shows fibrous or striated morphologies which are suggestive of cirrus (and hence ice). Incus (anvil), the supplementary features of cumulonimbus, are made predominantly of ice. Virga is often made of ice and is a prominent component of uncinus. Although no one doubts that most stratus and nimbostratus clouds are entirely made of water, it is important to realize that most genera of clouds can contain ice. According to Huschke (1959 [1980]), all clouds types can contain ice: "Of the cloud genera, only Cirrostratus and cirrus are always ice-crystal clouds; cirrocumulus can also be mixed; and only cumulonimbus is always mixed. Altostratus nearly always is mixed, but can occasionally be ice crystal. All the rest of the genera are usually water clouds, occasionally mixed: altocumulus, cumulus, nimbostratus and stratocumulus." We therefore recognize that a cloud classification could be devised and formalized based solely on ice content. Such an approach might help unify research and cross-link work in different fields concerned with ice: nucleation, crystallography, remote sensing, spectroscopy, and planetary physics. The WMO has already recognized classification by height, a scheme that crosses over morphological boundaries. In support of such a grouping, it may be valuable to perform a modern, visible, and infrared (10 um window) imaging study coupled to a polarization lidar-based survey of all clouds that are cold enough for ice to form. The survey would include simultaneous optical and infrared imagery. Such a survey would produce polarization images of clouds that would unequivocally identify ice in the clouds, measure the temperature distribution, and provide the visual observer with clues as to the state of the water. In effect, the survey could produce water-phase images of the outer layers of clouds.

History and Definition 9

1.5. Summary and Conclusions

All cirrus clouds are composed of ice, but not all ice clouds are cirrus. This is a consequence of the WMO's classification scheme, which is based on morphology rather than on physical content or dynamics. Current classifications do not include subvisual cirrus or contrail cirrus. Developing a subclassification based on ice content could prove useful. Acknowledgments I thank Kenneth Sassen for many discussions concerning the nature of cirrus and ice. This work was supported by The Aerospace Corporation's Independent Research and Development Program. References Abercromby, R., 1887. "Suggestions for an international nomenclature of clouds," Quarterly Journal of the Royal Meteorological Society, vol 13, London, pp. 154-166. Appleman, H.S., 1961. Occurrence and forecasting of cirrostratus clouds. World Meteorological Organization Technical Note No. 40. WMO, Geneva. Barnes, A.A., 1980. Observations of ice particles in clear air, /. Rech. Atmos., 14(3-4), 311-315. Barnes, A.A., 1982. "The cirrus and sub-visible cirrus background," AFGL-TR-82-0193, Hanscomb Air Force Base, MA. Berlin, P., 1988. The Geostationary Applications Satellite. Cambridge Aerospace Series. Cambridge University Press, Cambridge, MA. Descartes, R., 1637. Discours de la Method ... I Doptrique, II Geometrie, III Meteores. Dowling, D.R., and L.F. Radke, 1990. A summary of the physical properties of cirrus clouds, /. Appl Met., 29, 970-978. Fiocco, G., and G. Grams, 1964. Observations of the aerosol layer at 20km by optical radar, /. Atmos. Sci., 21,323. Gershenson, D.E., and DA. Greenberg, 1964. Anaxagoras and the Birth of Scientific Method. Blaisdell Publishing Co., New York. Greenler, R., 1980. Rainbows, Haloes and Glories. Cambridge University Press, Cambridge. Heymsfield, A.J., 1986. Ice particles observed in a cirriform cloud at -83°C and implications for polar stratospheric clouds. /. Atmos. Sci., 43, 851-855. Hildebrandsson, H., 1879. "Sur la classification des nuages employee a 1'Observatoire Meteorologique d'Upsala," Upsala, Sweden, p. 9. Hildebrandsson, H., 1887. "Remarks conserning the nomenclature of clouds for ordinary use." Quarterly Journal of the Royal Meteorological Society, vol 13, London, pp. 140-146. Hildebrandsson, H., Riggenbach, A. et Teisserenc de Bort, L., 1896 [1910]. "Atlas International des Nuages", Paris. Howard, L., 1803. On the Modification of Clouds, and on the Principles of Their Production, Suspension and Destruction. Philisophical Magazine, J. Taylor, London. [Reprinted in Neudrucke von Schriften und Karten iiber Meteorologie und Erdmagnetismus, Tome III, Berlin, 1894 p. 6.] Huschke, R.E., 1959 [1980]. Glossary of Meteorology. American Meteorological Society Press, Boston, MA.

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Lamarck, J.B., 1802. Sur la forme des nuages. In Annuaire Meteorologique pour 1'an XI de la Republic Francois, Paris, no. 3, pp. 149-164. Ligda, M.G.H., 1963. Proceedings of the First Conference on Laser Technology, U.S. Navy Office of Naval Research, Washington, D.C., pp. 63-72. Lynch, D.K., 1993. Subvisual cirrus: what it is and where you find it. In Proceedings of Passive Infrared Remote Sensing of Clouds and the Atmosphere (D.K. Lynch, ed.). SPIE Conference 1934, Bellingham, WA, pp. 264-274. Maiman,T.H., 1960. Stimulated optical emission in ruby, Nature, 187,493^494. Marriotte, E., 1681. De la Nature des Couleurs, Paris. Middleton, W.E.K., 1966. A History of the Thermometer and Its Uses in Meteorology. The Johns Hopkins Press, Baltimore, MD. Olscamp, P.J., 1965. Discourse on Method, Optics, Geometry and Meteorology. The BobbsMerrill Company, Indianapolis, IN. Pernter, J.J., and P.M. Exner, 1910. Meteorologische Optik. Wilhelm Braumuller, Wein. Renou, E., 1855. Insrtuctions Meteorologiques. In Annuaire de la Societe Meteorologique de France, Tome 3, Paris, pp. 142-146. Rossow, W.B., and L.C. Gardner, 1993. "Cloud detection using satellite measurements of infrared and visible radiances for ISCCP," /. Climate, 6,2370-2393. Sassen, K., and B.S. Cho, 1992. Subvisual-thin cirrus lidar dataset for satellite verification and climatological research. /. Appl. Meterol, 31,1275-1285. Sassen, K., M.K. Griffin, and G.C. Dodd, 1989. Optical scattering and microphysical properties of subvisual cirrus clouds, and climatic implications, /. Appl. Met., 28, 91-98. Schiffer, R.A., and W.B. Rossow, 1983. The international satellite cloud climatology project (ISCCP): The first project of the World Climate Research program, Bull. Amer. Meteor. Soc., 64, 779-784. Schmidt, E.O., and D.K. Lynch, 1995. Subvisual cirrus: associations to the dynamic atmosphere and radiative effects. In European Symposium on Satellite Remote Sensing II, Proceedings of Passive Infrared Remote Sensing of Clouds and the Atmosphere III, SPIE 2578 (D. Lynch, ed.). Sept. 25-28, Paris, pp. 68-75. Stone, R.G., 1956. A Compendium on Cirrus and Cirrus Forecasting. AWS TR 105-130, Air Weather Service, Scott Air Force Base, IL. Tape, W, 1994. Atmospheric Halos. Antarctic Research Series, vol. 64. American Geophysical Union. Washington, D.C. Tricker, R.A.R., 1970. Introduction to Meteorological Optics. Mills and Boon, London. Uthe, E., and P.B. Russell, 1977. Lidar observations of tropical high altitude cirrus clouds. In Proceedings of the IAMAP Symposium on Radiation in the Atmosphere, GarmischPartenkirchen, Science Press, pp. 242-244. Vaeth, J.G., 1965. Weather Eyes in Sky: America's Meteorological Satellites. Ronald Press. Venturi, G.B., 1794. "Indagine fisica sui colori (Modena)," Soc. Ital. Mem. VIII, 699-754. World Meteorological Organization (WMO), 1975 [1995]. International Cloud Atlas, vol. I, Manual on the Observation of Clouds and Other Meteors. WMO, Geneva. World Meteorological Organization (WMO), 1987. International Cloud Atlas, vol. II, Plates. WMO no. 407, Geneva. Young, T, 1802. An account of some cases of the production of colours, not hitherto described, Phil. Trans. Roy. Soc., 92,378-397.

2

Cirrus Clouds A Modern Perspective

K E N N E T H SASSEN

2.1. Growing Importance of Cirrus Research

It is now understood that the cirrus clouds inhabiting the upper troposphere play a significant role in regulating the radiation balance of the earth-atmosphere system and so must be recognized as a crucial component in solving the humaninduced climate change puzzle (Liou 1986). Because of their high altitudes, these cold, ice-dominated clouds act as a thermal blanket by trapping the outgoing terrestrial (infrared) radiation, but, at the same time, they can be effective at reflecting the incoming solar radiation back out to space. The balance between these two radiative processes, the greenhouse and albedo effects, respectively, determines the net impact of cirrus on our climate system. Which process dominates appears to be quite sensitive to the cloud microphysical and macrophysical properties (e.g., see Stephans et al. 1990). These properties in turn depend on the weather processes that generate cirrus, a function of geographic location, thereby complicating the global view. Of current concern is comprehending how cirrus clouds will respond, or feedback, to the effects of global warming caused by the buildup of carbon dioxide and other greenhouse gases. Would the changing atmosphere produce alterations in cirrus clouds that reinforce, or act to negate, the theoretically predicted global warming surmised from fundamental physics? One must also ask whether increasing jet aircraft traffic is creating more cirrus cloud cover, and if this traffic and agricultural activities are increasing the transport of dust and smoke particles into the upper troposphere and affecting, in a radiatively important sense, those cirrus formed naturally. Settling these issues could be pivotal to making difficult decisions on the future use of the Earth's resources.

11

12

Cirrus

Fortunately, a new generation of meteorological instrumentation has become available. The need for these new measurement capabilities has helped to spawn and adapt instrumentation for cirrus research. Sophisticated cloud measurement capabilities using in situ probes on jet aircraft, satellite multispectral imaging, and remote sensing with lidar, short-wavelength radar, and passive radiometers, have all greatly facilitated cirrus cloud research. Major advancements have also been made in the field of numerical cloud modeling. As will be reviewed briefly here and in depth in following chapters, these developments have significantly advanced our knowledge of the characteristic properties of cirrus clouds over the past few decades. Chapter 1 of this book gave the historical view of our understanding of cirrus clouds. Here in chapter 2, the emphasis is on what can be revealed about the basic nature of cirrus clouds using modern instrumentation, as illustrated by findings from the ground-based remote sensors at the Facility for Atmospheric Remote Sensing (PARS). In these introductory chapters we are attempting to provide a working definition for what are and are not cirrus clouds, based on the traditional view, but with some input from modern research to refine our knowledge. As pointed out by Sassen and Krueger (1993), modern remote sensors can be relied on to provide incredibly detailed information on clouds to supplement visual inspection. At the same time, although defining cirrus may seem straightforward to any trained professional weather observer, today clouds are viewed from aircraft and satellites and probed with laser and radar beams in portions of the electromagnetic spectrum that are far beyond the capacity of the human eye. This sophistication may present certain difficulties. This is not to say that remote probing techniques should not be used to identify and characterize cirrus clouds—such techniques are critically important due to the remoteness of cirrus—but we must first learn how our modern instruments respond to this class of clouds. We consider it vital for modern climate research that a consistent terminology for cirrus clouds be adopted to facilitate intercomparison of ground-based visual, remote sensing, in situ, and satellite top-of-atmosphere measurements. To end this introduction and begin a modern examination of cirrus clouds, I provide in figure 2.1 a typical image of a mid-latitude cirrus cloud, variety cirrostratus fibratus, drawn at random from the extensive record of lidar height-time displays to be discussed here. The data were collected using a vertically pointing lidar on the afternoon of May 15, 1992, as the cirrus clouds advected over our field site in Salt Lake City, Utah. I will return to this image several times.

2.2. Modern Cirrus Research: Capabilities and Limitations The recent advances in cirrus cloud research capabilities have included aircraft probes, ground-based (and airborne) active remote sensing using lidar and radar, and passive radiometric probing, either from Earth-orbiting satellites or from the ground as part of multiple active/passive remote sensor techniques. The major breakthroughs occurred during the 1960s, which saw the launching of the first weather satellite, the use of more sophisticated cloud radars, and the invention

Figure 2.1. A typical lidar returned energy display of the troposphere on May 15, 1992, when cirrus clouds were observed at PARS over Salt Lake City, Utah. Note the stratification in aerosol backscattering with height and consider the clouds that form in these regimes—low-level cumulus, mid-level altocumulus, and high-level cirrus clouds.

14

Cirrus

of the laser. Although these technologies are still evolving, it must be recognized that each method has its advantages and disadvantages. Because these instruments are being increasingly relied on to improve our knowledge of cirrus clouds, it is important to consider what these techniques actually measure. In situ instruments have come a long way since the pioneering cirrus cloud studies were carried out over World War II Germany in Luftewaffer fighter planes, amid growing concerns over the military consequences of contrail formation (Weickmann 1947). By the early 1970s laser-based probes with sophisticated electronic processors were developed to facilitate analysis of cirrus cloud content (Knollenberg 1976). These Particle Measurement System probes are now widely used to determine the ice crystal size-distribution for particles ranging from about 25 to 4000 urn maximum dimension. Another device based on the forward-scattering spectrometer principle has shown utility in sampling relatively minute ice particles as small as a few microns in diameter, but only under limited conditions (Gayet et al. 1996). However, these probes cannot generally determine the three-dimensional shape and mass of the particles. The direct sampling of cirrus ice crystals on treated substrates for later microscopic examination has certain advantages in determining particle shape and density and has been popular since the mid-1940s (Arnott et al. 1994; Sassen et al. 1994,1995,1998). These devices have not been widely used, though, because methods to electronically analyze the data in the laboratory are still in their infancy. The main drawbacks of existing instrumentation are their inability to effectively characterize the small (< 200 |im) crystal component and to directly measure ice water content and important optical parameters. Aircraft operations also inherently suffer from limited spatial coverage and limited instrument sampling volumes. Nonetheless, summaries of cirrus cloud microphysical properties derived from aircraft research can be found in Heymsfield and Platt (1984), Liou (1986), and Dowling and Radke (1990), and a number of new devices have recently appeared that attempt to tackle the current limitations. Chapter 4 provides a review of aircraft cirrus research. The ability to view cloud formations from outer space can be traced back to the polar-orbiting Television and Infared Observations Satellite (TIROS-I), launched in 1960, and the Geostationary Operational Environmental Satellite (GOES) series, first launched in 1974. Even these early satellites took advantage of visible and thermal infrared measurements, and such multispectral techniques are becoming more sophisticated as additional bands, and increased resolutions, are incorporated into the suite of orbiting sensors (Rossow and Garder 1993). Most prominent is the Advanced Very High Resolution Radiometer (AVHRR), as well as the new generations of radiometers to be flown on the Earth Observation Satellites (EOS). Attempts to identify and characterize cirrus cloud properties are based on increasingly advanced algorithms that exploit differences in measured multispectral radiances, as predicted by radiative transfer models. Essentially, the methods attempt to identify cirrus as appropriately high, cold, and optically thin clouds by relating solar and terrestrial radiances (see chapter 7). It is well known that cirrus clouds present unique challenges to satellite researchers. Among the weaknesses in satellite cirrus cloud retrieval are prop-

A Modern Perspective

15

erly accounting for the effects of cloud fraction and spatial inhomogeneities; characterizing the background (i.e., due to surface, clear sky and/or lower cloud) radiances; understanding nonspherical ice-particle scattering behavior (and their variations with size and habit); problems inherent in inferring cloud optical depth; and maintaining accurate instrument calibrations (Minnis 1998). Most fundamental are the questions of how many cirrus clouds go undetected because they are too thin, and how many cold and high clouds are characterized as cirrus even though the clouds are the tops of deep cloud systems such as altostratus or cumulonimbus. Recalling the traditional definition of cirrus based on their visual appearance from the ground, it can be appreciated that new problems are created when cloud systems are viewed by cameras from the top. Lidars and millimeter-wave radars have also reshaped the remote sensing landscape. Lidars were applied to the study of cirrus clouds not long after the invention of the laser (Schotland et al. 1971). Although weather radar has been popular since the post World War II period, it was only more recently that sufficient effort was given to improving the millimeter-wave radar technologies best suited for probing nonprecipitating clouds and cirrus (Pasqualucci et al. 1983), as a result of the wavelength Ar4 Rayleigh scattering law. As active remote sensors, lidar and radar provide accurate range resolution, down to the scale of meters and tens of meters, and come with a variety of techniques such as polarization diversity and Doppler velocity (see, e.g., Sassen et al. 1989a). By the time cirrus were recognized as worthy of a major field study, the 1986 First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment Intensive Field Observation (FIRE IFO I) program in the central United States, it was clear that ground-based and airborne lidars and millimeter-wave radars were indispensable for studying the properties of cirrus clouds (Sassen et al. 1990). It is important to recognize that due to fundamental differences in the scattering of light and microwaves by hydrometeors of diameter d (i.e., the d2 Mie/geometric optics versus the d6 Rayleigh domains), lidars and radars sense quite different cloud properties (see chapter 8). Although lidar probing seems ideally suited for cirrus research because of the great sensitivity of laser light to all sizes of hydrometeors, and it can even detect subvisual cirrus from the ground (Sassen et al. 1989b; Sassen and Cho 1992) or from orbit (Winkler and Trepte 1998), the consequence of this sensitivity can be strong range-limiting optical attenuation. Fortunately, this is typically not a problem in penetrating cirrus with modern lidars, but the presence of most lower cloud layers is a barrier for lidar probing. Moreover, the basic uncertainties in interpreting the backscattered signal in simple lidars can be largely overcome with Raman or high spectral resolution lidar techniques, which use complementary spectroscopic data to derive quantitative optical coefficients. The polarization lidar technique is particularly well suited for sensing the composition of cirrus (Spinhirne et al. 1983, Sassen 1991, 2000b; Platt et al. 1998). Lidar linear depolarization ratios are inherently sensitive to ice-particle shape and orientation (Takano and Liou 1995), and the ability to discriminate cloud thermodynamic phase is unambiguous. The use of microwave radar in cirrus research is ultimately limited by the Rayleigh scattering response of small ice particles due to the d6 dependence (thus

16

Cirrus

the push to improve the performance of W- and K-band radars at about 3 and 10mm wavelengths). Fortunately, even at millimeter wavelengths, attenuation in cirrus is small, and the non-Rayleigh effects from the largest particles present do not have a significant impact on the quantitative analysis of the returned signal using Rayleigh theory (Liao and Sassen 1994), the inherent advantage of radar studies. The basic problem remains, however, that where cirrus particle generation is taking place, or at extremely low temperatures, such as typically near cloud top, the small particles present impose perhaps insurmountable difficulties for radar detection (Sassen and Khvorostyanov 1998). Nonetheless, it is possible to accumulate large, climatologically representative data sets of cirrus cloud properties at high temporal and spatial resolutions using dedicated ground-based, remote sensing facilities such as the University of Utah PARS (Sassen 1997) and the Department of Energy Clouds and Radiation Testbed (CART) sites (Stokes and Schwartz 1994). Although the findings from such extended-time data sets are restricted to single locations, a number of geographically representative sites can be relied on to yield characterizations on a more global scale. At this time, extended ground-based cirrus cloud property data sets of various lengths have been reported from southern and northern Australia (Platt et al. 1987), the tropical western Pacific region (Platt et al. 1998), southern Japan (Imasu and Iwaska 1991), and the eastern Great Basin of the United States (Sassen and Cho 1992). By far the most comprehensive study is the 12+-year lidar measurement program from PARS, discussed below. Finally, the soundest approach to research cirrus is to synergistically combine as many remote sensor observations as possible. In addition to lidar and radar, it is often advantageous to use radiometers sensing in the visible, infrared, and microwave region. For example, the combined lidar and mid-infrared radiometer (LIRAD) method is useful not only to determine the cloud infrared emittance, but also to improve lidar optical depth assessment (Platt 1979; Platt et al. 1987,1998). The unitless emissitivy parameter e is used to characterize the grayness of an emitter with respect to a black body at the same temperature. Details of this and other cirrus remote-sensing methods are given in chapters 8 and 10. 2.3. Fundamental Cirrus Cloud Properties

The internationally accepted definitions for the family of cirrus clouds are at the foundation of our FARS research program and this chapter. As discussed in chapter 1, according to the World Meteorological Organization, the operational reporting of the low, middle, and high (i.e., cirrus) cloud categories is made by trained ground observers based on visual appearance. The only way of comprehending the morphological distinctions between the various species and varieties of the cloud genera is to study a cloud atlas book (e.g., WMO, 1975). Here I consider additional characterizations, based on cloud height, optical depth, thermodynamic phase, and formation mechanism. The rational scheme for discriminating between cloud types based on visual observations has been in practice for more than a century. As a corollary, the low,


17

middle, and high cloud categories are divided (loosely) on ranges of cloud height (for a given latitude and season), and so by analogy on cloud phase. Although not all ice clouds are cirrus, a basic postulate is that despite the fact that some cirrus may contain patches of supercooled liquid water (SLW) clouds or even form with the aid of transient SLW (see 2.6), they are predominantly ice-phase clouds. Other types of ice clouds that occur in the polar boundary layer or stratosphere are not cirrus: they may at times closely resemble proper cirrus in terms of content or laser-scattering signature (Gobbi et al. 1998), but they have been assigned other designations. The altostratus cloud is also ice dominated and often results from deepening cirrostratus, but it is classified as a mid-level cloud because of its considerable depth and dark appearance. Low-level ice clouds derived from glaciated water clouds often generate exceptional optical displays in Arctic and Antarctic regions (see Greenler 1980; Tape 1994). In comparison, complex halo/arc displays are rarely observed in cirrus, although the common 22° halo and associated arcs are frequent at many mid-latitude locations. It has been pointed out (Sassen 1999) that cirrus halo frequency appears to be a function of geographic location, and thus of the regional weather conditions and the nature of the cloud-forming particles that generate the local cirrus. It is suggested, then, that one of the distinguishing features of cirrus is the background aerosol in the relatively clean upper troposphere, on which the cirrus ice crystals form and take shape. Note the vertical distribution of the weakly scattering aerosol component in figure 2.1. Portrayed are the relatively dense materials near the surface, where convection from solar heating is gradually raising aerosols up to approximately 4km above mean sea level (or -2.5 km above ground level at PARS), the height of the previous day's convective boundary layer. Above this lies a diffuse regime of more aged aerosols in the planetary boundary layer, extending to nearly 6 km, and above that is molecular-dominated scattering. Although this example may be unusually clear cut, the low, middle, and high cloud categories form and dwell in different temperature and aerosol regimes. Cirrus are ice-dominated clouds that by definition inhabit the upper troposphere, where it is so cold that cloud droplets are only transient and cannot be primarily responsible for cirrus generation. Thus, it is now accepted that the precursors to cloud droplets (i.e., minute haze particles composed of aqueous solutions), are involved in the production of cirrus. Because the height of the tropopause marking the top of the troposphere depends on latitude and season, the maximum heights of cirrus will similarly vary, but what of their minimum heights? Unfortunately, the seasonal variability of both the cirrus cloud top and base heights derived in PARS lidar data is so great that we cannot attempt to specify the minimum and maximum heights that distinguish the three main cloud categories. In other words, winter cirrus-cloud top heights can lie below the base heights of summertime middle level altocumulus clouds, so cloud identification criteria based strictly on height would be ambiguous. Although weather observer cloud-height reports are routinely made, it must be recognized that the heights are based on conventional estimations for cirrus clouds (in the absence of sufficiently powerful laser ceilometers), and so do not necessarily

18

Cirrus

reflect the actual cloud base heights or the ceiling altitudes of importance to aviation. Another component used in identifying middle and high clouds is color or cloud transparency. The terms "translucidous" and "opaquis" are applied to layers through which the blue sky color can and cannot be seen by a ground observer, respectively. As a cirrostratus cloud gradually thickens from thin to opaque in response to developing weather patterns, they often evolve into altostratus clouds. Such mid-level clouds are operationally distinguished from cirrus by the visual loss of sharpness (or disappearance) of the solar (or lunar) disk, accompanied by descending cloud base heights. These useful distinctions have been related to approximate ranges in lidar-derived cloud optical thickness t. Characteristic T limits for subvisual cirrus, a category that obviously had to await the introduction of lidar, the thin-to-opaque cirrus change, and the cirrostratus/altostratus transition are shown in table 2.1. The backscattered signals from powerful laser beams become completely attenuated in ice clouds when T exceeds about 3.0 using anolog photodetectors (Kinne et al. 1992), which corresponds to the point where the sun visually becomes dim and irregular (Sassen and Cho 1992). This upper limit in i for cirrostratus, however, may not always hold for some varieties of cirrus. The chief problem is related to optically thick but spatially limited portions of cirrus layers, especially in cirrus derived from deep convection. In surface weather reports, a cumulonimbus cloud with spreading anvil is listed only in the low cloud category as long as the associated clouds are clearly a part of the thunderstorm. An obvious gray area arises as the cumulonimbus further develops and eventually decays, for at some point the anvil may spread significantly and become detached from the originating turrets, at which time it is appropriate to designate the remnants of the anvil as some variety of cirrus. Once the low and middle portions of the decaying cumulonimbus have eroded away in the precipitation process, the remaining high cloud is referred to as cirrus spissatus. So, at some point the maintenance of the cirrus initially generated by deep convection is no longer dominated by the effects of strong updrafts (although the injection of boundary layer moisture and aerosols may have lasting effects), and this should be used as the working definition for the cirrus derived from anvils. Unfortunately, it is often difficult to judge this turning point from surface observations. The classic picture of an isolated spissatus derived from a thunderstorm, however, often contains a central ice fallstreak that is relatively dense and could obscure the sun. Thus, in the case of cirrus spissatus, the tradiTable 2. 1 . Cirrus cloud categories and approximate optical depths based on cloud transparency and color Category Subvisual Thin Opaque Altostratus

T range 3.0

Description Invisible against the blue sky Translucent, retains a bluish color Usually appears white Disk of sun becomes indistinct


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tional cloud identification implies that a portion of the cloud mass could exceed the T limit derived for the cirrostratus/altostratus transition. Given that cirrus cloud type identification and species refinement can be aided by knowledge of the cloud-generating mechanism, cloud genesis is also important to shed light on fundamental cloud properties. Principally, these properties, such as cloud layer thickness and structure and ice particle number size distribution, will depend on the magnitude of the updraft velocity and the temperature in the generating region, as constrained by the action of the adiabatic and nucleation processes. Obviously, cloud content has a large impact on their radiative and climatic properties. To help understand the nature of cirrus cloud varieties, table 2.2 provides a breakdown of cirrus cloud-generating mechanisms into one human-made and four basic natural categories. The aircraft-induced cirrus category involves the initial rapid formation of a condensation trail (contrail) from aircraft-supplied moisture and nuclei (likely sulfur-based haze particles, which freeze homogeneously during the mixing process), typically resulting in the creation of high numbers of minute ice crystals (Sassen 1997). Subsequent development into cirrostratus can occur under some ambient conditions as a result of contrail-spreading processes, until the layer can no longer be distinguished from natural cirrus as observed from the ground or satellites. Note, however, that it is unknown how long the contrail-cirrus cloud composition will remain distinct because of the extra cloud-particle-forming nuclei introduced by jet engines. Of the four additional cirrus cloud-generating mechanisms in table 2.2, both the orographic and thunderstorm cases can involve ice formation in relatively strong updrafts of a few meters per second or more, which tends to increase ice particle concentrations according to model studies (e.g., Jensen et al. 1994). Ice nucleation from highly supercooled cloud droplets is also possible at certain temperatures. As in the case for contrail-cirrus, anvil cirrus in the maintenance stage should remain affected by the "foreign" nuclei lifted essentially from the boundary layer. (They are foreign in the sense that convectively-raised aerosols may normally represent a portion of the background aerosol in the upper troposphere, but depending on season and latitude, other nuclei derived from the stratosphere, for example, may be more numerous.) The synoptic cirrus category is a catch-all for the usual varieties of cirrus clouds that form in situ in the upper troposphere in response to weather disturbances. Updraft speeds can range

Table 2.2. Breakdown of cirrus clouds by generating mechanism Category

Mechanism

Synoptic (jet stream, frontal, etc.) Injection cirrus Mountain-wave updraft Cold trap Contrail-cirrus

Top-down generation Thunderstorm anvil Orographic, terrain-induced Tropopause-topped thin layer Rapid cooling of aircraft exhausts

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Cirrus

widely from the centimeter per second scale typical of gradual uplift (i.e., frontal overriding) to the meter per second scale of the single convective uncinus cell. Models show that such cirrus typically form from the top (where particle generation occurs) down, due to the strength of the particle sedimentation process (Starr and Cox 1985; Khvorostyanov and Sassen 1998). The particle precipitation and evaporation processes can destabilize regions of the atmosphere, however, contributing to additional cirrus cloud development below the original generating layer. The final category of tropical thin-to-subvisual tropopause cirrus has only been recently recognized. Their large spatial extent has been illustrated by spaceborne lidar measurements (Winker and Trepte 1998). We refer to such clouds as "cold trap" cirrus in view of their quintessential properties: they occur under uniquely cold (-70° to -90°C) and high (15-20 km) conditions, which are rarely encountered outside the tropics. These tenuous layers appear to be composed of relatively small ice crystals (Heymsfield 1986) and may be maintained by moisture supplied by deep convection. 2.4. Cirrus Cloud Characterization from PARS: A Modern Conception

To help improve our understanding of cirrus cloud properties, their connection to weather, and provide graphic illustrations of their structures, I draw here from the results of a 12+-year cirrus cloud study using lidar and other remote sensors from PARS (see Sassen 1997). PARS is a unique university-based research station located at 40°49'00" N, 111°49'38" E on the bench of the Wasatch Mountains (1.52km mean sea level), overlooking Salt Lake City, Utah. Since 1987 PARS has housed the ruby cloud polarization lidar (CPL) and a growing number of remote sensors, including a suite of visible, infrared, and microwave radiometers, all-sky imagery, a 3.2-mm polarimetric Doppler radar, and the dualwavelength scanning polarization diversity lidar (PDL). All the active remote sensors use polarization diversity to enhance the information content of the backscattered signals. For the purpose of the cirrus climatology described below, we rely mainly on data collected by the "turnkey" CPL system, which consists of a high-power (1.5 J per pulse) ruby (0.694 jam) laser with a low pulse repetition frequency (PRF) of 0.1 Hz and a recorded range resolution of 7.5m. The CPL generates a manageable amount of digitized data, which facilitates the management of an extended data set, but its sensitivity is sufficient to treat each pulse as independent. In comparison, the PDL is truly a high-resolution device: it can record the four polarization channels (at the 0.532 and 1.06 urn wavelengths) at a maximum resolution of 1.5m and a PRF of 10 Hz (Sassen 1994). Since its inception, PARS has been applied to the regular study of cirrus clouds in support of basic research and the satellite validation effort of the FIRE extended-time observation program (Schiffer and Rossow 1983). Currently in its 12th year, the PARS cirrus cloud data record is unique in that it is both long term and sufficiently large to be considered statistically significant on a monthly level. As of March 1997 (the cutoff for the current climatological analysis), more than


21

2200 h of ruby lidar data had been collected. PARS data collection goals emphasized the afternoon and evening hours corresponding to local National Oceanic and Atmospheric Administration polar orbiting satellite overpasses and hourly GOES imagery. Normally, the goal of collecting 20 h of PARS data per month was achieved, typically in 1- to 3-h periods. Note that for the special study described in section 2.4.2, a cirrus data period is defined as a 10-min average of cloud layer properties obtained from 0-10 min past the hour within ±3h of the 0000 UTC Salt Lake City sounding, in order to ensure representative atmospheric state data. It is clear from the preceding discussion that the basic criteria long relied on to identify cirrus clouds is their visual appearance: texture, color, spatial extent and variability, apparent phase, and optical depth. Thus, PARS cloud categorization has been consistently accomplished through visual inspection by an observer trained by the National Weather Service (then the Weather Bureau) as a Meteorological Technician. Importantly, no a priori constraints involving cloud height or temperature, or such other factors as lidar or radar signal intensity ranges, are used at PARS to define cirrus. However, the ability of polarization lidar to provide extra information (e.g., cloud phase and height) is exploited to corroborate the initial categorization, particularly at night. 2.4.1. Cirrus Cloud Case Studies Here I provide remote sensing displays of the typical structures and laser depolarizing properties of the principal cirrus cloud types studied at our midlatitude location—cirrus fibratus, cirrostratus, cirrocumulus, and cirrus spissatus—also illustrated are high-resolution views of frequent substructures such as "mares tails" and cirrus mammatta. To give a pictorial view of these cirrus case studies, plate 2.1 includes 180° fisheye photographs obtained during daylight observations at PARS. (Note: All plates can be found in the appendix which begins on p. 457) Each subsequent plate shows zenith-pointing lidar height-versus-time displays of attenuated backscattering (in relative units based on a logarithmic gray scale) and linear depolarization ratios (5, using the color scale at bottom right of plate), along with the closest radiosonde temperature and dewpoint profiles. Note that the cloud structure depicted in the returned laser power displays are not equivalent to snapshots of the entire cirrus cloud field because of the temporal alterations that occur in the clouds as they advect over the lidar. However, the resultant changes in individual cirrus structures are probably not major: generally speaking, cirrus cloud elements develop more slowly than they advect in the relatively rapid transport of the upper troposphere. As for the interpretation of the 8 value displays, this is a complex issue, facets of which I attempt to illuminate below. Reviews of this topic are given in Sassen (1991, 2000); it must suffice to say here that ice clouds generate a wide variety of 8 values that depend explicitly on ice-particle shape and orientation: polarization lidar studies indicate that cirrus produce 8 > 0.3, up to approximately 0.7, although much lower values are possible when oriented plate crystals are probed in the zenith direction. A trend that is becoming increasingly apparent, however, is that laser backscatter depolarization increases with decreasing cloud

22

Cirrus

temperature, and so indicates that ice crystal shape is in some manner fundamentally dependent on temperature. It has long been known that a fundamental generating structure is the ubiquitous cirrus uncinus cell (Ludlam 1956;Harimaya 1968;Heymsfield 1975). Cirrus uncinus display a relatively dense generating cell "head" and a "tail" of precipitating ice crystals, often referred to as "mares tails" because of their plumelike appearance. (They equally resemble the barnacle appendage "cirrus", the biological source term for this cloud genera.) The width of the uncinus cell head is on the order of 1.0km (Heymsfield 1975; Sassen et al. 1989b). However, individual uncinus cells can be composed of groups of much smaller (~100m) sporadic updrafts, and they can also be assembled into mesoscale uncinus complexes (MUC) with dimensions of tens to hundreds of kilometers. The necessary condition for uncinus development should involve some slight instability in the atmospheric profile, which spawns convection and updrafts up to about 1ms"1. However, a convective appearance to cirrus cloud-top is often detected by lidar (see fig. 2.1 and below) without indications of instability, so cloud-scale convective processes may be more important in cirrus clouds than soundings would predict. This progression, from the scale of the mares tail to the synoptic arc of prefrontal cirrus clearly visible from satellites, is portrayed in the following figures and plates. Figure 2.2 is the high-resolution PDL view of a relatively narrow cirrus layer embedded in a 4-km deep jet-stream cirrus (Sassen et al. 1995), which consists of a series of uncinus cell heads generating a layer of sheared fallstreaks. The height and time coordinates have been adjusted using the wind speed to yield a 1:1 correspondence in height and distance (time is shown). Although the cell heads are 0.5-1.Okm across, at this resolution they can be seen to be composed of smaller substructures, each of which produces a thin fallstreak that combine to define (i.e., at coarser resolution) the main solid cirrus layer. Plates 2.2-2.4 depict the progression in the development of cirrus fibratus, cirrostratus, and cirrus spissatus from the apparent action of cirrus uncinus as

Figure 2.2. A high resolution (6m by O.ls) gray-scale display of 0.532 urn polarization diversity lidar backscattering (in arbitrary units) revealing the structure of a cirrus layer created by complexes of cirrus uncinus cells. Data were collected on December 5,1991 during the FIRE IFO II at Coffeeville, Kansas.


23

observed at FARS. Plates 2.la and 2.2 show a classic fibratus case. Even at the 0.1 Hz temporal resolution, there is evidence for convective cell-generating structures present at the top of the fibratus streaks with massive tails of sheared particle fallstreaks below. The zenith lidar depolarization data indicate that a wide range of particle shapes and orientations were present, including the quite low (8 < 0.05) values typical of horizontally oriented plate crystals. Plate 2.3 represents the transition from a cirrostratus overcast to a series of serrated cirrus fibratus masses. Here, particularly at the right, the concept of the MUC is well portrayed, with each cloud-top generating region producing a sheared mesoscale fallstreak. As is typical for cirrus clouds, note the tendency for the highest depolarizations to occur near cloud top, with decreasing values (at warmer temperatures) below. Also note the three embedded aircraft contrails at an altitude of about 12km revealed by the strongly scattering streaks near the beginning of the period, and the cirrus cloud mammatta-like structures at cloud base from 0140 to 0210 UTC, which, as examined in more detail below, are not uncommon in deep cirrus clouds. Plate 2.4 further develops this scenario from CPL data in cirrus fibratus, in this case showing the periodic production of optically dense ice particle fallstreaks from cloud-top mesoscale generating structures. The apparent decreases in lidar cloud top heights at 1905,1925, and 2005 UTC were probably caused by strong optical attenuation, and these cloud elements were visually identified as cirrus spissatus (plate 2.1b). This example also shows the characteristic depolarization increase with height, and the presence of cirrus mammatta near the end of the period. Figure 2.3 is a high-resolution PDL returned power display of a particularly intense mammatta protruding from the base of a dense cirrus fibratus cloud. This unusual image represents a cross-section of a mammatta that apparently passed directly above the lidar, and which produced such strong optical attenuation that the lidar penetration depth was momentarily restricted to about 0.5 km in the 4.0-km deep spissatus layer. Note the depiction of various scales of turbulent eddies during its downward penetration into dry subcloud air. Plate 2.5 is a CPL image of the development of cirrus spissatus from a cirrostratus layer, which may have been derived from a distant thunderstorm anvil (see plate 2.1c). This case depicts a number of additional, common cirrus cloud structural properties, including a fetch of wave motions embedded at cloud top and base at the beginning of the period, and the range-limiting attenuation generated by the major precipitation streak around 2230 UTC, which was successful at moisturizing the subcloud environment and establishing a new lower cirrus layer (see also Sassen et al. 1990,1995,1998). The depolarization record is particularly diverse showing 5-value variations in association with the cloud wave motions, unusually high 8-values in portions of the anvil, and 8-values < 0.05 in the lower cirrus from oriented ice plates. Plates 2.6 and 2.7 ilustrate proper cirrostratus cloud conditions. Plate 2.6 depicts a rather extreme example of how vertically pointing lidar 8-values respond to cloud regions containing randomly and horizontally oriented ice plates. Note the strongly scattering and low-depolarizing cloud streaks in this thin, 22° halo-producing cirrostratus layer (see fisheye in plate 2.1d). Several contrails are at times present near the cloud top height. Plates 2.1e and 2.7 show a

24

Cirrus

Figure 2.3. A very high resolution (1.5m by O.ls) polarization diversity lidar display of a vigorous cirrus spissatus mammatta probed on April 21,1996 from the DOE CART site near Lament, Oklahoma.

classic cold mid-latitude cirrostratus derived from the advection of subtropical moisture, whose cloud tops extended into the bottom of the stratosphere. This cloud generated frequent solar corona, indicative of 10-30 um effective diameter ice particles (Sassen et al. 1998). Also note the relatively strong 5-values in this cold cirrus and the strongly scattering laminae within the cloud. As for cirrocumulus, after many years of PARS cirrus cloud observations, our group has concluded that the classic small-celled cloud depicted in cloud atlases does not apply to ice-phase cirrus clouds. Rather, when such cirrocumulus clouds are probed by polarization lidar, they invariably turn out to be unusually high-altitude altocumulus composed primarily of supercooled cloud droplets. Thus, although some cirrus layers have an obvious cellular appearance, particularly in contrails, orographic, and anvil cirrus, the classic ice-phase analog of the attractive array of cells covering the sky with dimensions of 1° (the approximate diameter of the sun or moon) do not appear to exist, at least not at our midlatitude location. Figure 2.4 is a high-resolution PDL returned energy display of a 1-h-old contrail, or group of contrails, studied during the SUCCESS field campaign in Oklahoma (Sassen and Hsueh 1998), which resembles cirrocumulus. However, such cellular structures are so common in contrails that they may be more a result of frozen contrail formation processes than atmospheric instability effects. An example of a typical, relatively wide-cellular cirrus that can be referred to as cirrocumulus is given in plate 2.8 for a PDL case study of an orographic cirrus wave cloud. Embedded within the complexly structured wave cloud are several


25

Figure 2.4. High resolution image of spreading contrails, resembling cirrocumulus, and natural cirrus probed in the 1.06 urn polarization diversity lidar channel at the CART site on May 2,1996.

layers showing pronounced cellularity, which often gave this cloud the appearance of cirrocumulus (see corresponding fisheye in plate 2.1f).The expanded view of one of these regions given in figure 2.5 takes advantage of the high-resolution (10 Hz by 1.5m) lidar capabilities, revealing the detailed structures of the elements. 2.4.2. Cirrus Cloud Statistical Properties Provided in table 2.3 are the seasonal and yearly (total) averages of a number of cirrus cloud properties characteristic of the PARS location on the eastern edge of the Great Basin. The method for determining the statistics for the cirrus cloud base and cloud top heights is based on the envelope method, in which either the top and bottom of a single layer or the top of the highest layer and base of the lowest cirrus layer are used. Also included in table 2.3 are the average cirrus layer thickness when multiple layers are present and the average thickness of all combined single and multiple layers. These distinctions result from the propensity of some cirrus systems to form and maintain multiple cloud layers (i.e., using the criterion of a >0.5-km gap in lidar signals), although it must be recognized that in many instances a single 10-min observation period may yield multiple layers that merely represent breaks in a vertically continuous layer that are due to the effects of wind shear on fallstreaks. This sample also uses only those cirrus that did not appear to be affected by a loss of cloud-top signals due to rangelimiting laser attenuation in dense cirrus or, more commonly, the limited (8bit) dynamic range of the detector package in the presence of a strongly backscattering layer. The data in table 2.3 represent the most comprehensive examination of cirrus cloud properties ever assembled from a particular location using modern remote sensors. In addition to the atmospheric parameters characterized, supplemental information on the prevailing cirrus all-sky coverage and the visual appearance of the cirrus in the zenith are included. Note that the visual appearance

26

Cirrus

Table 2.3. Seasonal and yearly averages of various cirrus cloud properties derived from the 10-year PARS data set

Cloud base Height (km) Pressure (mb) Temperature (°C) Wind direction (°) Wind speed (m/s) Cloud top Height (km) Pressure (mb) Temperature (°C) Wind direction (°) Wind speed (m/s) Cloud thickness (km) Layer envelope Multiple layer All cases Sky coverage (%) Overcast Broken Scattered Visual appearance (%) Opaque Thin Very thin

Jan-Mar

Apr-Jun

Jul-Sep

Oct-Dec

Total

8.40 353.4 -38.9 288.7 17.9

8.89 329.9 -38.4 272.5 16.0

9.10 326.9 -32.6 252.2 13.9

8.89 332.0 -39.0 281.8 18.8

8.79 336.3 -37.4 276.3 16.4

10.71 248.2 -55.8 285.0 23.9

11.14 235.1 -55.3 270.8 19.8

11.12 243.5 -47.6 252.9 17.0

11.15 233.9 -55.9 284.9 21.3

11.02 240.2 -53.9 275.7 20.2

2.31 1.32 1.93

2.25 1.13 1.72

2.02 1.17 1.60

2.26 1.38 1.91

2.23 1.24 1.81

75.7 19.4 4.9

63.9 30.8 5.0

40.5 49.4 10.1

69.2 22.6 4.8

64.9 29.0 6.0

42.7 39.9 17.2

54.3 28.9 17.7

59.8 33.8 6.4

41.6 38.0 20.5

48.2 35.4 16.4

For sky coverage, overcast applies to >90%, broken to 50-90%, and scattered to 10-50% cirrus cloud coverage.

Figure 2.5. Expanded polarization diversity lidar view of a cellular orographic cirrus layer (see plate 2.8) studied at PARS.


27

Figure 2.6. Comparison of monthly-averaged PARS cirrus cloud base and top heights (circles), compared with Salt Lake City tropopause heights averaged for all days and those in the cirrus sample (open and solid squares).

categories are analogous to those in table 2.1, except a combined very thin to subvisual group (with i < -0.05) is used. In this sample the opaque and thin-tovery-thin cloud categories are about equally divided. This is a significant finding, because if some satellite measurements, for example, cannot detect "thin" cirrus with i < 0.3 (Minnis 1998), then one-half of regional cirrus clouds would go undetected. This is obviously an area that deserves more careful study, particularly in view of the enhanced capabilities of the next generation of satellite probes. However, it is not the mean yearly or even seasonal properties that provide the most important information defining cirrus, but rather their variability on shorter time scales: the PARS high cloud data set is of sufficient size to permit the monthly variability in cirrus properties to be understood. Figure 2.6 contrasts the mean monthly values for cirrus cloud-base and cloud-top heights with the Salt Lake City sounding tropopause heights derived from a 10-year average and the same sample of cirrus days. The variability in monthly cirrus cloud top and base heights and temperatures (for the envelope data set) is shown in figures 2.7 and 2.8, respectively. Clearly, the accumulation of similarly comprehensive information from a number of surface sites would be highly useful in the context of testing and providing inputs for modern climate models.

28

Cirrus

Figure 2.7. Contoured monthly frequencies in percent of PARS cirrus cloud (a) top and (b) base heights, based on 0.5-km height intervals.

2.4.3. Cirrus and Weather over the Eastern Great Basin It is obvious that considerable seasonality exists in the PARS high cloud data set. The seasonal dependence on cirrus frequency and characteristics is clearly a reflection of the basic synoptic patterns that control the weather of the Great Basin. There is a strong correlation between the relative frequency of occurrence of PARS cirrus cloud observations (in percent of monthly to total observations), Salt Lake City National Weather Service reports of high cloud amounts (in terms of cirrus coverage-weighted amounts), and monthly Salt Lake City precipitation as shown in fig. 2.9. In other words, the synoptic weather patterns responsible for rain and snow are naturally also the harbingers for the cirrus cloud systems that sweep across the Great Basin. The crucial consequence is that cirrus properties will vary with season as the dominant weather patterns shift, as is well illustrated here for our particular geographic location. The PARS data compiled in table 2.3 and figures 2.6-2.9 exhibit a strong seasonal influence. A principal division in cirrus properties stems from the


29

Figure 2.8. Contoured monthly frequencies in percent of PARS cirrus cloud (a) top and (b) base temperatures, based on 2.5°C intervals.

distinct upper tropospheric circulations associated with advecting baroclinic systems versus the relatively quiescent summer monsoonal period, which draws Gulf and Pacific moisture through the desert Southwest northward into the Great Basin (Davis and Walker 1992; Adams and Comrie 1997). Thus, the distinction is mainly one of anvil-generated cirrus versus those generated by synoptic scale disturbances, although thunderstorms occur locally without monsoonal flow and local factors such as orography may not directly reflect the action of storm systems. A breakdown of observed PARS cirrus generation according to synoptic disturbances yields 24% in split jet flow/cutoff lows, 23% in zonal flow, and 6% under troughs (Campbell 1997). (Recall that PARS cirrus observations are confined to nonprecipitating conditions.) A total of 47% of the cirrus occurs under ridges, including 10% in flat ridges representative of deamplified upper level flow, typical of summer conditions. In other words, nearly one-half of the PARS cirrus clouds occur under conditions not normally associated with active cirrus cloud formation: these cirrus are simply advected

30

Cirrus

Figure 2.9. Contrasting 10-year monthly-averaged Salt Lake City NWS high cloud reports (black bars, weighted by cloud amount), PARS observation hours (by percent of total), and precipitation amounts for SLC. into our region. The majority of our cirrus are associated with maritime Pacific storm systems along the west coast of the United States, although subtropical moisture sources are often tapped in early spring and late fall. Occasionally, cirrus from subtropical jet streams and decayed Pacific hurricanes are also observed. The distinguishing summer cirrus properties result from the fact that the regional or local monsoonal thunderstorms tend to be rather weak because of their high cloud bases in the relatively dry desert environment. It is clear that average PARS cirrus cloud-top altitudes fall far below the tropopause height during the summer period (fig. 2.6). Thus, the average cloud-top heights above sea level are not noticeably dependent on season. In contrast to the relatively strong northwesterly flow encountered during most of the year when jet streams are typically nearby, table 2.3 shows that the monsoon brings weaker and more southerly flow. It is clear that during the summer months the cirrus layers are physically thinner but more opaque and less widespread (table 2.3), all attributes of cirrus derived from thunderstorms. In other words, the anvil cirrus clouds are distinct. It is appropriate to stress here that the basic connection between cirrus and weather means that other regions of the globe will likely have different cirrus cloud characteristics because of the local prevalence of weather patterns: cirrus cloud properties are fundamentally dependent on season and geographic location.


3I

2.5. The Altostratus Transition During the development of synoptic weather disturbances, the classic gradual thickening in cloud cover from cirrostratus to altostratus to nimbostratus often occurs. As already mentioned, the cirrostratus-altostratus transition, which is actually observed in association with a variety of weather situations, is identified operationally by the loss of the sharpness of the solar or lunar disk due to attenuation effects in the cloud. In this section I provide a recent comprehensive PARS case study analysis that illustrates the nature of the cirrostratus-altostratus transition. Plate 2.9 provides PDL height-time displays of relative 0.532 jim backscattering and linear depolarization over the 2040-2300 UTC period of the transformation of a cirrostratus into altostratus (see the fisheye photograph in plate 2.1g). Note that the approximately 1-km deep, weakly scattering aerosol layer just below the cloud represents the remnants of a dust storm advected over the north Pacific Ocean from the Gobi Desert: the low 0.5-5, the asymmetry of the flow is increasingly important, and flat crystals tend to orient with their plane horizontal, stabilized by attached rear eddies >20. For larger crystals and Reynolds numbers, the attached eddies are shed, and particles oscillate and fall along a helical path. Orientation sometimes occurs for smaller particles and Reynolds numbers with suitable geometry. The most striking example is for pine pollen

62

Cirrus

grains (diameter, 106um), with low density "ears," which are seen to be oriented by the ellipticity of the halo produced by diffraction around the sun (Parviainen et al. 1994). One may speculate that the ears serve to reduce the fall speed, thus giving the grains a greater dispersal range, the orientation effect being incidental—unless it helps entry into the appropriate pollination site in the flower. In this case the Brownian rotation angle is 0.05°, insufficient to remove the effect. Thus, particle orientation under terminal fall velocity results from the direct effect of flow, related to Reynolds number, and the effect of gradient of density and shape of the particle. The latter effect is more important for smaller particles and gives orientation when conventional wisdom would dictate otherwise (fig. 3.8). The results obtained from pollen grains are of particular interest because nature so contrives through the genetic code to give a low variability of size and shape, in contrast to situations for cloud droplets and particularly ice crystals in different stages of growth. Similar orientation effects may occur for ice as edge growth out of the plane of a dendrite or plate on one side, as might occur from an accreted droplet with changed crystallographic orientation, and may serve a similar purpose. A question arises as to when random orientation is produced, as is often assumed in numerical calculations of halo light intensity and radiation transfer (Greenler 1980). Clearly, Brownian rotational motion of smaller particles should produce such an effect, as also could a region of mixing leading to turbulent decay. With a viscous limit of a few millimeters, ice particles could be subject to these effects. It is unclear how much of the atmosphere where cirrus forms could be so influenced. A perfectly symmetrical 22° halo often forms in

Figure 3.8. Flow around falling plates and columns (a,c,d) characterized by terminal fall speed Reynolds number (Re). Horizontal orientation of the particles at Re 20-200 results from asymmetry of the flow and lee eddy effects, (b) A pollen grain falling at a much lower Re oriented by low-density "mouse ears" giving addition edge drag; asymmetry in ice crystals may have a similar orienting effect at low Reynolds numbers.

Ice Crystals in Cirrus

63

apparently quiescent conditions in the region of thin cirrus well ahead of a frontal system—why? Of interest is the fraction of crystals that need to be oriented to provide a specular reflection for a vertically pointing lidar. It is clear that many samples of ice in a region of several cubic meters from which a lidar pulse is scattered may contain a fraction of flat platelets whose density distribution (or size) leads to horizontal orientation to a fraction of a degree on fall. With crystal concentrations on the order of hundreds per liter, only a small fraction (a few percent) need to be oriented so such a phenomenon is a fairly frequent event and is indicative of such a size and shape spread. Estimating terminal velocity to give particle and mass vertical flux is complicated. Empirical relations between mass, dimension, and crystal form are commonly used. Considerations of measurements in figure 3.5 suggest that measurements of crystals under one set of growth conditions are unlikely to be transferable to other conditions, particularly if a significant change of pressure and hence vapor diffusivity is involved (equation 5). An approach to this problem has been made using a similarity argument (Mitchell 1996). An alternative approach is to use a laboratory-measured habit and inferred density as a variant of figure 3.5 together with estimated or measured drag coefficients for observed crystal shapes. A simplistic approach for a constant shape and drag coefficient is to assume a fall velocity (U) related to particle density (p() and air density (pa): where Cd - drag coefficient and a and p are shape factors. For variable density with radius, an integration may be necessary. Application of the above approach depends on further laboratory work. It also depends critically on measurement techniques available from aircraft to provide meaningful statistics of the larger particles together with direct measurements of properties used in the above analysis. 3.6. Radiative Properties of Cirrus Ice

The optical properties of ice crystals are determined in part by the refractive index of bulk ice, and in part by the ice crystal shape and size. One of the most frequently used tabulations of the refractive index for ice is that due to sources quoted in Warren (1984). Deviations from the Warren tabulation were found by Toon et al. (1994). These deviations were evaluated by Clapp et al. (1995) and were found to be related to the temperature at which the refractive index measurements were obtained. What are the consequences of imperfect knowledge of the refractive index of ice and water, especially in the thermal infrared? To partially evaluate this question, consider various literature measurements of the refractive index of water, as shown in figure 3.9. The real and imaginary parts are shown. Note especially the uncertainty in the refractive index between 3500 and 4000 cm'1. This variation in refractive index can lead to variations in the modeled infrared forcing and remote sensing of clouds. For example, figure 3.10 shows true and retrieved size distributions using the various refractive indices of figure 3.9, and the DW

64

Cirrus

Figure 3.9. Real and imaginary refractive index of water from various measurements (DW Downing and Williams 1975; HQ Hale and Querry 1973; WWQ Wieliczka et al. 1989).

(Downing and Williams) refractive index is taken to be exact. Figure 3.10a shows first that the retrieval algorithm is reasonable in that the DW refractive index retrieves the true size distribution; second, note the strong deviation of the WWQ (Wieliczka, Weng, and Querry) retrieval from the true distribution. Yet, the obtained optical depths shown in figure 3.10b are in reasonable agreement. Errors in refractive index can, unfortunately, be translated into errors of retrieved particle information, even though the obtained optical depth is quite satisfactory. This example is from Liu et al. (1999). Though cirrus clouds generally have a warming influence on surface temperature, the presence of small ( at the reference height R0 has to be estimated, in addition to the lidar ratio SP. Fortunately, in the case of cirrus profiling, the influence of this boundary value on the solution of equation 5 is small because most of the time air is very clean in the free troposphere above 5 km. (3P(,R0) -50°C.The blackbody spectral radiance at each level in the cloud is obtained from a radiometer calibration curve and radiosonde temperature data. 10.1.4. Water Vapor Absorption and Emission The chief absorber in the clear atmosphere at the radiometer filter wavelengths is water vapor with small contributions from carbon dioxide, ozone and aerosols. In a tropical atmosphere, with 4-6 cm of precipitable water, transmittance between a cirrus cloud and the ground can be 2000). The spatial resolution of this instrument depends on its position and is about 2 km at the earth's surface below the ER-2 aircraft at 20km. Smith et al. (1995) showed that sufficient information on cirrus clouds exists in the HIS spectrum and suggested that the cirrus cloud structure and composition may be inferred. Further, Smith et al. (1998) displayed an interesting spectrum for a case involving a cold cirrus that was particularly evident in the 800-1000 cm'1 window region. Due to two fundamental obstacles, we have not been able to successfully carry out spectral radiative transfer simulations in the thermal infrared for cirrus cloudy conditions. First, the scattering and absorption properties of nonspherical ice particles are largely unknown. Takano et al. (1992) used the solution for spheroidal particles and showed that a better interpretation can be made for measurements made in a number of window wavelengths in terms of the brightness temperature difference correlation, as compared with Lorenz-Mie results for spheres. As discussed earlier, when the ice crystal size parameters are smaller than about 20, it is physically incorrect to use the geometric ray-tracing approach for light-scattering calculations. With the availability of the FDTD method for small particles, the morphology of ice crystals can be accounted for in the analysis. Second, to effectively incorporate the multiple scattering process involving ice particles in an atmosphere where absorption of various greenhouse gases dominates requires innovative approaches. With the advance of the CKD method described above, we are now in a position to conduct physically based and efficient radiative transfer calculations to interpret the measured infrared spectra

Radiative Transfer in Cirrus Clouds

289

and to explore methodologies for the retrieval of cirrus cloud structure and compositions. In reference to the clear and cold cirrus spectra presented in Smith et al. (1998), we use a spectral interval of 1 cm'1 with 30 correlated absorption coefficients in which the adding/doubling method for radiative transfer is used to perform calculations in the spectral region from 800 to 970 cm'1 in which we can neglect overlaps between H2O and CO2 lines and H2O and O3 lines for wave numbers smaller and larger than these two, respectively. Moreover, we incorporate the water vapor continuum parameterization developed by Clough et al. (1989) in the calculations. We define the cloud radiative forcing as follows: where BT denotes the brightness temperature. Using the slope of the spectrum and the mean value as shown in figure 13.13, we have developed a preliminary approach to infer the ice crystal size and cloud optical depth. The left-hand side of figure 13.13 displays the measured AB and the computed results of AB for three mean effective ice crystal size of 42, 12, and 5 urn. The cloud-base height and thickness are fixed at 10.53 and 0.915km, respectively, based on lidar observations in this case. The slope of the AB spectrum is shown to be a function of the mean effective ice crystal size. It is clear that the AB for the 12 urn mean size follows the observed AB most closely. The optical depth is not known a

Figure 13.13. HIS data measured during the SUCCESS experiment, April 21,1996, from 800 to 970cm'1 (Smith et al. 1998) in terms of the cloud radiative forcing, defined as the difference between the clear and cloudy brightness temperatures. The theoretical results in the diagram on the left are shown for three mean effective ice crystal sizes. The diagram on the right displays the averaged value as a function of optical depth (see text for further explanation).

290

Cirrus

priori. To infer the optical depth we must construct a number of averaged AB values from the spectra as functions of the optical depth. The intercept from the measurements represented by the horizontal bar gives the best value of 1.14. Further analyses of the HIS data for cirrus and physical interpretations are needed to develop a physically based retrieval algorithm; the successful one would require appropriate validations using the co-located measurements of ice microphysics. 13.4. Summary

In this chapter, we have discussed and reviewed a number of fundamental issues concerned with the transfer of solar and thermal infrared radiation in cirrus clouds. These include light scattering and absorption by nonspherical ice crystals, radiative transfer in cirrus containing oriented ice crystals, and radiative transfer in finite and inhomogeneous clouds. The subject of light scattering by nonspherical ice crystals was briefly overviewed and the method of geometric ray tracing was introduced. Limitations of the geometic optics approach which requires the localization of light rays were discussed. This approach after modification that involves the exact mapping of the electric field at the near field to the far field can produce acceptable accuracies for size parameters greater than about 15-20. After researching an appropriate method to complement the geometric optics approach, we found that the finite-difference time domain technique for the numerical solution of light scattering by small nonspherical and inhomogeneous particles appears to be attractive from the standpoint of accuracy requirements and efficiency for size parameters less than about 20. Thus, by combining the aforementioned two methods, referred to as the unified theory for light scattering by ice crystals, we can now undertake reliable computations for the single-scattering properties of ice crystals covering all sizes and shapes definable by mathematical or numerical means. Representative phase function, linear polarization, and single-scattering properties for various ice crystal sizes and shapes and a cirrus cloud model were presented within the context of the information content of ice crystals. On the subject of radiative transfer, we reviewed the fundamentals associated with the orientation properties of ice particles due to the nature of nonsphericity. We outlined a formulation for the transfer of both solar and infrared radiation in terms of the Stokes vector in horizontally oriented ice crystals. In this case, the single-scattering properties of these types of ice particles are dependent on the direction of the incoming light beam, and the full four-by-four phase matrix is required in the discussion of the transfer process. A simple modification leads to the condition for random orientation in which only six independent phasefunction elements are needed. An example of bidirectional reflectances for randomly and horizontally oriented columns was presented to demonstrate that the latter case exhibits more anisotropy in the reflected pattern, a feature that could be important for the interpretation of satellite measurements involving cirrus clouds. Moreover, interpretation of the polarization of sunlight reflected


291

from cirrus clouds measured from aircraft reveals noticeable information of the ice crystal shape and size. On the basis of satellite imageries and lidar backscattering returns, cirrus clouds appear inhomogeneous and finite in extent. Consequently, the issue of radiative transfer in inhomogeneous cirrus must be addressed and follow by assessing its relative importance in satellite applications and radiative transfer parameterizations. Although most approaches to this have used the Monte Carlo method in radiative transfer simulations, we have undertaken a more fundamental approach based on the successive order of scattering, which can account for special geometry and inhomogeneity. However, because of the computer resource requirements, particularly for applications to entire solar and infrared spectra, we have innovated a modified diffusion approximation for radiative transfer in which the 5 adjustment for the sharp diffraction peak in the phase function can be accounted for locally. We demonstrate a case study by generating an extinction coefficient field representing contrail cirrus and examine the reflectance differences between inhomogeneous and homogeneous conditions. Much work is needed in this area in order to achieve a comprehensive physical realization as to the implications to remote sensing and radiative heating/cooling rate calculations for climate models. Finally, we reviewed and presented the information content of the structure and compositions of cirrus clouds from the perspectives of theoretical radiative transfer simulations and available observations. Line-by-line equivalent radiative transfer calculations involving the gaseous absorption sorted by the correlated k-distribution method coupled by multiple scattering and absorption of ice particles were carried out for solar bands with respect to the optical depth and ice crystal size information. In particular, we found that the 1.4-u.m water vapor lines after rearrangement exhibit distinction features that can be used to retrieve cloud optical and microphysical properties. In this pursuit, we defined a term called the "cloud radiative forcing," the difference between the cloud and clear reflectances, and demonstrated that the ice crystal size, cloud position, and optical depth are displayed in various parts of the rearranged spectra and solar zenith angles. Moreover, we presented observational evidence of cirrus clouds in the spectrum of the ARES data covering 2 to 6.4 um, a transition of domination from solar reflection to thermal emission in which cirrus clouds generally have larger signals than clear conditions in the former, while the reverse is true for the latter. The information content of thin cirrus in the HIS spectrum from 800 to 970cm'1 was then introduced. We demonstrated that it is feasible to deduce the ice crystal size and infrared optical depth objectively from the spectrum by using its slope and mean value. It is clear in view of the preceding presentations that a number of issues associated with the role of cirrus clouds in remote sensing and in climate modeling are still unresolved and require in-depth and well-organized theoretical and observational investigations. Indeed, owing to the spatial and temporal variabilities of ice crystal sizes and shapes in cirrus clouds as well as their high location in the atmosphere, remote sensing of their microphysical and optical properties from space and determination of their radiative properties and parameterizations represent an unusual challenge in atmospheric sciences. A number of

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radiometer-spectrometers and polarimeters available for the aircraft platform mentioned above, as well as several proposed and planned satellite instruments, such as the Moderate Resolution Imaging Spectroradiometer (MODIS; King et al. 1992), the Multi-angle Imaging SpectroRadiometer (MISR; Diner et al. 1998), and the Earth Observing Scanning Polarimeter (EOSP; Hansen et al. 1995), will undoubtedly provide an excellent opportunity to meet this challenge.

Acknowledgments. Support of the research work contained in this paper includes National Science Foundation grant ATM-9907924, National Aeronautics and Space Administration grants NAG-5-9667 and NAG-5-7738, U.S. Air Force Office of Scientific Research grant F49620-98-1-0232, and Department of Energy grant DE-FG0398-ER62526. References Arnott, W.P., Y.Y. Dong, J. Hallett, and M.D. Poellot, 1994. Role of small ice crystals in radiative properties of cirrus: A case study, FIRE II, November 22,1991. /. Geophys. Res., 99,1371-1381. Asano, S., 1983. Transfer of solar radiation in optically anisotropic ice clouds. J. Meteor. Soc. Japan, 61,402-413. Baum B.A., T. Uttal, M. Poellot, T.P. Ackerman, J.M. Alvarez, J. Intrieri, D. O'C. Starr, J. Titlow, V. Tovinkere, and E. Clothiaux, 1995. Satellite remote sensing of multiple cloud layers. /. Atmos. Sci., 52,4210-4230. Born, M., and E. Wolf, 1975. Principles of Optics. Pergamon Press, New York. Cahalan, R., W. Ridgway, and W. Wiscombe, 1994. Independent pixel and Monte Carlo estimates of stratocumulus albedo. /. Atmos. Sci., 51, 3776-3790. Cai, Q.M., and K.N. Liou, 1982. Theory of polarized light scattering by hexagonal ice crystals. Appl. Opt., 21, 3569-3580. Chepfer, H., G. Brogniez, and Y. Fouquart, 1998. Cirrus cloud's microphysical properties deduced from POLDER observations. J. Quant. Spectrosc. Radial. Transfer, 60, 375390. Chylek, P., and J.S. Dobble, 1995. Radiative properties of finite inhomogeneous cirrus clouds: Monte Carlo simulation. /. Atmos. Sci., 52,3512-3522. Clough, S.A., F.X. Kneizys, and R.W. Davies, 1989. Line shape and the water vapor continuum. Atmos. Res., 23,229-241. Coffeen, D.L., 1979. Polarization and scattering characteristics in the atmosphere of Earth, Venus, and Jupiter. /. Opt. Soc. Am., 69,1051-1064. Coleman, R.F., and K.N. Liou, 1981. Light scattering by hexagonal ice crystals. /. Atmos. Sci., 38,1260-1271. Conrath, B.J., R.A. Hanel, V.G. Kunde, and C. Prabhakara, 1970. The infrared interferometer experiment on Nimbus 3. /. Geophys. Res., 75,5831-5857. Diner, D.J., et al. 1998. Multi-angle Imaging SpectroRadiometer (MISR) Instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens. 36, 10721087. Draine, B.T., and P.J. Flatau, 1994. Discrete-dipole approximation for scattering calculations. /. Opt. Soc. Am. All, 1491-1499. Frankel, D., K.N. Liou, S.C. Ou, D.P. Wylie, and W.P Menzel, 1997. Observations of cirrus cloud extent and their impacts to climate. In Proceedings of the Ninth Confer-


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ence on Atmospheric Radiation, Long Beach, CA. American Meteorological Society, Boston, MA, pp. 414-417. Fu, Q., and K.N. Liou, 1993. Parameterization of the radiative properties of cirrus clouds. /. Atmos. ScL, 50,2008-2025. Gao, B.-C, and Y.J. Kaufman, 1995. Selection of the 1.375-um MODIS channel for remote sensing of cirrus clouds and stratospheric aerosols from space. J. Atmos. Sci., 52, 4231-4237. Goody, R.M., and Y.L. Yung, 1989. Atmospheric Radiation: Theoretical Basis. Oxford University Press, New York. Greenler, R., 1980. Rainbows, Halos, and Glories. Cambridge University Press, Cambridge. Hansen J.E., and J.W. Hovenier, 1974. Interpretation of the polarization of Venus. /. Atmos. ScL, 31,1137-1160. Hansen J. et al. 1995. Low-cost long-term monitoring of global climate forcings and feedbacks, dim. Change, 31,247-271. Heymsfield, A.J., and L.M. Miloshevich, 1993. Overview of microphysics and state parameter measurements from FIRE-II. In Proceedings of the Conference on FIRE Cirrus Science Results 1993, Breckenridge, Colorado. National Aeronautics and Space Administration, Washington, DC, pp. 1-4. Huffman, P.J., and W.R. Thursby, Jr., 1969. Light scattering by ice crystals. /. Atmos. Sci., 26,1073-1077. Humphreys, W.J., 1954. Physics of the Air. Dover. New York. Jacobowitz, H., 1971. A method for computing transfer of solar radiation through clouds of hexagonal ice crystals. J. Quant. Spectrosc. Radial. Transfer, 11, 691-695. Jayaweera, K., and B.J. Mason, 1965. The behavior of freely falling cylinders and cones in a viscous fluid. /. Fluid Mech., 22,709-720. King, M.D., Y.J. Kaufman, W.P. Menzel, and D. Tanre, 1992. Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectroradiometer. IEEE Trans. Geosci. Remote Sens. 30,2-27. King, M.D., S.C., Tsay, S.E., Platnick, M., Wang, and K.N., Liou, 1997. Cloud retrieval algorithm for MODIS; Optical thickness, effective particle radius, and thermodynamic phase. MODIS Algorithm Theoretical Basis Document no. ATBD-MOD-05.MOD06Cloud Product. Goddard Space Flight Center, Greenbelt, Maryland. Kunde, V.G., B.J. Conrath, R.A. Hanel, W.C. Maguire, C. Prabhakara, and V.V. Solomonson, 1974. The Nimbus IV infrared spectroscopy experiment. 2. Comparison of observed and theoretical radiances from 425-1450cm'1. J. Geophys. Res., 79, 777784. Liou, K.N., 1972. Light scattering by ice clouds in the visible and infrared: A theoretical study. J. Atmos. ScL, 29, 524-536. Liou, K.N., 1980. An Introduction to Atmospheric Radiation. Academic Press, New York. Liou, K.N, 1986. Influence of cirrus clouds on weather and climate processes; A global perspective. Mon. Wea. Rev., 114,1167-1198. Liou, K.N., 1992. Radiation and Cloud Processes in the Atmosphere: Theory, Observation, and Modeling. Oxford University Press, New York. Liou, K.N, and R.H. Coleman, 1980. Light scattering by hexagonal columns and plates. In Light Scattering by Irregularly Shaped Particles (D.W. Schuerman, ed.). Plenum Press, New York, pp. 207-218. Liou K.N, and S.C. Ou, 1979. Infrared radiative transfer in finite cloud layers. /. Atmos. Sci., 36,1985-1996. Liou, K.N., S.C. Ou, and G. Koenig, 1990. An investigation on the climatic effect of contrail cirrus. In Air Traffic and the Environmental-Background, Tendencies and

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Potential Global Atmospheric Effects (U. Schumann, ed.). Springer-Verlag, Berlin, pp. 154-169. Liou, K.N., and N. Rao, 1996. Radiative transfer in cirrus clouds. Part IV: On cloud geometry, inhomogeneity, and absorption. /. Atmos. Sci., 53,3046-3065. Liou, K.N., and Y.Takano, 1994. Light scattering by nonspherical particles: Remote sensing and climatic implications. Atmos. Res., 31,271-298. Liou, K.N., P. Yang, Y.Takano, K. Sassen,T.P. Charlock, and W.P. Arnott, 1998. On the radiative properties of contrail cirrus. Geophys. Res. Lett, 25,1161-1164. Lynch, D.K., and W. Livingston, 1995. Color and Light in Nature. Cambridge University Press, Cambridge. Lyot, B., 1929. Recherches sur la polarisation de la lumiere des planetes et de quelques substances terrestres. Ann. Observ. Paris (Meudori), 8. [Available in English as NASA TTF-187,1964, National Aeronautics and Space Administration, Washington, DC.] Macke, A., 1993. Scattering of light by polyhedral ice crystals. Appl. Opt., 32,2780-2788. Macke, A., J. Mueller, and E. Rascke, 1996. Single scattering properties of atmospheric ice crystals./. Atmos. Sci., 53,2813-2825. Martin, P.G., 1974. Interstellar polarization from a medium with changing grain alignment. Astrophys. J., 187, 461-412. Minnis, P., K.N. Liou, and Y. Takano, 1993. Inference of cirrus cloud properties using satellite-observed visible and infrared radiances. Part I: Parameterization of radiance field. /. Atmos. Sci., 50,1279-1304. Mishchenko, M.I., 1991. Extinction and polarization of transmitted light by partially aligned nonspherical grains. Astrophys. J., 367,561-574. Mishchenko, M.I., W.B. Rossow, A. Macke, and A.A. Lacis, 1996a. Sensitivity of cirrus cloud albedo, bidirectional reflectance and optical thickness retrieval accuracy to ice particle shape. /. Geophys. Res. 101,16973-16985. Mishchenko, M.I., L.D. Travis, and D.W. Mackowski, 1996b. T-matrix computations of light scattering by nonspherical particles: A review. /. Quant. Spectrosc. Radiat. Transfer. 55, 535-575. Muinonen, K.,T. Nousiainen, P. Fast, K. Lumme, and J.I. Peltoniemi, 1996. Light scattering by Gaussian random particles: Ray optics approximation. /. Quant. Spectrosc. Radiat. Transfer, 55, 577-613. Ou, S.C., K.N. Liou, Y. Takano, NX. Rao, Q. Fu, A.J. Heymsfield, L.M. Miloshevich, B. Baum, and S.A. Kinne, 1995. Remote sounding of cirrus cloud optical depths and ice crystal sizes from AVHRR data: Verification using FIRE II IFO measurements. J. Atmos. Sci., 52,4143^158. Ou S.C., K.N. Liou, P. Yang, P. Rolland, T.R. Caudill, J. Lisowski, and B. Morrison, 1998. Airborne retrieval of cirrus cloud optical and microphysical properties using Airborne Remote Earth Sensing System 5.1-5.3 and 3.7-fim channel data./. Geophy. Res., 103,23231-23242. Perrin, F, 1942. Polarization of light scattered by isotropic opalescent media. /. Chem. Phys., 10,415^27. Platt, C.M.R., N.L. Abshire, and G.T. McNice, 1978. Some microphysical properties of an ice cloud from lidar observations of horizontally oriented crystals. /. Appl. Meteor., 17,1220-1224. Prabhakara, C, D.P. Kratz, J.-M. Yoo, G. Dalu, and A. Vernekar, 1993. Optically thin cirrus clouds: Radiative impact on the warm pool. /. Quant. Spectrosc. Radiat. Transfer, 49, 467-483. Rolland, P., and K.N. Liou, 1998. Remote sensing of optical and microphysical properties of cirrus clouds using MODIS channels. In Proceedings of the Cirrus Topical Meeting, Baltimore, Maryland. Optical Society of America, pp. 17-19.


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14

On Cirrus Modeling for General Circulation and Climate Models

HILDING SUNDQVIST

14.1 Cirrus Characteristics and Modeling Aspects Cirrus clouds are significant regulators of the earth's radiation budget. Cirrus generally have low ice water content, leading to partial transparency to radiation, and a variety of ice crystal types constitutes the cloud. As a consequence, cirrus have complex optical qualities, which are discussed in other chapters of this book. In this chapter, I discuss the appearance and behavior of the cirrus clouds per se and discuss approaches to include those features in numerical models by parameterization. The number of general circulation models (GCMs) containing physically based parameterizations of cloud processes with prognostic equations for water/ice content increased remarkably during the 1990s. Model simulations of the general circulation of the atmosphere have shown a pronounced sensitivity to modeled optical properties of cirrus (e.g., Ramanathan et al. 1983; Senior and Mitchell 1993; Mitchell 1994b; Fowler and Randall 1996a,b; Kristjansson et al. 1998). Most studies with GCMs and climate models have focused on features of radiation and energy budgets and the modulation of these budgets as a consequence of changes in cloudiness quality or other conditions. Much less attention has been paid to the characteristics and realism of the model cloudiness itself (e.g., Liou 1992). Only meager discussions are generally found on these topics from studies in this context. In most cases, zonally averaged and/or bird's-eyeview cloudiness are reported. The reason for this is the sparseness of observational data, which makes it difficult to conduct a detailed verification of the simulated cloud fields. Many papers on model experimentation on this topic do 297

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indeed contain statements that uncertainties in cloud behavior constitute a severe weakness of the simulations (Senior and Mitchell 1993; Mitchell 1994). It is also emphasized that substantial improvement in our understanding of the behavior of clouds (not least cirrus) is required for satisfactory confidence in simulations of different climate scenarios. The critical need for high-accuracy measurements of upper-tropospheric water vapor is emphasized for example, in a paper by Stephens et al. (1996) discussing satellite measurements of water vapor. Clouds also have an indirect effect on climatology because their appearance and disappearance (evaporation) modulate the distribution of water vapor in the atmosphere. This effect may be especially prominent in cirrus. At cirrus altitudes, the difference between the saturation humidity and the ice water content of cirrus is considerably smaller than the corresponding difference at lower levels with higher temperatures and warm clouds. Evaporation of cirrus in the upper troposphere may therefore change the relative humidity substantially in this region, so the cloud situation has an essential impact on the equilibrium moisture content there. Operational measurements of moisture at low temperatures have a relatively large uncertainty, and the humidity analyses used for operational numerical weather prediction are therefore strongly governed by the 6-h model predictions of humidity in the assimilation cycle. These analyses may then also constitute elements of the climate data record. Consequently, assumptions about the liquid-ice phase conditions and the model treatment of cirrus have a pronounced impact on our notion of the water content of the upper troposphere. This again emphasizes the need for studies that lead to an enhanced understanding of dynamic and microphysical processes of cirrus, and eventually to a reliable certainty in their simulation in GCMs and other models. It is likely that large portions of the extensive cirrus clouds over tropical and subtropical regions emanate from anvils at the top of convective clouds, which are condensate-rich sources of cirrus. The idea that the difference between the saturation humidity and the water content of cirrus is often relatively small leads to the inference that evaporation of merely a fraction of the cirrus cloud may be sufficient to saturate the environmental air. This may explain why cirrus can survive transport over long distances away from the convective sources. Hence, it is of great importance to understand the processes forming anvils. It is not just the complex optical qualities of cirrus that distinguish them from warm clouds, but also the microphysical features and processes of cirrus. The main mechanism for ice crystal growth is deposition. This in turn means that there is no direct mechanism for generation of precipitation, but the particles fall as they become too heavy to be suspended by the upwind. In this context we are naturally led to the question of the definition of a cloud. Here I define a cloud as consisting of particles whose fallspeed due to gravitation is smaller than the upwind that is suspending them. That is, matter so heavy that its net vertical velocity is downward is denned as precipitation. Applying this to cirrus means that the rate of precipitation from this type of cloud is tantamount to the rate of production of ice crystals with a size or weight causing a net downward velocity. For inclusion in GCMs, it is, in general, hardly feasible, and certainly not realistic, to treat the evolution of individual cloud crystals. Instead, the bulk amount of cloud

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ice has to be used as a dependent variable. It is pertinent to note that, irrespective of how a specific scheme of prognostic equations of the cirrus condensationcloud processes is designed, it is necessary to know, or adopt, the size distribution of the cloud particles. The ice crystals of cirrus appear in many different shapes as a function of the environmental state, such as temperature, humidity, and aerosol type and amount. These circumstances affect both the microphysical growth characteristics and the optical properties (Liou 1995). These aspects are treated in several chapters of this book. It is not the cirrus cloud alone, but also the precipitating ice crystal mass (virga) that determines the optical depth of the cirrus layers. An appropriate account of the evaporation of the precipitating mass is therefore required. There is not unanimous agreement on the definition of cirrus. Their particular quality is due to the low temperatures at which these clouds exist. Here I adopt the definition that cirrus consist solely of ice crystals and furthermore presume saturation with respect to ice and that the temperature is below Tcir = -38°C. An important question regarding cirrus is what density of ice crystals produce a detectable effect on radiation. This density (optical depth) limit, which is probably a function of size distribution and crystal habit, may be adopted as a useful definition of the existence of a cloud. This question of the density-radiation relationship is also of vital interest in verifying and validating model simulations of cirrus. Whether fractional cloud cover has to be considered is another question, closely related to the density-radiation question. The basic condition is determined by the resolution, ideally the three-dimensional one. It seems that the resolution used in GCM and climate models generally is so coarse that fractional cover has to be accounted for. In the following sections, I discuss more elaborately views and suggestions devoted to the deduction of a physically based parameterization of cirrus.

14.2. Parameterization of Cirrus

14.2.1. Basic Considerations To derive a scheme for model treatment of cirrus, I start from a relatively simplistic approach, which I can then broaden by gradually accounting for refined considerations. I also review some schemes that are currently applied in GCMs. It is pertinent to note that the present discussion deals with approaches suitable for use in GCMs. In cloud-resolving models (CRMs), the mode of approach may be somewhat different with respect to details. Studies with the aid of CRMs constitute important research efforts that may give vital insights into many detailed processes. Aspects of CRMs are mentioned here only when they are relevant for the present review. A cloud scheme presumes that at least cloud extension and ice water content are regarded as variables. Some of the basic questions in this context have been

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discussed in the previous section. There is still a crucial question specific for cirrus modeling: What conditions shall be applied to decide whether or not condensation can take place? To elaborate on the above points, I consider the thermodynamic equation and the tendency equations of water vapor, cloud ice content, and precipitating water content respectively, as follows

where Lvi is the latent heat of deposition, Aa is all tendencies except that due to release of latent heat, Q is the rate of release of latent heat or rate of production of condensate, EP is the rate of evaporation of precipitation, GP is the rate of generation of precipitation, m is the cloud ice water mixing ratio, raP is the precipitating (ice) water mixing ratio, VP is the fall velocity of bulk precipitating water mass, and p is the air density. The remaining quantities have their conventional meaning. I first suggest an approach that also demonstrates the main features connected with the deduction of a cirrus parameterization scheme. I review some different cirrus schemes, which are applied in GCMs. To make the budget relations clear, the tendency equation of precipitating matter is included. By definition, the rate of precipitation at a level z (downward flux of water mass) is mPpVP. Omitting the left-hand term and the first term on the right-hand side of equation 4 and integrating from a level z to the top of the cloud, where the flux is zero, we find

Hence, the rate of precipitation at an arbitrary level is obtained from the integral of GP from that level to the top. So, if we do not include a prognostic equation for the precipitating matter, the rate of precipitation is obtained from equation 5a; if rap is required (as it is for calculation of EP), we have to adopt a (typical) fall velocity of the precipitation in question and diagnostically calculate mP from

We reformulate equation 2 in terms of relative humidity, U, and saturationspecific humidity, qs, (i.e., q = Uqs), by applying the Clausius-Clapeyron relation and get


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where the last right-hand-side is inserted from equation 2.Then, eliminating dT/dt between equations 1 and 5, we obtain after rearrangement of the terms

where

and

For later reference, I introduce the notation Mq:

Equation 7 shows that condensation may occur without direct transport convergence of vapor, through temperature changes (AT < 0), which result from for example expansion or radiative flux divergence. A prominent term of equation 7 is qsdU/dt, which together with GP of equation 3, has to be formulated to close the system of equations 1-3 and 7. There are two circumstances under which dU/dt * 0. One appears in the beginning of the cirrus condensation during the existence of a supersaturation that is being relaxed toward an equilibrium value (near saturation). The other situation with a non-zero humidity tendency may generally be present when a fraction of the grid square (or box) is covered by the cloud; in that situation, only part of the grid square has a relative humidity that allows condensation, implying that the grid point value of U then may vary with time. I return to the q^dU/dt term but first discuss the formulation of the generation of precipitation, GP. 14.2.2. Microphysical Aspects As stated above, the cloud proper is the ice water mass that is carried by the upwind, w, against the gravitational mean fall speed, V, which is (disregarding the sign)

where N(D) is the size distribution of the cloud ice crystals, and M(D) is the mass of the crystal of size D. Strictly, the upper integration limit should be DP, the size beyond which the crystals are so heavy that they precipitate. But if N(D) is (essentially) made up of the cloud particles, then the contribution to the integral for D > Dp, is negligible. Then setting w - V in equation 8, the ice water mixing ratio, mci, of the cloud is obtained from

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provided that we know the integral in the numerator (i.e., the size distribution and the terminal fall speed of individual crystals). From other chapters of this book, I conclude that the integral on the right hand side of equation 9 is most likely a function of the ambient conditions, such as temperature, humidity, aerosol existence, and upwind. As we gain a confident insight into such relations, it will become possible to obtain a consistent relation between mci and, for instance, the equivalent radius, which is an important cloud optical property (Wyser 1998). Based on various observational data, several relations between mass and fall velocity have been formulated (e.g., Locatelli and Hobbs 1974). In their paper on ice-cloud parameterizing, Heymsfield and Donner (1990) use a corresponding relation, while Mitchell (1996) treated the terminal velocity of individual crystal types and their sizes. But those relations are applicable to the fall velocity of the precipitating part of the ice crystals and are not suitable to be applied in equation 9. In those relations, the mass is proportional to the fall velocity raised to a large power (exponent -5-10). When the fall velocity is deduced from the mass, this implies a pronounced sensitivity to small uncertainties in velocity (a percentage change in velocity becomes amplified by the factor exponent value in the corresponding change in mass). The mass-fall velocity relations given in Cotton and Anthes (1989), and based on the observational data of Hobbs et al. (1972), are valid for smaller crystal sizes and hence for cloud ice mass. Simplifying these latter relations by taking a representative number density, N, and fall velocity, V, of the cloud ice particles, we obtain a related ice water mixing ratio,

where the product k\k2 varies from about 5000kg~°5ms 1 for a cloud consisting of small partjcles to about 8000kg~°5m/s for a cloud having typically larger particles. Then, V is here replaced with w, which is obtained from the model calculations. With w varying between 0.1 m/s for small particles, and Im/s for large particles (e.g^in anvils), mci will have mixing ratio values between 5 x 10~5 and 2 x 10~3, for N ~ 5 x 104. This mci corresponds to the autoconversion threshold of cloud ice in Fowler et al. (1996). They point out that they have to use a lower value than recommended by Lin et al. (1983), because the GCM has a low resolution without consideration of fractional cloud cover. Considering md to be an amount toward which the cloud water content adjusts as a result of the microphysical processes, and presuming that we have a w-value available from the model calculations, a description of the release of precipitation is suggested as follows


303

Considering that the amount of ice water that is greater than rac( precipitates, the exponent cix should be given a value >1. The time scale that is represented by the inverse of kci reflects the time it takes to adjust toward mci through precipitation of "overweight" mass. The length of this adjustment time may be taken as the time it takes until the precipitating mass has been removed from the cirrus cloud layer due to the flux at the base of the cloud. That is,

where A/zc, is the thickness of the cirrus cloud layer. Expression 12 implies that kci ~ 2 x 10~3 to 5 x IQ'4. Fowler et al. (1996a) adopted a temperature-dependent coefficient, 10-3exp[0.25(r- 273)]. 14.2.3. Evaporation of Precipitating Ice Crystals The precipitating matter will be subject to evaporation when it falls through subsaturated strata. The rate of evaporation is described by the same relation as the depositional growth. For an individual particle, the evaporation is proportional to a representative size measure, C, the subsaturation, (S - Us - U), and a function, K, of vapor diffusion and thermal conductivity coefficients. Adopting an equivalent diameter for the size, and assuming an exponential size distribution, integration over the entire spectrum yields a relation for E? at a level z:

where equation 5b is introduced to give the last equality, hence VP is the average fall speed of the precipitation; kE is a rate coefficient to be determined, and P = 0.5. With a ventilation effect, which is size dependent, included in K, we get P = 0.65 (Kessler 1969). The unit of EP here is per second (mixing ratio per unit time). The coefficient kE contains the square root of the number concentration per unit length, dimension (nT4), and K.The latter is a pronounced function of temperature, so kEis about 15 times larger at 248 K than at 220 K. We may estimate how far the precipitation falls before it is completely evaporated. We consider

Integrating equation 14 over a fall distance, hE, required to evaporate the mp(cloud base), we find

Taking P = 0.5, kE = 10~5, VP ~ 1 m/s, Pbase ~ 4mm/day, and a subsaturation of 40% (U = 0.6), we obtain hE = 2300m.

304

Cirrus

14.2.4. Macrophysical and Meso-scale Aspects As concluded above, a formulation of the qsdU/dt term in equation 7 is needed to close our system. The problem is closely connected with processes of both microphysical scales and larger scales. There are two basic possibilities for condensation to take place. One is that the supersaturation is high enough for, what is termed homogeneous (Heymsfield and Miloshevich 1995), condensation to occur. The other possibility is that small ice crystals, advected or falling into a volume of air, act as freezing nuclei if the humidity is at, or above saturation. In the case of homogeneous condensation, Heymsfield and Miloshevich (1995) found a temperature-dependent threshold value of relative humidity, t/hom. For the second alternative, it is necessary to establish at what concentration the freezing nuclei become effective initiators of cirrus formation. Zurovac-Jevtic (1999a) reported that the resulting model cirrus situation is sensitive to the chosen lower limit in this context. To account for the Brownian-dif fusion contact nucleation process, Lohmann and Roeckner (1996) related the droplet number concentration to sulfate aerosol mass, which is empirically different over oceans and land as well as over the two hemispheres. Whether condensation commences due to one or the other of the two alternatives, it is necessary to state how a supersaturation is relaxed toward an equilibrium (near saturation) value. Assume an exponential adjustment of the relative humidity to saturation,

where Us = 1 is the relative humidity at saturation, T is a relaxation time, which we take to be on the order of a fraction of 1 h, and conceivably T is a function of current type and number density of aerosol (freezing nuclei). The above discussion applies to an in-cloud situation (i.e., 100% cloud cover). The matter of fractional cloudiness adds considerable complexity to the parameterization of condensation-cloud processes. As noted above, we need to find (empirically) a guiding resolution at which it is reasonable to assume that the cloud fully fills a (grid-box) volume of air. Fractional cloudiness means that only part of an air volume is saturated. Consequently, it is then necessary to describe how the available humidity, Mq (equation 7a) is partitioned between the condensation proper on one hand, and a general humidity change in the cloud-free part of the volume on the other (Sundqvist 1978,1993). So far, there are no physically based parameterizations derived for fractional cloudiness and rate of change of relative humidity in partially cloud-free air volumes. These questions appear to be the most difficult ones in this context. Strictly, fractional cloud cover is a diagnostic, nonphysical quantity, but Tiedtke (1993) and Rasch and Kristjansson (1998) introduced an intriguing application of a prognostic treatment of fractional cover. Wilson and Ballard (1998) also considered the matter of fractional cover. For the time being, the best we can do is to resort to empirical or statistical approaches. It is not unusual to find reports from field measurements that report strong spatial variations of cloud ice content on relatively short scales. It appears


305

natural to apply the principle introduced by Sommeria and Deardorff (1977), which is based on a statistical distribution of points where condensation occurs inside a given volume. In the case of anvils, the situation probably is markedly different from cirrus formed through large-scale lifting and similar circumstances. The pronounced convergence of vapor at the top of convective cells also brings suitable freezing nuclei, implying that condensation can take place without particular supersaturation, provided the temperature is below TciT. Convective clouds and anvils are likely to be of subgrid scale. As a consequence, anvils may form even if the gridbox relative humidity is below saturation. Assuming that the fractional cover is calculated separately, a plausible requirement on the relative humidity for condensation to take place is that the environmental humidity together with the moisture brought up during a time step in that fractional area of the grid square be greater than the saturation value. This may then be expressed as follows

where b is the cloud cover and q0 is the environmental specific humidity in the cloud-free area, and Mq is defined in equation 7a. The grid square specific humidity when condensation takes place in a fraction b is

Eliminating q0 between equations 17a and 17b and expressing the condition in terms of relative humidity, we get

In summary, the above derivation then consists of the following closed set of equations (omitting the precipitating mass as a prognostic variable): the prognostic equations 1-3; the consistency relation (equation 7); and the parametric relations (equations 10-11,13,16 and 17c or a corresponding relation). In deducing those relations, the crucial quantities for cirrus parameterization are also identified. Those quantities are size distribution, fall velocities of particles, and particle number density. These are primarily temperature dependent. The quantities are components of the above approach, for which some (typical?) averages have been adopted. The scheme has the potential for refinements. As we gain more insight into the microphysical relations, the ice water mixing ratio, m, may be divided into different categories of particle shapes, whereby it becomes necessary to describe transfer processes between the categories. It is here pertinent to observe studies by Mitchell (1994a), Mitchell et al. (1996), and Khvorostyanov and Sassen (1998), for example, which aim at an improved understanding of size distributions, fall velocities, and ice water contents. Wyser (1998) and Wyser and Yang (1998) suggest microphysics parameterizations that are consistent both with regard to cloud physics and optical qualities.

306

Cirrus

14.3. Modeling with Specific Parameterization of Cirrus

As stated above, there are a few NWP models and GCMs that treat an ice phase of hydrometeors in their condensation cloud schemes. Examples of such approaches are found in Senior and Mitchell (1993), Del Genio et al. (1996), Fowler and Randall (1996b), Fowler et al. (1996), Lohmann and Roeckner (1996), Rotstayn (1997), Rasch and Kristjansson (1998), Wilson and Ballard (1998), and Zurovac-Jevtic (1999a,b). Heymsfield and Donner (1990) presented the first specific ice cloud parameterizations intended for use in large-scale models. Some of the approaches have a specific treatment of cirrus clouds and/or pay particular attention to the model cloud behavior (Heymsfield and Donner 1990; Del Genio et al. 1996; Fowler et al. 1996b; Wilson and Ballard 1998; Zurovac-Jevtic 1999a,b), whereas others concentrate on the optical properties of the ice crystal clouds and associated effects (Senior and Mitchell 1993; Kristjansson et al. 1998). The approach suggested by Heymsfield and Donner (1990) is simplistic and straightforward. They demonstrate the characteristics of the scheme with the aid of theoretical calculations and comparisons with FIRE data (Cox et al. 1987). The scheme is based on essentially the model vertical velocity, from which the amount of condensate produced during lifting is calculated under the assumption that freezing nuclei are abundant. The rate of removal of ice mass due to precipitation is obtained from a bulk terminal velocity, which is a function of the ice mixing ratio. An elaborate investigation of sublimation and survival distances of ice particles in subsaturated environments is included. The approach suggested in section 14.2 has not yet been tested in a threedimensional model. A few tests have been carried out in a one-dimensional version. In a standard atmosphere with a vertical velocity forcing between 300mb and 200mb with maximum speed of 15cm/s, the resulting cirrus ice water path is 0.002 kg/m2 and the total ice water path (including the precipitating ice) is 0.006 kg/m2; the maximum ice water content is about 3 x 10~3 g/kg. In a case with a deep convective cloud with the anvil top at about 12.5 km altitude, T= -65°C and maximum vertical velocity in the anvil of about l.Sm/s1, the result is a cirrus ice water path of 0.35 kg/m2 and a total ice water path of 1.1 kg/m2; the maximum ice water content is about 0.4 g/kg. In their scheme for cloud microphysical processes, Fowler et al. (1996a) used prognostic variables for cloud liquid water, cloud ice, rain, and snow. It is assumed that sufficient nuclei always are present to initiate condensation and deposition. The sinks of cloud ice (i.e., the rate of generation of precipitation) are autoconversion and collection of ice by snow. The former mechanism is similar to expression 11, but with a fixed value on the threshold parameter, and as indicated a temperature dependent coefficient. It is important to note that the scheme includes a spreading of cirrus from detraining convective cells. In a subsequent paper, Fowler et al. (1996b) carried out comprehensive analyses of the simulated hydrometeor fields and an evaluation of the sensitivity to the assumptions of the scheme. Among other things, they found pronounced sensitivity to the value of the autoconversion threshold, which emphasizes the key role of size distribution and fall velocity as they govern this threshold value. A great deal may be learned


307

from this type of well-planned model experiment, but at the same time it is an important reminder of the great and urgent need for adequate observational data, to which the model results eventually must be compared. Zurovac-Jevtic (1999a) focused on the cirrus parameterization part of an overall condensation-cloud scheme. The cirrus ice water content is diagnosed from time step to time step, under the assumption that the cloud ice amount has a relatively short time of adjustment to the ambient conditions. Hence, the tendency of m is available in this quasi-equilibrium assumption. The release of latent heat is given by expression 7, so to guarantee a balanced water budget, the rate of release of precipitation is obtained by solving for GP in equation 3. The approach is included in a high-resolution numerical weather prediction model and has been applied to a mid-latitude situation and to a tropical situation (Zurovac-Jevtic, 1999b), in both cases with some observational data for comparison. In the present form, it appears that the scheme does not sufficiently account for the temperature dependence of derived parameters because the relation between temperature and cloud ice amount, indicated in observational data (Heymsfield and Platt 1984), is not clearly simulated. Zurovac-Jevtic (1998b) found an important sensitivity to assumed size distributions in the scheme. 14.4. Verification Aspects

In efforts to develop cirrus parameterizations for GCM, it is my opinion that results from model integrations should be analyzed in the synoptic time scale— not as statistical features—over extended time periods to account for all relevant circulation conditions that may be expected to occur. To obtain a revealing and constructive evaluation of the performance of a cloud scheme, it is absolutely necessary that observational cirrus data are available. The above discussions have shown that, in addition to ice water content, cloud cover, temperature, humidity, such data should contain information on ice crystal habits, size distributions, fall velocities, and survival distances (depth of virga). The chapters of this book show that there are useful data sets available from field campaigns. Those data have their greatest value in connection with the derivation of parameterization schemes and first-test applications in full-fledged model integrations. It appears difficult to detect, catch, and measure small ice particles. This means that ice crystal size distributions suffer from uncertainties, which severely affect the derivation of the cloud characteristics. Enhanced certainty in this respect would also suggest what lower limit of water content should be used to define a cirrus cloud for modeling and verification purposes. To reliably verify and validate model simulations, data covering large areas are required. Such coverage is provided only by satellite measurements. The great coverage is obtained at the expense of the details and three-dimensionality of field measurements. Retrievals of cirrus cloud parameters, especially, still suffer from uncertainties and inaccuracies, so it is essential that studies be encouraged and supported to achieve improvements in this respect. In conclusion, a good deal of useful data already exist for use in connection with numerical weather prediction and GCM experiments. It is important that

308

Cirrus

we find ways to promote the much needed improvement of information and cooperation between the measuring and observing community and the modeling community. Then the former may be given a better insight into the modeler's request for observation data, and the latter will learn about potentials and limitations in measurements and observations. References Cotton, W.R., and R.A. Anthes, 1989. Storm and Cloud Dynamics. Academic Press, New York, Cox, S.K., D. McDougal, D.A. Randall, and R.A. Schiffer, 1987. FIRE-The First ISCCP Regional Experiment. Bull Amer. Meteor. Soc., 68,114-118. Del Genio, A.D., M-S. Yao, W. Kovari, and K.K.-W, Lo, 1996. A prognostic cloud water parameterization for global climate models. / Climate, 9,270-304. Fowler, L.D., and D.A. Randall, 1996a. Liquid and ice cloud microphysics in the CSU General Circulation Model. Part II: Impact on cloudiness, the earth's radiation budget, and the general circulation of the atmosphere. /. Climate, 9,530-560. Fowler, L.D., and D.A. Randall, 1996b. Liquid and ice cloud microphysics in the CSU General Circulation Model. Part III: Sensitivity to modeling assumptions. /. Climate, 9,561-586. Fowler, L.D., D.A. Randall, and S.A. Rutledge, 1996. Liquid and ice cloud microphysics in the CSU General Circulation Model. Part I: Model description and simulated microphysical processes. /. Climate, 9,489-529. Heymsfield, A.J., and L.J. Donner, 1990. A scheme for parameterizing ice-cloud water content in general circulation models. /. Atmos. Sci., 47,1865-1877. Heymsfield, A.J., and L.M. Miloshevich, 1995. Relative humidity and temperature influence on cirrus formation and evolution: observations from wave clouds in FIRE II. /. Atmos. Sci., 52,4302-4326. Heymsfield, A.J., and C.M.R. Platt, 1984. A parameterization of the particle size spectrum of ice clouds in terms of the ambient temperature and ice water content. /. Atmos. Sci., 41,846-855. Hobbs, P.V., L.F. Radke, A.B. Fraser, J.D. Locatelli, C.E. Robertson, D.G. Atkinson, R.J. Farber, R.R. Weiss, and R.C. Easter, 1972. Field observations and theoretical studies of clouds and precipitation over the Cascade Mountains and their modifications by artificial seeding (1971-72). Research Report VII. Department of Atmospheric Science, University of Washington, Seattle. Kessler, E., 1969. On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteorological Monographs 10. American Meteorological Society, Boston, MA. Khvorostyanov, V.I., and K. Sassen, 1998. Cirrus cloud simulation using explicit microphysics and radiation. Part I: Model description. /. Atmos. Sci., 55,1808-1821. Kristjansson, I.E., J.M. Edwards, and D.L. Mitchell, 1998, A new parameterization scheme for the optical properties of ice crystals for use in general circulation models of the atmosphere. Phy& Chem. Earth (in press). Lin, Y.-L., R,D. Farley, and H.D. Orville, 1983. Bulk parameterization of snow field in a cloud model. /. Climate Appl. Meteor., 22,1065-1092. Liou, K.N., 1992. Radiation and Cloud Processes in the Atmosphere. Oxford University Press, New York. Liou, K.N., 1995. Issues related to parameterization of the radiative properties of ice


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clouds in GCMs. In Workshop on Cloud Microphysics Parameterizations in Global Atmospheric Circulation Models. Kananaskis, Alberta, Canada, 23-25 May, 1995, WCRP-90. World Meteorological Organization, Geneva pp. 233-247. Locatelli, J.D., and P.V. Hobbs, 1974. Fall speeds and masses of solid precipitation particles. J. Geophys. Res., 79,2185-2197. Lohmann, U, and E. Roeckner, 1996. Design and performance of a new microphysics scheme developed for the ECHAM general circulation model. Climate Dynamics, 12, 557-572. Mitchell, D.L., 1994a. A model predicting the evolution of ice size spectra and radiative properties of cirrus clouds. Part I: Microphysics. /. Atmos. Sci., 51,797-816. Mitchell, D.L., 1996. Use of mass- and area-dimensional power laws for determining precipitation particle terminal velocities. /. Atmos. Sci., 53,1710-1723. Mitchell, D.L., S.K. Chai, Y. Liu, A.J. Heymsfield, and Y. Dong, 1996. Modeling cirrus clouds. Part I: Treatment of bimodal size spectra and case study analysis. /. Atmos. Sci., 53,2952-2966. Mitchell, J.F.B., 1994b. Modelling clouds in GCMs for climate change studies. In Report of International Workshop on Cloud- Radiation Interactions and Their Parameterization in Climate Models, Camp Springs, Maryland, 18-20 October 1993. WCRP-86. World Meteorological Organization, Geneva pp. 40-41. Ramanathan, V.E., EJ. Pitcher, R.C. Malone, and M.L. Blackmon, 1983. The response of a spectral general circulation model to refinements in radiative processes. /. Atmos. Sci., 40,605-630. Rasch, PI, and I.E. Kristjansson, 1998. A comparison of the CCM3 model climate usingdiagnosed and predicted condensate parameterizations. /. Climate, 11,1587-1614. Rotstayn, L.D., 1997. A physically based scheme for the treatment of clouds and precipitation in large-scale models. I: Description and evaluation of the microphysical processes. Quart. J. Roy. Meteor. Soc., 123,1227-1282. Senior, C.A., and J.F.B. Mitchell, 1993. Carbone dioxide and climate: The impact of cloud parameterization. /. Climate, 6,393-418. Sommeria, G., and J.W. Deardorff, 1977. Subgridscale condensation in models of nonprecipitating clouds. / Atmos. Sci., 34,344-355. Stephens, G.L., D.L. Jackson, and I. Wittmayer, 1996. Global observations of uppertropospheric water vapor derived from TOYS radiance data. J. Climate, 9, 305-326. Sundqvist, H., 1978. A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc., 104,677-690. Sundqvist, H., 1993. Parameterization of condensation and associated clouds in models for weather prediction and general circulation simulation. In Aerosol-Cloud-Climate Interactions (P.V. Hobbs, ed.). Academic Press, New York, pp. 175-203. Tiedtke, M., 1993. Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 3040-3061, Wilson, D.R. and S.P. Ballard, 1996. A microphysical based precipitation scheme for the UK Meteorological Office unified model. Quart. J. Roy. Meteor. Soc., 125,1607-1636. Wyser, K., 1998. The effective radius in ice clouds. /. Climate, 11,1793-1802. Wyser, K., and P. Yang, 1998. Average ice crystal size and bulk short-wave singlescattering properties in cirrus clouds. Atmos. Res., 49,315-335. Zurovac-Jevtic, D., 1999a. Development of a cirrus parameterization scheme: Performance studies in HIRLAM. Mon. Wea. Rev., 127,47(M85. Zurovac-Jevtic, Dance, 1999b. Cirrus modeling in the Tropics, chapter of Ph.D. Thesis, Dynamic modeling of cirrus cloud characteristics. Department of Meteorology, Stockholm University, Sweden.

15

GCM Simulations of Cirrus for Climate Studies A N T H O N Y D. DEL G E N I O

15.1. The Challenge of Modeling Cirrus Clouds

One of the great challenges in predicting the rate and geographical pattern of climate change is to faithfully represent the feedback effects of various cloud types that arise via different mechanisms in different parts of the atmosphere. Cirrus clouds are a particularly uncertain component of general circulation model (GCM) simulations of long-term climate change for a variety of reasons, as detailed below. First, cirrus encompass a wide range of optical thicknesses and altitudes. At one extreme are the thin tropopause cirrus that barely affect the short-wave albedo while radiating to space at very cold temperatures, producing a net positive effect on the planetary radiation balance and causing local upper troposphere warming, thus stabilizing the lapse rate. At the other extreme are thick cumulus anvil cirrus whose bases descend to the freezing level; these clouds produce significant but opposing short-wave and long-wave effects on the planetary energy balance while cooling the surface via their reflection of sunlight. In fact, satellite climatologies show a continuum of optical thicknesses between these two extremes (Rossow and Schiffer 1991). In a climate change, the net effect of cirrus might either be a positive or a negative feedback, depending on the sign and magnitude of the cloud cover change in each cloud-type category and the direction and extent of changes in their optical properties (see Stephens et al. 1990). Second, the dynamic processes that create cirrus are poorly resolved and different in different parts of the globe. In the tropics, small-scale convective trans310

GCM Simulations for Climate Studies

3II

port of water from the planetary boundary layer to the upper troposphere is the immediate source of a significant fraction of the condensate in mesoscale cirrus anvils (see Gamache and Houze 1983), and ultimately the source of much of the water vapor that condenses out in large-scale uplift to form thinner cirrus. However, many observed thin cirrus cannot directly be identified with a convective source, suggesting that in situ upper troposphere dynamics and regeneration processes within cirrus (see Starr and Cox 1985) are important. In mid-latitudes, although summertime continental convection is a source of cirrus, in general cirrus is associated with mesoscale frontal circulations in synoptic-scale baroclinic waves and jet streaks (see Starr and Wylie 1990; Mace et al. 1995). Third, prediction of cirrus formation depends on the accuracy of transports of small concentrations of water vapor to and within the upper troposphere. The Clausius-Clapeyron equation implies that upper troposphere water vapor concentrations are several orders of magnitude smaller than those in the lower troposphere. Global numerical models, especially climate models that typically have only 10-20 vertical layers, have difficulty resolving such vertical gradients of water vapor. Thus, even a small error in the upward water vapor transport by the "resolved" dynamics in such models can result in first-order errors in instantaneous upper-level humidity. As a result, global models often fluctuate between extremely dry and supersaturated conditions at high altitude, producing bimodal distributions of high-level cloud cover with peaks near 0 and 100% (see Slingo 1980). Fourth, the relative humidity at which cirrus clouds form varies depending on the nature and concentration of nucleating particles. At sufficiently cold temperatures ( 9.38 dominate the short-wave forcing in the tropics, while clouds thinner and thicker than i = 9.38 have comparable effects on outgoing long-wave radiation that dominate those from low and middle cloud types. Chen et al. (2000) used ISCCP data in combination with climatological information on cloud thicknesses, water vapor, and temperature in a radiative transfer model to estimate the effects of different cloud types on the top-of-the-atmosphere (TOA), within-atmosphere, and surface radiation budgets. Table 15.1, adapted from their work, illustrates that although thin, high cloud occurs over a larger area of the globe, it does not necessarily dominate the radiative impact of high clouds as a whole; in fact, the relative importance of different high-cloud types changes with the altitude at which one considers the energy budget. Thin, high cloud (0.02 < i < 3.55) is the only type that has a net positive TOA radiative effect. Moderate optical thickness (3.55 < T < 22.63) and optically thick (i > 22.63) high clouds have a negative TOA effect, with the latter dominating globally because of different short-wave and long-wave degrees of compensation between cover and albedo effects. (Moderate high-cloud coverage exceeds that of thick, high cloud, but thick, high clouds have higher albedo; in contrast, both moderate and thick, high clouds have near-unit emissivity, so long-wave warming


3 13

Table 15.1. Global radiative impact (W/m2) of different high-cloud types (defined by their visible optical thickness, see text) on the energy budget at different altitudes Top of atmosphere Short-wave Long-wave Thin (13%) Moderate (6%) Thick (3%)

-4.2 -7.9 -6.2

5.5 5.5 2.9

Atmosphere

Surface

Net Short-wave Long-wave Net Short-wave Long-wave 1.3 -2.4 -3.3

-0.6 -0.7 -0.4

4.4 3.8 2.2

3.8 3.1 1.8

-3.6 -7.2 -5.8

1.1 1.7 0.7

Net -2.5 -5.5 -5.1

Adapted from Chen et al. (2000). Global areal coverage is indicated next to each cloud type.

effects due to the greater cover of moderate high cloud dominate.) Within the atmosphere, all high-cloud types have a net warming impact because short-wave cloud absorption is small; thin cirrus have the largest effect because they have the greatest cloud cover. The surface effect of all high-cloud types is negative because albedo effects are similar to those at TOA, while long-wave effects at the surface are severely muted by the intervening water vapor (and to some extent by low cloud in less humid regions). Overall, the largest negative net surface effect is due to moderate high clouds, which have greater cover than thick clouds and higher albedo than thin, high clouds. It is not known whether cirrus clouds as a class combine to produce a negative or a positive feedback on long-term climate change. Existing climate models produce a bewildering variety of predictions (see Cess et al. 1996), considering that all models agree that in a warming climate, an enhanced hydrologic cycle will result in stronger, deeper upward transport of water by convection and largescale motions. Cess et al. show that in response to a prescribed ±2°C sea surface temperature (SST) perturbation, long-wave amplification factors (which are due almost entirely to cirrus) of direct radiative forcing range from 0.4 (strong negative feedback) to 1.8 (strong positive feedback) among recent versions of 16 GCMs. At one extreme is the National Center for Atmospheric Research Community Climate Model Version 2, which prescribes cirrus optical properties and predicts an overall decrease in cirrus with warming despite an upward shift in altitude; the former change dominates to produce negative long-wave feedback. At the other extreme is the Laboratoire de Meteorologie Dynamique GCM, which has variable cloud optical properties and simulates increases in both cirrus cover and emissivity with warming, giving positive long-wave cloud feedback. Short-wave contributions of cirrus are also variable, but are largely explainable according to whether a given GCM allows for variable optical properties and a convective source of cirrus anvil water. Those that do incorporate such physics generally predict a negative cirrus component of short-wave cloud feedback, while those that do not tend to simulate a positive cirrus short-wave feedback. The cirrus "wild card" in an actual climate change scenario is easily demonstrated with the Model IF version of the Goddard Institute for Space Studies (GISS) GCM (Del Genio et al. 1996; Yao and Del Genio 1999). The standard version of this model has a 3.1°C global sensitivity to a doubling of CO2 when coupled to a mixed layer ocean and run to equilibrium. A set of three sensitivity

314

Cirrus

experiments illustrates the range of uncertainty caused by our poor knowledge of cirrus and the processes that form them: 1. Typical of most GCMs, the GISS GCM underpredicts thin cirrus coverage (0.1-6% depending on latitude, as opposed to 5-20% observed by Stratospheric Aerosol and Gas Experiment II, according to Liao et al. 1995). A crude upper limit on the climatic role of the missing thin cirrus can be estimated by invoking the following artificial parameterization: Assume 100% thin (visible optical thickness T = 0.1) cirrus coverage at the tropopause in the current climate. If a doubling of CO2 causes thin cirrus T to increase fractionally by the same amount as the tropopause humidity (27%), then the resulting additional warming is 1.3°C, giving a global sensitivity of 4.4°C. 2. Thick convective anvil cirrus are created in part by the detrainment of condensate from cumulus updrafts. How much of this condensate detrains depends on the evolution of the drop size distribution in the updraft, the strength of the updraft itself, and the extent of entrainment of drier environmental air, all of which are beyond the scope of current parameterizations. The GISS GCM assumes that all vapor condensed in cumulus updrafts above the 550 rnb level detrains; when detrainment is excluded, the global sensitivity rises by 0.6°C, to 3.7°C. Allowing some of the condensate formed below the 550mb level to be transported upward and detrain would presumably lower the sensitivity of the model instead, since these clouds are already optically thick. 3. Since cirrus properties are sensitive to the dynamic processes that form them, the cirrus cloud feedback depends on uncertainties in the response of the largescale dynamics to climate forcing as much as it does on parameterization uncertainties. We conducted two pairs of fixed SST perturbation (±2°C) perpetual July experiments (which overemphasize tropical feedbacks relative to CO2 doubling simulations) with identical versions of the cloud parameterization: One in which the SST change is applied uniformly, and another in which the tropical East Pacific is allowed to warm or cool by >2°C, and the tropical West Pacific by 100km during the TOGA/COARE IOP detected with the Machado and Rossow (1993) algorithm (Ye 1999).

whereas intermediate optical thickness clusters range in size from 100km to almost 1000km. These properties are undoubtedly influenced by the extent of water transport by convective updrafts and eventual detrainment into the anvils themselves, but it is probably not possible to predict the strength of convective water transport in a GCM from first principles. Furthermore, there is an equally important source of anvil condensate from in situ mesoscale uplift and condensation. Unfortunately, the extent of detrainment versus local production of anvil ice varies widely depending on the nature of the convective system. Table 15.2 is a sampling of water budget estimates from observations and CRM simulations of convective events in different locations. Detrainment of condensate into anvils can be either an insignificant fraction of, or comparable to, the amount of precipitation that reaches the ground in different convective systems. Plausibly this is related to the Table 1 5.2. Some Observational and Modeling Estimates of Cumulus Detrainment, Normalized by Total Precipitation, in Tropical and Mid-latitude Convective Clusters Model, date, reference GATE, Sept. 12 (Gamache and Houze 1983) COPT81, May 27-28 (Roux and Ju 1990) OK PRE-STORM, June 10-11 (Callus and Johnson 1991) GATE, Sept. 12 CRM (Ferrier et al. 1996) COHMEX, June 29 CRM (Ferrier et al. 1996)

Detrained condensate 0.42-0.61 0.14 0.26 0.63-0.73 0.37

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strength of the convection itself: Weak updrafts are less capable of lofting the larger droplets that comprise much of the mass of condensed water than are strong updrafts, and so in general we might expect condensate detrainment (and hence anvil cirrus optical thickness and cover) to scale with convective strength. This presents a challenging problem for parameterization: The factors controlling updraft strength are not yet understood fundamentally, and it is not obvious that the essential relationships between updraft strength and cumulus microphysics can even be parameterized in a large-scale model. In addition, other environmental factors such as the ambient humidity structure may influence both the amount of condensate (and hence droplet size) and the degree to which entrainment dilution takes place. Nonetheless, there are at least some indications that convection strength affects cluster optical thickness. Figure 15.2 shows ISCCP optical thicknesses for 9 TOGA/COARE clusters that were also observed by a passive microwave instrument mounted on the ER-2 aircraft (McGaughey et al. 1996). The 85-GHz brightness temperature, which is sensitive to scattering by large ice particles and hence thought to be diagnostic of strong vertical motion lofting ice to high levels, correlates well (in a negative sense, since scattering depresses the brightness temperature) with visible optical thickness in all but one case. Furthermore, the highest optical thickness cluster (with the coldest brightness temperature) was associated with extensive lightning discharges and large (30dBZ) radar reflectivity in the mixed phase region of the cloud (Petersen et al. 1996), features that can only be explained if strong updrafts loft supercooled liquid water to high, cold levels in the cloud. Likewise, two of the less optically

Figure 15.2. ER-2 AMPR 85.5-GHz average brightness temperatures for convective cores sampled during 9 TOGA/COARE flights (McGaughey et al. 1996) versus ISCCP DX visible optical thickness averaged over the spatial extent and life cycle of associated anvils.


317

thick clusters in the diagram were observed by Petersen et al. to occur during a period of suppressed lightning. Ye (1999) has investigated the pre-storm environmental factors contributing to the observed size and optical thickness differences among the TOGA/COARE clusters in figure 15.1 by defining subensembles of large size, small size/optically thin, and small size/optically thick clusters. Large size clusters tend to be favored by strong large-scale upward motion throughout the troposphere and moist low- to mid-level relative humidities, which destabilize the atmosphere and increase the probability that updrafts will maintain their buoyancy against entrainment dilution. Consistent with this, large storms exist in slightly more buoyant environments: Convective available potential energy (CAPE) for pseudo-adiabatically lifted surface parcels is slightly greater for the large size subensemble (3008 J/kg) than for the two small size subensembles (2687 J/kg). This is a promising result for parameterization development because GCMs predict large-scale vertical velocity and relative humidity and have been shown to be capable of simulating reasonable statistics of CAPE occurrence (Ye et al. 1998). On the other hand, large-size clusters are also favored in conditions of strong (>30 m/s) front-to-rear flow, which is not related in any obvious way to the large-scale zonal or meridional wind because of variations in cluster propagation speed and direction that are difficult to predict in a large-scale model. Unlike cluster size, cluster optical thickness has no obvious relationship to parcel buoyancy in the TOGA/COARE data set. Instead, optically thick anvils appear to be favored in situations of strong, low-level moisture convergence, which optimizes the source of condensate. Furthermore, there is a preference for optically thick anvils in situations of strong upper troposphere shear; specifically, an increase in front-to-rear flow with height above the 500-mb level allows cumulus condensate to flow back into a trailing anvil cloud, increasing its ice content, while a decrease in front-to-rear flow with height causes more condensate to fall into the advancing updraft, enhancing autoconversion and reducing detrainment. 15.4. Parameterization Issues for Thin Cirrus

Cirrus clouds that are sufficiently thin for their long-wave radiative warming effect to dominate present a different set of parameterization challenges. First among these is understanding why such cirrus exist. In mid-latitudes, thin cirrus are found in regions of upper-level rising motion in advance of the surface warm fronts of baroclinic waves and are also associated with mesoscale circulations in the vicinity of jet streak entrance and exit regions. In the tropics and the summer mid-latitudes, cirrus are also observed as the product of outflow from deep convective updrafts, usually adjacent to the outer, thinner portion of the cumulus anvil. These source types explain the geographical distribution of thin cirrus simulated by GCMs (fig. 15.3), with peaks in the Intertropical Convergence Zone and mid-latitude storm tracks, and are consistent with the latitudinal variation in thin cirrus occurrence observed by SAGE II (Liao et al. 1995).

3 18

Cirrus

Figure 15.3. October geographical distribution of thin (t < 1) cirrus occurrence (%) in the tropopause region (80-192 mb) simulated by the 2° x 2.5° x 18 L version of the GISS GCM. The contour interval is 10%.

However, the frequency of occurrence of thin cirrus, especially near the tropical tropopause, is much higher than that of convection, and individual thin cirrus clouds often have no obvious connection to a convective source. This type of detached cirrus may sustain itself for long periods of time via cloud-scale motions arising from radiative destabilization within the cloud, but the extent to which this occurs and the role played by such processes climatologically is unknown. Such cirrus are likely to be underrepresented in climate models: The radiative heating profile is sensitive to the cloud physical thickness, typically 0.5 km or less (Starr and Wylie 1990), but current climate GCMs tend to have upper troposphere layers at least twice this thick. Climatologically, the relative contributions to cirrus occurrence from direct convective outflow, large-scale upward motion, and internal cloud-scale regeneration have not been determined. One potential way to distinguish source mechanisms is via characteristic differences in cloud radiative properties and the environmental situations in which they occur. Figure 15.4 shows scatterplots of thin (T < 1) cirrus cloud cover and optical thickness as a function of relative humidity (RH) for the near-tropopause layers of a 2° x 2.5° x 18 L version of the GISS GCM. There are two distinct populations of clouds. For RH > 0.65 (the assumed threshold for stratiform cloud formation in the GCM), cloud cover increases systematically with RH up to 100% cover according to the parameterization proposed by Sundqvist (1978); the two different curves followed by most points in this region of the diagram reflect the fact that GCM stratiform clouds are assumed to fill the gridbox vertically when the layer is disturbed by a coexisting convective event but are assumed to spread out horizontally and have subgrid-scale physical thickness when the box is convectively stable. Superimposed on this is a second population of mostly small cloud cover cirrus occurring primarily (but not


319

Figure 15.4. Scatterplot of individual layer thin (T < 1) cirrus (upper) cloud cover and (lower) visible optical thickness versus relative humidity with respect to ice for the GISS GCM cirrus distribution shown in figure 15.3.

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Cirrus

exclusively) in the presence of dry upper-level conditions. These cirrus are created directly from convective outflow into otherwise unfavorable environments for cirrus generation by the GCM's cumulus parameterization. Note that the two populations play qualitatively different climatic roles: The stratiform cirrus are a response to moist upper-level conditions and act as a sink of water vapor; the convective cirrus are a source of water vapor (after they sublime) for a previously very dry upper troposphere. Correct simulation of the balance between this source and sink, currently unverifiable with data, is a prerequisite for confidence in a GCM's prediction of water vapor feedback in a warming climate. Not surprisingly given the qualitatively different source mechanisms, the frequency distribution of cirrus optical thickness in figure 15.4 is bimodal.Thin cirrus produced by the stratiform cloud parameterization almost exclusively have T > 0.04, while thin cirrus produced by convective outflow span a broad range of optical thicknesses down to 0.001, with peak occurrence in the range 0.01 < i < 0.1. Beyerle et al. (1998) also observed a bimodal distribution of cirrus optical thickness in lidar observations of the tropical Atlantic; however, the thinner segment of their population peaks at lower T than that of the convective outflow population in the GCM. Whether the Beyerle et al. results are climatologically representative is not known. Vertical resolution is not the only problem for GCM cirrus simulation; horizontal resolution is also an issue because cirrus formation and maintenance occur largely on the cloud scale and the mesoscale. Cirrus sensitivity to unresolved dynamics (i.e., cumulus updrafts) may appear to be primarily a tropical problem, but this is not the case. In mid-latitudes, cirrus form typically in ageostrophic circulations associated with 50-km wide frontal uplift regions of baroclinic waves and upper troposphere jet streaks and tropopause folds (see Starr and Wylie 1990; Mace et al. 1995). Although climate GCMs do a surprisingly good job of portraying marginally resolved synoptic aspects of baroclinic waves, they cannot resolve the shears and temperature gradients in the frontal regions. The result is simulated fronts that are more upright than tilted, according to the SawyerEliassen equation, and cirrus occurrence only within the column that contains the surface front rather than spread over a broad region in advance of the front. GCMs that do not underpredict mid-latitude cirrus may often accomplish this artificially via a background vertical eddy diffusion of moisture not tied to any specific process. This may be either explicitly parameterized or an implicit consequence of inaccuracies in the GCM's finite-difference scheme or spectral representation of transport. Water vapor transport errors can also be reflected in choices made in the parameterization of cirrus microphysics. For example, a model with excessive ice crystal fallspeeds may be one whose grid-scale vertical transport of water vapor is too strong and which therefore overpredicts cirrus [see the original version of the United Kingdom Meteorological Office prognostic cloud water scheme developed by Smith (1990)], whereas a model that underestimates upward water fluxes and underpredicts cirrus may characteristically have very weak ice fallout [see the GISS prognostic scheme of Del Genio et al. (1996)]. Given the important role of cirrus in regulating outgoing long-wave radiation, it can be argued that the wide range of cirrus microphysics parameterizations in use in current GCMs is less indicative of uncertainties in the microphysics


321

itself and more indicative of the need to compensate for errors in other parts of the GCM. One exception to this statement is the generally ignored problem of the scale dependence of parameterizations. Existing parameterizations of cirrus microphysical processes (see Heymsfield 1977) are valid on the scale of individual cirrus and are appropriate for use in CRMs, which resolve single clouds. Such parameterizations are sometimes adopted for use in GCMs with grid sizes of hundreds of kilometers, though. On this scale, there is substantial subgrid variability in ice water content (IWC), and to the extent that process parameterizations are nonlinear functions of IWC, they are inappropriate for use in a GCM. For example, the GCM gridbox mean IWC should generally be much smaller than the large local values in regions of cloud in which the majority of ice sedimentation occurs; use of a scheme valid on the cloud scale will cause ice to build unrealistically in the GCM. The same issue arises in simulating the radiative effects of cirrus: even if three-dimensional aspects of radiation are ignored, the gridbox mean albedo and emissivity are not simply related to the gridbox mean IWC because of subgrid IWC variability and the nonlinear relation between albedo/emissivity and optical thickness. Considine et al. (1997) have suggested that histograms of marine-stratus liquid water path are related to cloud cover in a straightforward manner that depends only on the mean cloud depth and the standard deviation of the lifting condensation levels for surface air parcels. Their simple model successfully predicts a distribution that peaks at the lowest value when skies are partly cloudy but at finite liquid water path values in overcast conditions. Cirrus would seem to be more complex—there is no well-defined lower boundary for parcel origin, and horizontal water fluxes should be more important than in the planetary boundary layer. Nonetheless, aircraft observations of cirrus IWC from 18 FIRE II flights analyzed by Smith and Del Genio (2001) exhibit exactly the same behavior (fig. 15.5). The only exception is a single flight (KA05) through nearly overcast skies whose histogram resembles that for partly cloudy situations; this cloud formed under anomalously highly stratified, weakly turbulent conditions that would plausibly suppress the vertical motions required to produce partial cloud clearing. These results tentatively suggest that universal subgrid distributions that depend on only a few parameters may be a feasible approach to the GCM scaledependence issue for radiation and microphysics. Cirrus can nucleate in three fundamentally different ways: Homogeneously, at temperatures T < -40°C; heterogeneously via direct deposition from vapor to ice on a suitable nucleus; and heterogeneously via freezing of previously condensed supercooled water droplets. For GCMs, this translates into uncertainty about whether to make liquid or ice cloud at temperatures above the homogeneous nucleation point but below freezing, and whether to assume saturation with respect to the ice or the liquid phase (or alternatively on the grid scale, a threshold RH referenced to one or the other phase) as the condition for initiating cirrus formation. This issue is only beginning to receive attention in the context of the impact of contrails on cirrus formation (see Jensen and Toon 1997). Less appreciated is the possible indirect effect of aerosols produced in the lower troposphere on cirrus. The indirect effect is generally thought of as purely a short-wave,

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Cirrus

Figure 15.5. FIRE II cirrus ice water content histograms for flights (upper) SA07 (cloud cover 100%), (middle) SA13 (51%), and (lower) KA05 (97%). Overlain are the predictions of a simple cirrus analog to the model of Considine et al. (1997; see also Smith and Del Genio 2001). low-stratus problem, but sulfur chemistry-climate models (see Lohmann and Feichter 1997) suggest that upper troposphere sulfate mixing ratios in northern mid-latitudes are comparable to tropical near-surface concentrations, and observations during SUCCESS suggest significant surface sources of upper troposphere sulfate as well (Dibb et al. 1998), raising the possibility of an indirect longwave effect on cirrus. In the GISS GCM, referencing cirrus initiation to ice rather than to water saturation makes a qualitative difference in the model's energy budget and circulation. Because ice saturation is easier to achieve, cirrus is more frequent, more ice is formed, especially at low latitudes where water vapor concentrations are highest, and less short-wave radiation is thus absorbed in the tropics. This in turn reduces the latitudinal radiative forcing imbalance that drives the general circulation. With a reduced need for poleward heat transport, mid-latitude synoptic storms weaken, producing less cirrus there, and absorbed short-wave radiation consequently increases instead in mid-latitudes. 15.5. Observation and Modeling Strategies for Improved Cirrus Parameterization Parameterization of the formation as well as of the microphysical and radiative properties of cirrus is more uncertain than for liquid-phase clouds because they


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are more difficult to observe, as described in section 15.1. The radiative impact of cirrus within a column of atmosphere is the integrated effect of a large number of parameters: coverage, height, physical thickness, particle size and shape, IWC, and subgrid variability of the cirrus itself; the same properties of any other cloud layers below the cirrus; environmental temperature and humidity profiles; and the surface albedo. Several of these quantities are rather poorly constrained by observations, at least climatologically. Thus, there are many combinations of the parameters within the realm of plausibility that produce similar radiative fluxes. For example, since thick anvil cirrus have approximately unit emissivity, one can change their IWC without changing the long-wave cloud forcing. Thus, three models, one with overly thick anvils and overly thin underlying stratus, another with thinner anvils and thicker stratus, and a third with thin anvils and thin stratus but small ice crystal sizes, can theoretically produce the same TOA radiation budget. As proof that such compensating errors actually occur, Rasch and Kristjannson (1998) showed that state-of-the-art climate GCMs differ wildly in their estimates of global ice water path. Table 15.3, adapted from their work, shows the ice and liquid water paths and the global TOA radiation budgets from three representative GCMs having prognostic cloud water schemes. Each model is within 10 W/m2 of ERBE-derived absorbed short-wave and outgoing long-wave radiation, and given the factor of two differences in available microwave liquid water path climatologies, each is within range of one of the existing estimates, yet the global ice water paths differ by almost an order of magnitude among the models. The absence of a reliable ice water path climatology makes such leeway possible in models. Lin and Rossow (1996) have indirectly estimated ice water path as a residual from ISCCP optical thicknesses, particle size assumptions, and microwave liquid water path estimates, but an approach based on more direct sensing of ice itself is desirable. Recent advances in submillimeter passive remote sensing (see Evans et al. 1998) offer hope that global estimates of ice water path may soon be feasible. Beyond this, active remote sensing techniques that provide cloud-base heights and thicknesses and allow radiative heating profiles through the atmosphere to be calculated would provide additional constraints, but only if such instruments can sample a sufficiently large

Table 15.3. Cloud water paths and top-of-the-atniosphere radiation budgets of three GCMs: GISS (Del Genio et al. 1996), CSU (Fowler et al. 1996), and CCM3 with the prognostic cloud water scheme of Rasch and Kristjannson (1998)

Ice water path (g/m2) Liquid water path (g/m2) Absorbed short-wave radiation (W/m2) Outgoing long-wave radiation (W/m2)

GISS

CSU

CCM3

150 90 238 235

18 44 228 230

20 32 237 237

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fraction of the globe in unbiased fashion for sufficiently long time periods to provide a representative climatology. Finally, more observational constraints on cirrus nucleating particles and environmental humidity conditions, well beyond the few case studies currently available, would help modelers more accurately simulate when cirrus should form. Cloud-resolving modeling of cirrus, as advocated by the GEWEX Cloud System Study (GCSS), also has the potential to contribute to improved parameterization. Three of the four GCSS working groups (WG) study cirrus in some fashion: WG2 focuses specifically on cirrus, and WG3 (extratropical layer clouds) and WG4 (deep convective systems) focus on the dynamic entities that are the sources of most cirrus clouds. The GCSS approach currently envisions a limited number of idealized and real-world case studies by CRMs in tandem with singlecolumn model (SCM) versions of GCM parameterizations as a vehicle for parameterization development. The limited scope of this approach is consistent with the status of GCSS as an unfunded program but is probably not sufficient to meet most parameterization goals. A more comprehensive strategy would add two components: 1. Simulation of a large number of cases by CRMs, covering the phase space of relevant parameters in different climate regimes, would define the probability density functions of subgrid-scale variations of relevant parameters needed to predict fractional cloudiness and to scale microphysical process and radiative parameterizations that are valid on the cloud scale up to the GCM grid scale. 2. SCMs forced by observed dynamic fluxes in theory can test the validity of cloud parameterizations, to the extent that the dynamic fluxes can be accurately specified. Because cloud simulation in a GCM depends also on the accuracy of the simulated dynamics, a separate project exploring the ability of climate GCMs to predict the synoptic evolution and associated mesoscale fluxes of water that determine when and where clouds form, starting from common analysisgenerated initial conditions, would complement current GCSS activities. In the context of a climate GCM, it is not necessary to simulate cirrus occurrence in specific weather events as much as it is necessary to simulate their statistics and relationships with characteristic dynamic structures. Lau and Crane (1995), for example, use ISCCP data in combination with a meteorological analysis to produce composite maps of the relationship of cirrus and other cloud types to features of synoptic-scale midlatitude and tropical storms. This suggests that systematic analysis of a climate GCM's simulation of weather and cirrus formation, composited over many weather events, would provide insights into model inadequacies. Unfortunately, extensive analysis of models is not yet embraced by funding agencies as being of equal value to acquisition of data, making the prospects for progress in this area remote.

Acknowledgments This research was supported by the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission and the Department of Energy Atmospheric Radiation Measurement Program. I thank W. Kovari, Jr., for assistance with the figures.


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16

Ice Clouds in Numerical Weather Prediction Models Progress, Problems, and Prospects

C H R I S T I A N JAKOB

The properties of cirrus, as well as the role ice clouds play in the atmosphere, have been extensively described in the previous chapters. To represent the effects of cirrus in atmospheric models, several intimately linked processes need to be described. These processes include the generation and dissipation of ice clouds as well as their interaction with the radiative fluxes throughout the atmosphere. In this chapter the cloud parameterization aspects of this problem (i.e., the treatment of the generation and dissipation of ice clouds), are discussed in the context of global numerical weather prediction (NWP) models. Aspects of the radiative transfer in ice clouds can be found in chapter 13. The main focus of the current chapter is on the cloud parameterization used in the global forecast model of the European Centre for Medium-Range Weather Forecasts (ECMWF). This parameterization will serve as an example in highlighting the progress made, the problems encountered, and the prospects for improving the representation of ice clouds in atmospheric models. The principles of representing clouds in global NWP models are identical to those in general circulation models (GCMs) used for climate research (see chapter 15). Although ice clouds are the focus of this book, a substantial part of this chapter will be concerned with the overall treatment of clouds in numerical models of the atmosphere. In fact, many models used in NWP today distinguish ice clouds from mixed-phase and water clouds only as a function of temperature. Cloud parameterizations in GCMs have evolved rapidly over the last few years. Section 16.2 is a general overview of the progress made. Section 16.3 will describe the cloud parameterization that is currently used in the ECMWF forecast model as a specific example for a state-of-the-art cloud 327

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parameterization in NWP. General aspects of the simulation of ice clouds with this model will be presented. GCM simulations of the atmosphere are very sensitive to the treatment of clouds in general (Senior and Mitchell 1993; Rasch and Kristjansson 1998) and to assumptions about cloud ice in particular (Fowler et al. 1996; Jakob and Morcrette 1995). Section 16.4 gives an example of those sensitivities and the model design problems that can arise when model sensitivities exist in combination with a lack of observations, as noted for cloud ice by Stephens et al. (1998). As an example, the fallspeed of settling ice particles in the ECMWF model is systematically modified and the model climate response to those modifications assessed. The model climate undoubtedly affects the overall forecast performance of NWP models, particularly in the medium range (5-10 days). However, there is an additional requirement for a cloud parameterization when applied in those models—namely, the ability to predict the instantaneous distribution of clouds over the globe. There are two main reasons for demanding good knowledge about clouds at any given time. First, they are a forecast product, and one can envisage the direct use of model output for their prediction. Second, a large amount of data is gathered in cloudy regions by existing and planned satellite systems. A reasonable simulation of clouds in short-range forecasts is a prerequisite for the use of this data in global data assimilation systems. These "new" tasks for a cloud parameterization lead to additional requirements for the evaluation of NWP predicted cloud fields. Section 16.5 examines the prospects of using new data sources, such as radar and lidar measurements, for that purpose.

16.1. General Parameterization Issues

Arakawa (1975) summarized the reasons for parameterizing clouds in GCMs and the state of the then-existing parameterization schemes as follows: The importance of clouds in climate modelling cannot be overemphasized. Clouds, and their associated physical processes, influence the climate in the following ways: 1. By coupling dynamical and hydrological processes in the atmosphere through the heat of condensation and evaporation and redistributions of sensible and latent heat and momentum; 2. By coupling radiative and dynamical-hydrological processes in the atmosphere through the reflection, absorption, and emission of radiation; 3. By coupling hydrological processes in the atmosphere and in the ground through precipitation; and 4. By influencing the couplings between the atmosphere and the ground through modifications of the radiation and the turbulent transfers at the surface. Although these cloud-dominated processes have long been known to be important in determining climate, clouds have been very poorly formulated in climate models.

Today, the treatment of clouds in GCMs can probably still be described as crude. Nevertheless, state-of-the-art cloud parameterizations today are considerably more advanced than those in 1975. The difficulties in parameterizing clouds

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arise partly from a lack of understanding of the processes in individual clouds, and partly from problems in describing the distribution of an ensemble of clouds in a several-hundred square-kilometer gridbox based only on knowledge about the average dynamic and thermodynamic variables. It is the purpose of this section to briefly review the conceptual changes in the approach to cloud parameterization and to highlight the most recent developments relevant to ice clouds. One of the main tasks of cloud parameterizations is the description of the radiative of effects of clouds on both the atmosphere and the Earth's surface. To achieve this, several parameters such as the areal cloud cover in a gridbox, its vertical extent, the amount and phase of condensate, and the size and shape of the cloud particles need to be described. Out of this list, cloud parameterizations classically deal with the fractional cloud cover and the amount and phase of condensate. For all other parameters, simple assumptions are made whose details depend on the complexity of the treatment of clouds in the radiation schemes used in the respective model. To describe the latent heat effects connected to clouds and precipitation, a description of the condensation and evaporation processes within clouds, as well as the formation and dissipation of precipitation, are necessary. This requires some treatment of the microphysical processes occurring in clouds and precipitation. The process of moist convection, which obviously leads to cloud formation, is treated by a separate parameterization scheme in all global atmospheric models (e.g., Arakawa and Schubert 1974; Tiedtke 1989). These parameterizations are designed to describe the influence of moist convection on the heat and moisture budget of the model's gridboxes. Although a link to clouds obviously exists, the parameterization of that link continues to be a matter of lively debate (e.g.,Tiedtke 1993; Randall 1995). Early cloud parameterizations diagnosed both fractional coverage and condensate content from the large-scale conditions, such as relative humidity (e.g., Slingo 1987). Such a treatment can only provide the interaction of clouds with radiation, and the latent heat effects need to be treated with separate, simple condensation schemes. Condensation is assumed to occur whenever the gridbox mean relative humidity exceeds 100%, and the resulting condensate is precipitated out in the same model time-step. Introducing a convective cloud type whose area coverage is diagnosed from the convective precipitation rate and whose condensate content is prescribed provides a link to the convection schemes. The complete separation of convection, cloud, and condensation schemes, apart from being counterintuitive, leads to the undesirable effect that the clouds that interact with the radiative fluxes are not related to the actual condensation/ evaporation processes predicted by the model. Ice clouds in these early schemes either did not exist at all or the phase of the condensate was prescribed as a function of temperature. The link between the hydrological and radiative aspects of clouds was first established with the introduction of a prognostic equation for cloud condensate (Sundqvist 1978), where the condensation and evaporation processes directly modify the amount of condensate that is used in describing the radiative impact of the clouds. The cloud fraction is still treated as a diagnostic quantity

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depending mainly on the relative humidity in the gridbox. Most NWP centers today are using, or are about to use, a prognostic equation for cloud condensate in their operational global forecasting systems. An alternative approach is that of Smith (1990), in which the cloud variables (both fraction and condensate amount) are diagnosed assuming some knowledge about the distribution of moisture and temperature-related variables in a grid box. In both approaches convective clouds are separate entities whose treatment is similar to that in the diagnostic schemes. In early implementations of the prognostic condensate equation, the distinction of phase is still made based on temperature. More recent applications of the schemes include a separate equation for the evolution of cloud ice (e.g., Fowler et al. 1996, Rotstayn 1997). Tiedtke (1993) proposed an approach to cloud parameterization that includes prognostic equations for both cloud fraction and cloud condensate. One of the most important differences between Tiedtke's approach and the ones described above is the manner in which individual physical processes affect the clouds. Earlier cloud parameterizations predict clouds using the current value, or the rate of change, of grid-mean variables after all physical processes (e.g., vertical motion, convection, turbulence) have adjusted those variables. In other words, the clouds are based on the net integrated result of all physical processes. In Tiedtke's "process-oriented" approach, the clouds are the direct result of the physical processes. Here, the potential of each individual process to generate or dissipate clouds is assessed, and a change of cloud fraction and amount of condensate due to that process is evaluated. Figure 16.1 shows the conceptual difference between the two approaches in a schematic way. The process-oriented approach leads to a very strong coupling of all physical processes and their effects on clouds. Of all conceptual changes made in recent years, the most important one for ice clouds is the direct link of convection to clouds, which is now used in many cloud parameterizations (e.g., Ose 1993;Tiedtke 1993; Roeckner 1995; Del Genio et al. 1996; Fowler et al. 1996). The ice cloud types affected are those generated by active deep convection, which are frequently observed in the atmosphere in the form of anvil clouds and/or cirrus debris. The basic idea behind this coupling is illustrated in figure 16.2. In simplified terms, the convective parameterization describes the circulation of mass through convective-scale updrafts, which are driven to a large extent by the latent heat release due to condensation. The condensate formed in these updrafts is transferred into the model clouds at the levels where the convective updrafts terminate. In deep convection this leads to a large source of cloud ice in the upper troposphere, which strongly affects the radiative balance. It will be shown later that this source of cloud ice can be the most important one in an atmospheric model. 16.2. The ECMWF Cloud Parameterization

In the following sections the ECMWF model is used to highlight some critical aspects of the parameterization of ice clouds in NWP models. The cloud parameterization used in this model has been developed by Tiedtke (1993), with


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Figure 16.1. Schematic of the (top) integrating and (bottom) process-oriented approaches to cloud parameterization.

further changes discussed by Jakob (1994). The scheme is based on two prognostic equations for cloud fraction, a, and cloud condensate, / (the sum of water and ice):

and

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Figure 16.2. Schematic of the link of detrainment of condensate from convective updrafts to cloud production.

where Aaj represents advection, and Sait and Dal are the source and dissipation terms for cloud fraction and condensate, respectively. Sources for cloud fraction and condensate in the scheme arise from convection, boundary layer turbulence (for stratocumulus clouds), large-scale lifting, and diabatic cooling. The dissipation terms are determined by evaporation processes due to subsiding motions, including both large-scale and cumulus-induced subsidence, diabatic heating, turbulent erosion processes at both cloud top and cloud sides, and precipitation processes (condensate only). Through the direct link of clouds to all physical processes, the scheme falls into the class of the process-oriented schemes as


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described in the previous section. The distinction between the different phases of condensate is made solely as a function of temperature. Pure ice clouds exist for temperatures lower than -23°C, mixed-phase clouds occur between -23°C and 0°C, and pure water clouds are formed at temperatures above 0°C. The definition of an ice cloud in the ECMWF model would therefore be a cloud that exists at a temperature colder than -23 °C, quite different from the definition of cirrus used elsewhere in this book. Figure 16.3 shows the zonal mean ice water path (IWP) for June/July/August (JJA) 1987 and December/January/February (DJF) 1987-88 produced by the ECMWF model. The model has in each case been integrated for a 4-month period, beginning 1 month before the averaging period, with a horizontal resolution of T63 (= 250km), 31 model levels in the vertical, and time-varying seasurface temperatures (SST). For JJA, the geographical distribution of IWP exhibits two maxima, which are located in the Intertropical Convergence Zone (ITCZ) and at mid-latitudes of the Southern (winter) Hemisphere. The largest values of IWP in the model occur over the tropical oceans, reaching values >100g/m2. The values in the winter hemisphere reach 100 g/m2, whereas the summer hemisphere values are only half as large. In DJF, the mid-latitude maximum shifts to the Northern Hemisphere, although the values remain lower

Figure 16.3. Zonal mean distribution of ice water path produced by 4-months integrations of the ECMWF model at T63L31 resolution. The results shown are averages over the last 3 months of the integrations.

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than those in the Southern Hemiphere in JJA. The tropical IWP is lower than in JJA and the maximum is shifted south, consistent with the southward movement of the ITCZ. One of the biggest problems in global modeling is that there is little data to judge the quality of the simulation of IWP shown in figure 16.3, as highlighted by Stephens et al. (1998). Lin and Rossow (1996) have derived the only estimates of IWP on a global scale to date. They found values of 80-120 g/m2 for the winter mid-latitudes, 50-100 g/m2 for the tropics, and 50-100 g/m2 for the summer mid-latitudes. The model simulation appears to be in good agreement with those values; however, it should be assessed with great caution. First, agreement in zonal mean averages of a vertically integrated quantity might not be indicative of a good simulation of cloud ice, and second, the error margins are large in both model and observations. To assess which physical processes contribute to the cloud ice distributions shown above, zonal mean distributions of different source and sink terms for cloud ice (T < -23°C) have been calculated (fig. 16.4). The results shown represent vertical integrals for all layers with T < -23°C averaged over a model integration for July 1998. There are two sources of cloud ice: the detrainment from convection and the condensation processes due to large-scale motion and radiative cooling. The largest sources for cloud ice occur in the tropics. The dominant source is convection, which accounts for 90% of the total cloud ice production. The subtropics, particularly in the winter hemisphere, are regions of minimum ice production. At mid-latitudes two different pictures emerge. In the Northern Hemisphere (summer) southward of 80°N, convection still dominates ice generation. This is most likely due to the fact that in summer considerable amounts of ice are generated by convective events over the land areas. Also, due to the activity of the summer monsoons, the subtropical minimum is much less visible in this hemisphere. In the Southern Hemisphere (winter), the ice generation is dominated by nonconvective condensation processes mostly linked to ascending motion in model-resolved baroclinic systems. The two sinks of cloud ice are evaporation processes, due to large-scale or cumulus-induced subsidence and radiative heating, and the conversion to precipitation. The dominant sink term in all regions is clearly the conversion of cloud ice to precipitation. The apparent residual between sources in sinks is due to ice settling out of the bottom of the integration domain, which comprises only levels with temperatures lower than -23°C (pure ice clouds). Note that in figure 16.3, typical IWPs are an order of magnitude smaller than their sources and sinks. Hence, the IWP is not necessarily indicative of the strength of the hydrological cycle of the model. 16.3. Sensitivity to Ice Fallspeed

Atmospheric models are very sensitive to the treatment of cloud ice (e.g., Gregory and Morris, 1996). As an example, the sensitivity of the ECMWF model climate to assumptions about value of the terminal velocity of ice particles will be investigated. First, a brief overview over the fallout parameterization and its numerical implementation is given.

Figure 16.4. Zonal mean distribution of sources and sinks of cloud ice in the ECMWF model. The results represent an average for July 1998 taken from a T63L31 simulation of the month. Included are sources due to detrainment from convection (Detr.) and nonconvective condensation processes (Cond.) and sinks due to evaporation (Evap) and precipitation (CVSN).

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16.3.1. Ice Fallout Formulation and Its Numerical Treatment As in many GCMs, the fallspeed of cloud ice is parameterized in the ECMWF model as a function of ice content using the functional form

where a and b are constants, p is the air density, and / the specific ice content in units of kilograms per kilogram. The derived fallspeed is then used to calculate the tendency of cloud ice due to settling (sett) for a given model layer as the flux divergence of the ice flux as

Note that the positive sign of the right-hand-side of equation 4 occurs because vt is defined positive downward. A careful look at the physical meaning of equations 3 and 4 in an atmospheric model having a horizontal resolution of up to several hundred kilometers reveals several problems. First, the use of a single fallspeed implies that all ice particles in the gridbox are falling at that speed. This is an oversimplification because, in reality, a spectrum of ice particle sizes and terminal velocities exist. Second, there are numerical treatment considerations. Typical fallspeeds given by equation 3 reach values between 0.5 and Im/s, and in GCMs, model time-steps can be 30min to 1 h with vertical resolutions in the upper troposphere of 500m to 1 km. With a fallspeed of, say, Im/s, ice particles will settle through more than one model level in one model time step. Hence, the numerical treatment of equation 4 is far from trivial. Consequences of the first problem are highlighted by the following example. If a constant fallspeed, v, = v(0, is assumed, equation 4 reduces to a onedimensional advection equation. It can be shown for this equation that an explicit upstream numerical treatment,

yields an exact solution if viQAt = Az. In the case of two model layers with cloud ice contents of /£_! = /0 and l'k = Vk_2 = 0, the solution of equation 5 under the additional assumption that vi0At = Az yields for layers k and k - 1 is /£f = 0 and Ik* - k- Thus, the whole ice content is moved down one model layer. Although numerically exact, this solution reveals the simplicity of the physical assumption made because in reality an ice cloud does not move as a "block" because of the variety of fall velocities present. In the second problem, the long time steps encountered in GCM simulations prevent an explicit numerical treatment of equation 4 because the solution for v,-oAf » Az becomes numerically unstable. One possibility of achieving numerical stability is to solve equation 4 analytically, as proposed in Rotstayn (1997). Tiedtke (1993) has shown that if equation 2 can be rearranged in the form


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yielding the solution

It is evident that in case of equation 4 such a rearrangement is possible, whereby C = v/o/Jt-i/Az and D = v,0/Az. Using the same assumptions as above, solutions for the ice contents in layer k - 1 and k are /£f = 0.37/0 and /fAf ~ 0.63/0. This solution, although numerically inaccurate, leads to a physically more appealing result. Cloud that was initially confined to layer k -1 has spread downward without being completely removed from layer A;. This is entirely an artifact of the numerical treatment and should hence be treated with great care when interpreting model output. 16.3.2. Sensitivity to the Value of Terminal Velocity of Ice The operational ECMWF global model uses a formulation of ice settling following equation 4 with a numerical treatment as in equation 7. The fallspeed in that version is a function of the ice content as in equation 3. The constants are chosen as a = 3.29 and b = 0.16, following Heymsfield and Donner (1990). The integration of the model with that version is referred to as the control simulation. Six more model integrations were carried out, each using a fixed value of fallspeed of 0.1, 0.3, 0.5,1,1.5, and 2m/s, respectively. Figure 16.5 shows the 3-month average (JJA) of the global means of IWP, integral radiative flux divergence (= top of the atmosphere minus surface net radiative flux), and precipitation as a function of ice fallspeed. The horizontal lines represent the results of the control experiment. It is evident that with the reduction of fall speed, the IWP averaged over the globe increases from values around 40g/m2 for the largest assumed fall speed (2m/s) to 140 g/m2 for the smallest (0.1 m/s).This leads to a reduction of the global mean integral radiative flux divergence from 110W/m2 to 90W/m2 This decrease is achieved mainly through a decrease in outgoing long-wave radiation (OLR).The integral flux divergence of the solar component and the net surface long-wave radiation change only by small amounts (not shown). The reduction in the radiative cooling has an immediate effect on the atmosphere's response through latent heat release, which becomes obvious in figure 16.5c.The global mean precipitation is reduced from 3.25 mm/day to 2.7 mm/day. The main reduction is in convective precipitation, pointing to a much lower level of convective activity when using small ice fallspeeds. The geographical distribution of the changes outlined is shown in figure 16.6 for the most extreme case of fallspeed, v, = 0.1 m/s. The figure shows the change in IWP, OLR, and convective precipitation with respect to the control simulation. The largest changes occur in the tropics. This is not surprising because the tropics have been identified as the region of maximum ice production (see fig. 16.4) in the model. It is, however, noteworthy that the regions of maximum change in OLR do not coincide with the region of minimum OLR in the ITCZ (not shown) but occur downwind to both sides of the minimum value. This is most

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Figure 16.5. Three-months averages (JJA87) of the global means of (a) ice water/liquid water path, (b) integral radiative flux divergence, and (c) precipitation in aT63L31 version of the ECMWF model as a function of the assumed fallspeed for ice. Control model results are shown as horizontal lines. Precipitation is shown as total precipitation (TP) and is split into convective (CP) and large-scale (LSP) precipitation.


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Figure 16.5. (continued)

likely due to an increase of the residence time of cloud ice in the atmosphere caused by the decrease in the fallspeed of the ice. Hence, the advection process is now able to transport ice farther away from its source before it falls out. It has been shown that through the modification of assumed fallspeed of ice particles, the climate of the ECMWF model, particularly in the tropics, can be changed dramatically. It is obvious from the slope of the curves in figure 16.5a-c that the control model results are situated in a sensitive part of the parameter space, although not the most sensitive one. Hence, small changes to the fallspeed parameterization will substantially modify the model climate. Unfortunately, the accuracy of the available global observations is not sufficient to draw firm conclusions about the correct value of IWP in the sensitivity experiments. In the next section, recently available observations of ice cloud parameters, such as cloud fraction and ice content, are used to evaluate model performance. 16.4. New Data Sources for the Evaluation of Ice Cloud Parameterizations

The high sensitivity of atmospheric models to the treatment of ice clouds calls for a thorough model evaluation. Unfortunately, as mentioned before, few observations for such an evaluation exist. This is particularly true for global data sets on the long time-scales necessary to evaluate climate model predictions of ice clouds. Because NWP models have the advantage of predicting individual cloud

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Figure 16.6. June/July/August 1987 differences between the experiment assuming v, = 0.1 m/s and the control simulation for ice water path (g/m2; top), outgoing long-wave radiation (W/m2; middle), and convective precipitation (mm/day bottom).


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events, it is possible to evaluate these "cloud forecasts" directly, using data that are available only for a limited time or at few locations. The approach is to perform short-range forecasts with the NWP model and then to compare the results to instantaneous measurements of cloud parameters, such as cloud fraction (Mace et al. 1998a; Miller et al. 1999). Two examples, using a space-borne lidar system and a ground-based radar, are used to highlight this method. In September 1994 the LITE (McCormick et al. 1993; Winker et al. 1996) lidar system was installed on the space shuttle Discovery. Miller et al. (1999) derived cross-sections of the vertical distribution of cloud fraction from the LITE data and compared them to those predicted by the ECMWF model in the 24- to 30-h forecast range (fig. 16.7). This particular orbit includes a slice through the western Pacific warm pool, and it is therefore possible to evaluate model clouds that are most likely produced by the convection detrainment mechanism described earlier. It is evident that the model is able to simulate the deep,

Figure 16.7. Comparison of the vertical distribution of cloud fraction from LITE orbit 124 as predicted by the ECMWF model (top) with that derived from LITE (Miller et al. 1999) (bottom).

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anvil-like cloud structures between 0° N and 20° N. This comparison, together with the work of Mace et al. (1998a), is one of the first to evaluate the details of the vertical structure of model-generated cloud fields. Although important, the correct simulation of cloud fraction is a necessary but not a sufficient condition for capturing the main hydrological and radiative effects of modeled clouds. The amount of condensate present in the cloud must also be correctly simulated. In the context of the Atmospheric Radiation Measurement program (ARM; Stokes and Schwartz, 1994), millimeter-wave cloud radars (MMCR; Moran et al. 1998) have been installed at ARM's Southern Great Plains (SGP), Tropical Western Pacific, and North Slope of Alaska sites. Mace et al. (1998a) have used data from the radar at the SGP site to evaluate the ability of the ECMWF model to predict the vertical distribution of hydrometeors. They concluded that the model exhibits sufficient skill to allow more detailed investigations. Mace et al. (1998b) derived ice contents for isolated cirrus clouds from combined radar reflectivity and infrared interferometer data. Figure 16.8 shows a first evaluation of the ECMWF model's ability to simulate the ice content in those clouds. The figure shows the frequency distribution of ice content for clouds at temperatures lower than 219 K and higher than 227 K (note that all clouds in this study need to be colder than 250 K to be ice clouds) derived from observations and two versions of the ECMWF model. The chosen boundary tempera-

Figure 16.8. Frequency distributions of cloud ice in isolated cirrus (see Mace et al. 1998b for definition) over the ARM SGP site for two different temperature ranges. Shown are values derived from a combination of radar reflectivity and infrared interferometer data and from two versions of the ECMWF model.


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tures represent the 33% and 67% percentiles of the observed cloud distribution with respect to temperature. The observations were gathered between November 1996 and December 1997. The model data represent the periods November 1996-October 1997 and January-July 1998. Although the time periods over which the comparison is made are not identical, enough cases are used in all samples to allow at least a qualitative comparison. A marked difference in the shape of the observed distributions exists for the two temperature regimes. At low temperatures the distribution is fairly narrow and exhibits a large peak at low ice contents. Although similar in shape, the distribution at high temperatures is much broader, and the peak at low ice contents is less pronounced. Large ice contents are encountered more frequently at high temperatures. The model captures this difference in the distributions to a fair degree. However, both model versions severely overestimate the frequency of very low ice contents and underestimate the number of events with intermediate values. Despite these problems, the 1998 version of the model, which incorporates a change to the numerical treatment of falling cloud ice, constitutes an improvement over the 1997 version. Although only qualitative in nature, this comparison is a major step forward in model evaluation. For the first time, a long time series of point observations of cloud ice exist, and they can be compared to model results at least in a statistical way. The model-to-data comparisons presented above are far from comprehensive. They should only be considered as examples of the possible ways to evaluate short-range model cloud forecasts using the remotely sensed data. This approach will gain importance with the use of data provided by anticipated space-borne radar systems within the next 5-10 years. 16.6. Summary

Despite the considerable progress made in the last few years, the parameterization of clouds in general, and that of ice clouds in particular, remains one of the biggest challenges in global modeling. The large sensitivity that modeled atmospheres show to parameterization assumptions and the general lack of data necessary to evaluate the model simulations creates a serious problem for the modeling community. The use of new data acquired by both space-borne and ground-based active remote sensing instruments for the evaluation of ice cloud simulations in atmospheric models shows the optimistic prospects for eliminating some of the remaining uncertainties. It was the purpose of this chapter to outline these three aspects for global NWP models using the ECMWF model as an example. In the future, NWP will continue to play an important role in the quest to improve cloud simulations in global models. References

Arakawa, A., 1975. Modelling clouds and cloud processes for use in climate models. Global Atmospheric Research Program Publication Series, no. 16; The Physical Basis of Climate and Climate Modeling. 183-197.

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Arakawa, A., and W.H. Schubert, 1974. The interactions of a cumulus cloud ensemble with the large-scale environment. Part I. J. Atmos. Sci., 31, 674-701. Del Genio, A.D., M.-S. Yao, W. Kovari, and K.K.-W. Lo, 1996. A prognostic cloud water parameterization for global climate models. J. dim., 9,270-304. Fowler, L.D., D.A. Randall, and S.A. Rutledge, 1996. Liquid and ice cloud microphysics in the CSU general circulation model. Part I: Model description and simulated microphysical processes. /. dim., 9,489-529. Gregory, D., and D. Morris, 1996. The sensitivity of climate simulations to the specification of mixed phase clouds, dim. Dyn., 12, 641-651. Heymsfield, A.J., and L.J. Donner, 1990. A scheme for parameterizing ice-cloud water content in general circulation models. /. Atmos. Sci., 47,1865-1877. Jakob, G, 1994. The impact of the new cloud scheme on ECMWF's integrated forecasting system. In ECMWF Workshop on Modeling, Validation and Assimilation of Clouds, Oct. 31-Nov. 4, 1994. European Centre for Medium-Range Weather Forecasts, Reading, UK, pp. 277-294. Jakob, G, and J.-J. Morcrette, 1995. Sensitivity of the ECMWF model to the treatment of the ice phase. In Cloud Microphysics Parametrizations in Global Atmospheric Circulation Models, WMO/TD-no. 713. World Meteorological Organization, Geneva, pp. 37-46. Lin, B., and W.B. Rossow, 1996. Seasonal variation of liquid and ice water path in nonprecipitating clouds over oceans. /. dim., 9,2890-2902. Mace, G.G., C. Jakob, and K.P. Moran, 1998a. Validation of hydrometeor occurrence predicted by the ECMWF model using millimeter wave radar data. Geophys. Res. Lett., 25,1645-1648. Mace G.G., T.P. Ackerman, P. Minnis, and D.F. Young, 1998b. Cirrus layer microphysicsl properties derived from surface-based millimeter radar and infrared interferometer data./. Geophys. Res., 103, 23207-23216. McCormick, M.P., D.M. Winker, E.V. Browell, J.A. Coakley, C.S. Gardner, R.M. Hoff, G.S. Kent, S.H. Melfi, R.T. Menzies, C.M.R. Platt, D.A. Randall, and J.A. Reagan, 1993. Scientific investigations planned for the Lidar In-space Technology Experiment (LITE). Bull. Amer. Meteor. Soc., 74, 205-214. Miller, S.D., G.L. Stephens, and A.C.M. Beljaars, 1999. A validation survey of the ECMWF prognostic cloud scheme using LITE. Geophys. Res. Lett., 26,1417-1420. Moran, K.P., B.E. Mariner, M.J. Post, R.A. Kropfli, D.C. Welsh, and K.B. Widener, 1998. An unattended cloud profiling radar for use in climate research. Bull. Amer. Meteor. Soc. 79,443^55. Ose, T, 1993. An examination of the effects of explicit cloud water in the UCLA GCM. J. Meteor. Soc. Japan, 71, 93-109. Randall, D.A., 1995. Parameterizing fractional cloudiness produced by cumulus detrainment. In Cloud Microphysics Parameterizations in Global Atmospheric Circulation Models, WMO/TD-No. 713, World Meteorological Organization, Geneva pp. 1-16. Rasch, P.J., and J.E. Kristjansson, 1998. A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. /. dim., 11,1587-1614. Roeckner, E., 1995. Parameterization of cloud radiative properties in the ECHAM4 model. In Workshop on Cloud Microphysics Parameterizations in Global Atmospheric Circulation Models, WMO/TD-No. 713, World Meteorological Organization, Geneva pp. 105-116. Rotstayn, L.D., 1997. A physically based scheme for the treatment of stratiform clouds and precipitation in large-scale models. I: Description and evaluation of the microphysical processes. Quart. J. Roy. Meteor. Soc., 123,1227-1282.


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Senior, C.A., and J.F.B. Mitchell, 1993. Carbon dioxide and climate: The impact of cloud parameterization. /. dim., 6, 393^-18. Slingo, J.M., 1987. The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113, 899-927. Smith, R.N.B., 1990. A scheme for predicting layer clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 116,435-460. Stephens, G.L., C. Jakob, and M. Miller, 1998. Atmospheric ice —a major gap in understanding the effects of clouds on climate. Global Energy and Water Cycle Experiment Newsletter, 8, no. 1. Stokes, G.M., and S.E. Schwartz, 1994. The Atmospheric Radiation Measurement (ARM) Program: Programmatic background and design of the Cloud and Radiation Test Bed. Bull. Amer. Meteor. Soc., 75,1201-1221. Sundqvist, H., 1978. A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc., 104, 677-690. Tiedtke, M., 1989. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Man. Wea. Rev., 117,1779-1800. Tiedtke, M., 1993. Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 3040-3061. Winker, D.M., R.H. Couch, and M.P. Cormick, 1996. An overview of LITE: NASA's Lidar In-space Technology Experiment. Proc. IEEE, 84,164-180.

17

Dynamical Processes in Cirrus Clouds A Review of Observational Results

MARKUS QUANTE DAVID O'C. STARR

17.1. The Role of Dynamics and Related Experimental Studies

Local dynamical processes are a key factor determining the microphysical characteristics and typically heterogeneous macroscopic structure of cirrus cloud fields. The internal and background flow fields are correspondingly heterogeneous, albeit only weakly turbulent in most instances, as is discussed here. Nucleation processes and ice crystal growth and habit are intrinsically governed by the local temperature and humidity (saturation ratio) conditions that, in turn, are strongly regulated by the intensity and duration of local updrafts and downdrafts. The microphysical result of equivalent lift by a 50cm/s updraft over a cell width of 200m is quite different from that by a 0.5 cm/s updraft over a 2-km width, even though the overall mass fluxes are equivalent. The great degree of horizontal structure seen in fallstreaks emanating from cirrus likely reflects corresponding variability in microphysical properties, primarily ice crystal size, resulting from variability in the dynamical conditions in the ice-crystal-generating layer. The ice fallout process is a first-order effect in determining overall cloud ice water path. Entrainment of noncloudy environmental air and internal mixing processes are other dynamical aspects that likely play a significant role in cloud life cycle. Dynamical processes provide an important coupling between cirrus cloud microphysical and radiative processes, as described in chapter 18 and illustrated in figure 17.1. Cirrus cloud microphysical properties and macroscopic structure strongly affect the overall radiative properties of a cirrus cloud field and thus the important radiative effect of cirrus in the climate system. Knowledge of the dynamical 346

Observations on Dynamical Processes

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Figure 17.1. Schematic illustrating the complex interactions within cirrus clouds among turbulence and other dynamical processes and microphysical and radiative processes.

processes influencing cloud macrophysical properties and microphysical structure is important to understanding the origin of these characteristics. Moreover, cloud-resolving models of cirrus cloud systems must be evaluated in these respects due to the importance of cloud dynamical processes in determining overall cloud properties. Dynamical processes in cirrus are linked to the state of the background flow field that, in general, is characterized by significant wind shear and a stable thermal stratification. Gravity waves are ubiquitous and occur over a range of scales. Upper tropospheric turbulence tends to occur intermittently in patches, likely a result of sporadic shear generation (Kelvin-Helmholtz instabilities) or breaking gravity waves. Turbulent mixing in stratified shear flows is a notoriously difficult subject, and advances in its description have been obtained only recently (e.g., Fernando 1991; Schumann and Gerz 1995; Vanneste and Haynes 2000). Additionally, cloud-scale turbulence in cirrus may be generated by heating and cooling effects associated with phase changes of water and radiative processes leading locally to convection. Only a limited set of detailed dynamical measurements in cirrus is available. The data were chiefly obtained during the FIRE (Starr 1987) and ICE/EUCREX (Raschke 1988; Raschke et al. 1998) field campaigns. Extended measurements of turbulence in cirrus were first reported by groups from the former USSR (Pinus and Litvinova 1980; Dmitriev et al. 1984,1986; Ermakov et al. 1984). Most of the data have been obtained at mid-latitudes in the Northern Hemisphere. Here we discuss results from analysis of the available in situ data. Results from studies using high-resolution Doppler radars (e.g., Auria and Campistron 1987; Palmer and Martner 1995; Fujiyoshi et al. 1999) are not considered here, but will likely become increasingly important.

348

Cirrus

Concepts for analyzing and interpreting turbulence data are presented in section 17.2, and gravity waves are also discussed. Existing measurements are briefly reviewed in section 17.3. Results of published studies are surveyed and discussed in section 17.4, including mean flow characteristics, turbulent statistics and fluxes, and results from spectral and wavelet-transform analyses. Conclusions and recommendations are offered in section 17.5. 17.2. General Concepts

Turbulence in cirrus clouds is linked to the dynamical state of the ambient flow field in which the clouds are embedded. To distinguish cloud dynamical processes, it is important to properly account for the background flow, its variability, and associated processes when interpreting high-frequency turbulence measurements. Intermittent turbulence may occur in the absence of cloud or in association with, but quasi-independent of, cloud processes. Some ambiguity is often present. Here we provide a brief theoretical background on turbulence quantities and their importance. More elaborate discussions of atmospheric turbulence may be found in reviews by Panofsky and Dutton (1984) and Wyngaard (1992). Houze (1993) discusses turbulence and instabilities in his book on cloud dynamics. In general, it is important to distinguish between developed turbulence and waves and other coherent structures because of differences in their transport characteristics and resultant differences in the interactions with cloud processes. In a vertically sheared, stably stratified flow, typical of upper tropospheric conditions, several questions are of fundamental importance, including the effects of buoyancy on different scales of turbulence and on mixing in the turbulence regime and the production of turbulence by shear-turbulence interactions. In general, it is suggested that mixing in a sheared, stratified turbulent flow consists of a small number of powerful stirring events (e.g., Piccirillo and Van Atta 1997) confined to thin layers, total mixing across larger distances can be regarded as a discontinuous process in time occurring in a stepwise manner involving several turbulence events (Dewan 1981; Vanneste and Haynes 2000). If the sources of turbulence vanish, the turbulence decays rapidly or may even collapse under the influence of strong stratification (e.g., Etling 1993). If gravity (buoyancy) waves are present in the flow, wave-turbulence interactions are possible. Reviews of turbulence in stably stratified flows and related transitional phenomena are provided by Hopfinger (1987) and Thorpe (1987). 17.2.1. Turbulent Kinetic Energy In the simplified case of homogeneous turbulence where the effects of vertical shear and stratification dominate, the turbulent kinetic energy, .Ekin, evolves according to


349

with Ekin = 1/2 (u'2 + v'2 + w'2} ; u', v', w', and 0' denote the fluctuating parts of the velocity components and potential temperature, respectively (prime indicates the deviation from an appropriate average; e.g., horizontal); U and V are the mean horizontal wind components, 90 is the mean potential temperature, and g is the acceleration due to gravity. Terms contributing to shear (mechanical) production of Ekin are denoted P, while B indicates the buoyancy consumption/production term, and e represents viscous dissipation. Although homogeneous turbulence is unlikely to occur over extended areas, the above relationship is useful to illustrate the interplay of shear production and buoyancy effects, which are believed to be the predominant factors in upper tropospheric turbulence associated with cirrus cloud systems. Turbulent kinetic energy (TKE) may be mechanically produced by extraction of kinetic energy from the mean flow in the presence of wind shear (i.e., Kelvin-Helmholtz instability) or by gravity wave-breaking. Buoyancy production of TKE occurs through radiative heating and latent heat release (phase changes of water) that influence the heat flux, w'Q'. For example, cloud-top radiative cooling can lead to sinking air motion (TKE production) and associated eddy generation, as seen in marine stratocumulus cloud layers. Conversely, enhanced infrared radiative cooling can occur in association with enhanced ice water content in a rising air parcel, leading to a negative eddy heat flux (TKE consumption). The rates of radiative and latent heating are generally of comparable magnitude in cirrus clouds, although the spatial patterns can be quite different given the history and far-field (nonlocal) factors that determine local radiative heating. In terms of local TKE production in a cirrus cloud, the radiative and latent heating fields may act in opposition or in concert depending on the local circumstances. In a statically stable environment, TKE is mainly consumed by work against the thermal stratification and by viscous dissipation. Because the buoyancy term acts only on the vertical velocity component of TKE, the flow tends to be anisotropic on scales where this term dominates. In general, the distribution of cloud-related sources and sporadic wave-breaking events leads to an overall intermittent distribution of TKE. Hence, horizontal wind shear and advection of TKE can also be significant factors. Thus, because of the intermittence of the generation mechanisms and subsequent decay, turbulence in the stably stratified cloudy atmosphere appears intrinsically heterogeneous especially in the vertical. It occurs in thin pancake-like patches, each following a more or less independent life-cycle. Flow parameters commonly used to assess the existence of atmospheric turbulence are the Brunt-Vaisala frequency, N, and the Richardson number, Ri, which are defined by the vertical gradient of potential temperature and the vertical shear of horizontal wind velocity as: and

350

Cirrus

It is generally accepted that for Ri smaller than a critical value (Ricrn = 0.25), initial perturbations or disturbances will grow exponentially. However, this is a local criterion. Ri, as defined above, is often evaluated over extended vertical layers using, for example, standard radiosonde data. In this case, a value of bulk Ri greater than 0.25 does not ensure that Ri less than 0.25 does not exist somewhere in a sublayer within the extended layer. Thus, turbulence generation can occur in flows with bulk Ri larger than RiCTn, although it tends to decay with time because subsequent mixing will destroy the original discontinuity. Considerable turbulence exists if the Ozmidov scale, L0 = (e/7V3)1/2, which corresponds to a balance between inertial and buoyancy effects, is significantly larger than the Kolmogorov microscale, LK = (v3/e)1/4, which corresponds to the smallest scale in a flow at which dissipation takes place, and where v is the kinematic viscosity. In other words, there is room for the development of an extended inertial subrange. The Ozmidov scale is related to the buoyancy length scale, LB = oJN, by a factor of about 10-30 (LB = 10-30 L0; e.g., Bacmeister et al. 1996). Here, GW denotes the standard deviation of the vertical air velocity. Buoyancy prevents larger amplitude vertical motions from developing at vertical scales larger than LB (Lesieur 1990). Thus, in an environment generally characterized by Ri much greater than 0.25, except possibly within shallow, embedded subregions, sources of turbulence will occur only for limited times and in limited spatial areas, and the turbulence generated will tend to decay, especially in the vertical dimension. Thus, intermittent turbulence is expected in the upper troposphere and will predominately consist of quasi-two-dimensional motions, especially at larger (meso) scales. The signatures of an originally well-mixed or distorted field of passive tracers, such as cloud particles, may be left behind in a highly structured appearance. In oceanographic flows, this phenomenon is sometimes called "fossil" turbulence (Gibson 1987). Thus, highly structured cloud features, as might be revealed by lidar or millimeter-radar observations, do not necessarily indicate the structure and intensity of the corresponding flow field at that time. 17.2.2. Energy Spectra and Cascade Cirrus clouds range in horizontal scale from hundreds of meters to hundreds of kilometers. Turbulent flows may contain motions at all scales up to tens of kilometers, and coherent dynamical phenomena, such as gravity waves, also occur within this range. Turbulent energy that is inserted into an environment at a specific scale or over a limited subrange of scales can potentially cascade to smaller (downscale) and/or larger (upscale) scales. Upscale energy transport might originate from initially three-dimensional, smaller-scale turbulence, which, after its decay or collapse, builds up a quasi-two-dimensional mesoscale-turbulence regime (Gage 1979; Lilly 1983). Mixing processes are generally associated with a downscale cascade. Analysis of the spectral (scale) distribution of TKE can be very useful in characterizing and understanding the processes at work in the upper troposphere and in cirrus cloud systems. For example, fundamental cloud processes, such as ice crystal nucleation, may be dominated by effects of a


35 I

specific subrange of the overall flow field given the inherent time constants associated with those processes. Observations of the mesoscale flow field in the upper troposphere have revealed a typical spectral slope of -5/3 for wavenumber energy spectra, E(k), of the horizontal wind components (e.g., Gage 1979; Nastrom and Gage 1985). Lilly's stratified turbulence theory leads to the following spectral relation for the mesoscale:

where ku is the largest wave number in the constant slope range of stratified turbulence and aL is Lilly's universal constant. Only a small percentage of the turbulent energy injected at a scale of the order of the buoyancy length scale, LB, is needed for the development of the stratified turbulence spectrum. A competing explanation of the observed spectra in the mesoscale region that also leads to a theoretical slope of -5/3, considers a saturated gravity-wave spectrum (van Zandt 1982; Sidi et al. 1988). There is no final agreement on the more appropriate interpretation of the available data. Both wave and turbulence processes seem to contribute to the observed mesoscale spectra. At intermediate scales, spectral slopes between -3 and -5/3 can be expected in a stably stratified flow. At the smallest scales, a -5/3 slope and a quasi-isotropic flow often mark the classical inertial subrange of Kolmogorov's turbulence theory, which provides the following relation:

where kB is the buoyancy wavenumber and a*: denotes Kolmogorov's universal constant. The intermediate scales are increasingly influenced by stratification, where turbulent energy is lost significantly as eddies work against buoyancy forces. In an extension of earlier work by Bolgiano (1962), Shur (1962), and Lumley (1964) that predicted fixed spectral slopes for the vertical air velocity spectra of either -11/5 or -3, Weinstock (1978) showed that no universal spectral slope exists for this buoyancy subrange. He derived a complex spectral relation that depends on the flux Richardson number and the local TKE. In a later study, Weinstock (1980) found a tendency for energy cascade processes to generate a spectral hump (peak) at length scales smaller than the "effective" source scale at which a gap (dip) would form. Here, the effective source scale is a length scale characterized by enhanced energy cascading to other scales. Thus, the occurrence of a peak in the energy distribution does not necessarily indicate that this is the predominant scale of the energy source, as the occurrence of significant spectral energy at a particular scale may only reflect the cascade of energy from other scales. Furthermore, a -5/3 slope for energy spectra of horizontal wind components in stably stratified flows does not always indicate inertial subrange turbulence. The spectral regimes mentioned above are illustrated in a schematic shown in figure 17.2. The transition from smaller-scale, three-dimensional turbulence to quasi-two-dimensional (mesoscale) turbulence typically occurs over scale lengths of the order of 2-10 km.

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Cirrus

Figure 17.2. Illustrative representation of power spectral density, S, as a function of the horizontal wave number, k, for the quasi-two-dimensional and the smaller scale threedimensional flow regimes. BSR indicates the buoyancy subrange, and ISR denotes the inertial subrange.

Thus, the interpretation of velocity spectra is not straightforward and many factors need to be considered. Some ambiguity is often present. 17.2.3. Gravity Waves Gravity waves are ubiquitous in the atmosphere (Stewart 1969) and may coexist and interact with turbulence or convective circulations. Gravity waves in the upper troposphere have been reported with wavelengths of a few kilometers up to several tens of kilometers. These scales are comparable to scales observed in cirrus cloud systems (Sassen et al. 1989). Thus, gravity-wave phenomena are often present within cirrus cloud systems and may potentially influence, or be influenced by, cirrus cloud processes. It is important to distinguish gravity waves and turbulence because of their different transport characteristics. The nature of gravity waves and their relation to turbulence is discussed by Dewan (1985), Weinstock (1987), Finnigan (1988), and Fritts and Werne (2000). Convective and dynamical instabilities associated with atmospheric waves are extensively reviewed by Fritts and Rastogi (1985). Gravity waves can arise from synoptic scale phenomena in the upper troposphere (e.g., Uccellini and Koch 1987; Starr et al. 1992), such as geostrophic adjustment processes, as well as from mesoscale processes such as the intrusion of deep convection. Lower tropospheric processes associated with frontal zones or even boundary layer convection can stimulate gravity waves that may propagate into the upper troposphere. The structure of cirrus is also influenced by orography and associated wave-generating processes. Cirrus are more frequent over mountainous regions (Wylie and Menzel 1994; Randall et al. 1996) and transverse (standing) wave patterns and longitudinal structures (streamers) are often observed for hundreds of kilometers downwind of mountain barriers.


353

In measurements, it is often difficult to distinguish the signal due to gravity waves from that due to turbulence, convection, or other processes, especially for in situ observations as from aircraft. Linear gravity waves may be identified in time series data by a nearly 90° phase shift between vertical velocity and temperature which is assessable by cross-spectral analysis. However, the observed signal may be quite complex due to the superposition of various intermittent processes. Separation of the component signals is not straightforward. Moreover, the sample is often statistically inadequate, especially for the longer wavelength waves. The difficulty is further compounded by the tendency for gravity waves to occur intermittently in small groups of variable frequency whose alignment with the aircraft flight track is uncertain. Remote sensing observations are only now beginning to be explored for detection and quantification of wave signals in cirrus cloud systems. The possible contribution of gravity waves to mesoscale energy spectra and velocity variances of horizontal wind components has already been discussed and is believed to be significant in many cirrus cloud data sets. 17.3. Measurements and Studies

Observations of turbulence in cirrus have been made by specially instrumented aircraft using either five-hole pressure probes or wind vanes mounted on nose booms or radomes in combination with data from an inertial navigation system to derive the three velocity components. Fast-response temperature measurements have been obtained from Rosemount temperature sensors, and fastresponse humidity measurements have occasionally been made using Lyman-oc hygrometers. The basic principles of airborne turbulence measurements are outlined by Lenschow (1986). The uncertainties of in situ observations, especially in weak turbulence as often expected at cirrus level, are discussed by Quante et al. (1996) and Chan et al. (1998). Weak turbulence is especially challenging due to instrumental limitations. The difficulties in adequately determining the mean mesoscale vertical motion in cirrus from airborne measurements, which is typically a few centimeters per second, are described by Gultepe et al. (1990). Data sampling rates used during reported airborne measurements range between 1 Hz and 100 Hz. In most cases, a sampling rate of 1 Hz is not adequate to resolve inertial subrange turbulence. This affects the related estimation of dissipation rates and the determination of the flow isotropy. There are relatively few studies that report on analysis of turbulence in cirrus clouds. Most are presented as case studies. In an early investigation, Heymsfield (1975) explored the formation and maintenance of cirrus uncinus clouds and explained the observed phenomena/structure dynamically as a result of wind shear and convective activity. The measurements could not directly resolve smaller scale turbulence. The work by Pinus and Litvinova (1980), Dmitriev et al. (1984,1986), Ermakov et al. (1984), Sassen et al. (1989), Flatau et al. (1990), Smith et al. (1990), Gultepe and Rao (1993), Gultepe and Starr (1995) and Demoz et al. (1998) assess dynamics and turbulence in cirrus based on data with 1-Hz resolution and therefore resolving length scales down to about 150m. Studies by

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Table 1 7. 1 . Time and location of major observational programs for which turbulence measurements in cirrus have been evaluated Campaign Dedicated flights

Year/month (season)

FIRE

Spring-autumn 1978-1980 Oct.-Nov. 1981 Mar.-Apr. 1982 Oct. 1986

ICE

Sept.-Oct. 1987

ICE

Sept.-Oct. 1989

FIRE II

Nov.-Dec. 1991

EUCREX CART RCS

Sept.-Oct. 1993 April 1994

SUCCESS

April 1996

Dedicated flights

Location

Reference

European and far eastern part of former USSR Vologda, Northern part of former USSR Wisconsin, Minnesota, USA North Sea, German Bight, Germany North Sea, German Bight, Germany Coffeyville, Kansas, USA Prestwick, Scotland Lamont, Oklahoma, USA Lamont, Oklahoma, USA

Ermakov et al. (1984) Dmitriev et al. (1984, 1986) Flatau et al. (1990) Gultepe and Starr (1995) Quante (1989) Quante et al. (1990) Quante and Brown (1992) Gultepe et al. (1995) Smith and Jonas (1997)

Demoz et al. (1998)

Quante (1989), Quante et al. (1990), Quante and Brown (1992), Gultepe et al. (1995) and Smith and Jonas (1996) use high resolution data (sampling rates greater than 20 Hz to resolve spatial scales of less than 10m), capable of resolving inertial subrange turbulence. Dmitriev et al. (1986) and Quante and Brown (1992) compare turbulence in different types of cirrus clouds. Pinus and Litvinova (1980) and Ermakov et al. (1984) compare turbulence in cirrus to that in other types of stratiform clouds within the troposphere. The major observational programs for which turbulence measurements in cirrus have been evaluated along with relevant references are listed in table 17.1. Results from selected studies are described in the next section.

17.4. Survey of Results

With the exception of the studies by Dmitriev et al. (1984,1986) and Ermakov et al. (1984), who published mean statistical quantities and energy spectra averaged over many cases, studies of in situ turbulence measurements in cirrus have been mainly discussed in relation to the specific meteorological situation encountered. Here, we provide a survey of the available results rather than focusing on individual case studies. 17'.4.1. Mean Flow Characteristics Considerable vertical shear of horizontal wind velocity and/or direction, at least over portions of the cloud vertical extent, is reported in most studies. The most


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intense shear is found in jet stream cirrus, with magnitudes up to lOm/skm" 1 (e.g., Quante and Brown 1992; Demoz et al. 1998). In almost all cases, the overall thermal stratification of the cloud layer is stable where calculated bulk Richardson numbers were much larger than 1, in agreement with the results of Starr and Cox (1980) based on analysis of more than 3600 radiosonde ascents through upper tropospheric clouds. Example profiles of horizontal wind speed and Ri for different types of cirrus selected from ICE/EUCREX missions are shown in figure 17.3. Shear zones (speed and/or direction; the latter not shown) are evident in all cases. Typical values for the mean static stability expressed as potential temperature gradients here ranged from 4 K/km to 7 K/km, rarely dropping below 2K/km. In some cases, very stable layers were observed above cloud top with potential temperature gradients up to 10 K/km. Brunt-Vaisala frequencies ranged between 0.005/s as a lower limit and 0.5/s for the stable regions (Quante 1989; Smith and Jonas 1996). The Ri profile for the ICE-207 mission indicates the possibility of shear generation. Based on the overall stratification and bulk Ri of cirrus cloud layers, it might be concluded that shear generation of turbulence is somewhat uncommon and that convection is rare in cirrus clouds. However, cirrus cloud systems often contain mesoscale patches with a highly cellular appearance at small scales over some limited portion of their vertical extent (Sassen et al. 1990). Corresponding evidence for shallow embedded layers (-100 m) with approximately neutral (icepseudoadiabatic) stratification is not uncommon when high vertical resolution sonde data or airborne profiles are available (Gultepe and Starr 1995; Demoz

Figure 17.3. Profiles of horizontal wind velocity and Richardson number for different types of cirrus selected from ICE/EUCREX cases. The scale for the Richardson number has been cut off at Ri = 10. Cases are classified as jet stream cirrus (ICE 207), frontal cirrus (ICE 212), cirrus in upper-level trough (ICE 216), jet front cirrus (ICE 217), and convective cirrus associated with an occlusion (EUCREX 108). Only ICE 207 (base 7km; top 9.3km) and EUCREX 108 (base 6.5km; top llkm) not totally in cloud.

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Cirrus

et al. 1998). This indicates that small-scale convective processes are likely to be active. It might be assumed that shear instabilities are also likely in such layers. However, lidar and millimeter-radar observations do not usually show tilted cells in these shallow cirrus-generating layers as might be expected in the presence of vertical wind shear (e.g., Sassen et al. 1995). Evaluation of Ri at this scale has not been generally possible. Wind profiles from conventional sonde data typically do not adequately resolve the wind profile as significant vertical averaging must be used in processing the raw data to suppress noise. Moreover, typical airborne in situ sampling patterns usually emphasize vertical profiles via a series of extended horizontal flight legs at intervals of 500m or more in the vertical, as seen in figure 17.3, with rapid ascents/descents during turns between the selected flight levels. Spiraling ascents/descents have also been used. Proper separation of the wind field into its component parts is problematic during rapidly changing aircraft altitude, and the wind data are usually disregarded during these maneuvers. Thus, the required high vertical resolution wind profiles are not generally available. If shallow convective layers are commonly present within cirrus, they provide a source for gravity-wave generation in adjoining stable layers at a scale governed by the depth of the convective layer. Cell widths generally range from hundreds of meters to 1-2 km. Boundary layer studies indicate that such cells should exhibit an aspect ratio on the order of 5-7, which is consistent with the inference of shallow generating layers. Gravity-wave activity can also be stimulated at larger scales, governed by the stability (TV) and depth of the adjoining layers. Another interesting consequence of shallow, unsheared generating layers is that the vertical wind shear in the regions immediately bounding the convective layer would be larger than estimated from bulk (low vertical) resolution data, and thus the possibility for shear generation of turbulence may be underestimated there. Wave breaking may also be enhanced as a result. The source of such shallow apparently convective layers is uncertain. Modeling studies show that cloudrelated processes such as latent and radiative heating/cooling can tend to destabilize layers within and around a cloud layer. However, differential advection associated with larger scale dynamical processes or mesoscale stratified turbulence could also lead to the formation of such structures. Given that intermittent turbulence is commonly observed in cirrus clouds, it must be concluded that mechanical and/or buoyancy generation mechanisms are present. However, the overall stability and the shallowness of the apparent generating layers results in a turbulence field that is usually in a perpetual transition between sporadic generation of TKE and its damping or dying out by stratification, leaving sometimes a highly structured patchy ice cloud field behind (fossil turbulence). Buoyancy lengths derived from ICE/EUCREX data range from 5m to 50m for regions with low to high turbulence intensity, respectively. Maximum values observed for different types of cirrus are listed in table 17.2. The numerical values are calculated from high-pass filtered vertical air velocity time series and the Brunt-Vaisala frequency for the appropriate height interval. Cut-off wavelengths of 2 and 5 km have been used and result in almost the same values for LB when segments obviously influenced by gravity waves are excluded. The buoyancy


357

Table 1 7.2. Maximum buoyancy length scales for different types of cirrus observed during ICE/EUCREX as deduced from rms values of the high pass-filtered (2 km and 5 km cutoff) vertical velocity and the layer mean Brunt- Vaisala frequency Buoyancy wavelength (m) Type of cirrus Jet stream Occlusion (convection) Jet front Frontal Upper-level trough

2km

5km

38 20 15 9 6

42 24 20 10 8

length scale marks the upper limit of a possible inertial subrange and is therefore an important quantity influencing the selection of an appropriate grid size in large eddy models used to simulate cirrus cloud processes. 17.4.2. Turbulence Statistics and Fluxes Examination of «', v', w', and 6' time series along horizontal flight legs flown through cirrus reveals an inhomogeneous appearance in nearly all cases. Thus, calculation of representative turbulence statistics is rather difficult. Figure 17.4 presents a vertical cross-section of a wind and temperature field together with

Figure 17.4. Vertical cross-section of wind (dashed lines; m/s) and temperature (solid lines; °C) fields in a jet stream cirrus observed on November 11, 1981 (from Dmitriev et al. 1984). Cross-hatched areas represent regions of dense cirrus; wavy lines mark zones of relatively intense turbulence (ov,w > 0.1 m/s).

358

Cirrus

the location of turbulent zones in jet stream cirrus as observed by Dmitriev et al. (1984). Regions of dense cirrus are marked by cross-hatched areas. The wavy lines indicate sections along flight legs where more intense turbulence was encountered; the criterion was a local standard deviation of 0.1 m/s to be exceeded simultaneously by ov and ow. The turbulent zones are not necessarily found in the cloudy parts and are distributed intermittently within the flow, a typical phenomenon for cases with shear-induced turbulence. It is also of note that, although cloudy areas might be deemed to have a stable thermal stratification based on an assessment of lapse rate over the entire vertical extent of a cloudy region, shallow layers exhibiting unstable, or nearly so, thermal stratification are also evident (e.g., along -40°C isotherm and along the -47°C and -49°C isotherms in the left-most 50km of the analysis), consistent with the ideas discussed in section 17.4.1. Individual vertical air velocity time series are shown in figure 17.5. The examples chosen are from a jet stream cirrus case (fig. 17.5a) and a case with embedded convective cells (fig. 17.5b). The length of the flight legs was about 100km. The inhomogeneous and dissimilar appearance of the w time series is obvious. In the very stable layer just above cloud top (fig. 17.5a), a region of distinct wave motions is evident, and high-frequency turbulent fluctuations are minimal. The amplitude of the waves approaches Im/s. Yet, considerable turbulence was encountered on a flight leg flown only a few hundred meters below this level (fig. 17.5b). Velocity fluctuations there reached values up to ±2 m/s in the most

Figure 17.5. Time series of vertical air velocity as measured by the FALCON aircraft (100 Hz sampling) along flight legs in (a) jet stream cirrus, above cloud top at a flight altitude of 9.5km (upper curve) and in the cloud-top region at an altitude of 8.9km (lower curve) for mission ICE 207 (from Quante and Brown 1992) and in (b) cirrus associated with an occluded frontal system encountered during mission EUCREX 108 (courtesy of M. Quante).


359

intense region and were mainly produced by a wave-breaking event. Less intense w-fluctuations, but still patches of considerable turbulence, are seen in figure 17.5b.This leg was flown in convectively active cirrus associated with an occluded upper layer front. The most intense turbulence reported in cirrus, with vertical air velocity fluctuations up to 5-6 m/s, have been measured in lee-wave lenticular cirrus (A. Heymsfield, personal communication, 1996). Velocity variance or the standard deviation of u', v', and w' can be used as indicators of turbulence intensity. However, reported values of these quantities are sensitive to the methods used to separate small-scale turbulence from trends, mesoscale variations and waves in the flow which might otherwise dominate the statistics. Researchers use different filters and filter limits (cut-offs). Also, the specific location of an analyzed segment along a flight path may have a strong impact on the results, as evident from the preceding discussion. Typical standard deviations of the horizontal velocity components for high-pass filtered data (2-km cutoff) vary from 0.1 m/s in the calmer regions found in all types of cirrus to 0.4m/s in turbulent zones within jet stream cirrus. Ratios of o^ to ou>v generally range from 0.1 to 0.4, indicating a high degree of anisotropy. However, higher ratios of about 0.7 may be found in jet stream cirrus and cirrus dominated by convective activity (e.g., Quante and Brown 1992; Gultepe and Starr 1995; Smith and Jonas 1996). The highest reported values of the variance ratios are found inside clouds. Dmitriev et al. (1986) calculated mean vertical profiles of velocity and temperature standard deviations from all their observations of cirrostratus clouds observed during several summer experiments. They found that vertical profiles of GV>W and o>, in general, behave similarly, with maximum values occurring around the center of the cloud layers. However, they also found that turbulence again increases above cloud top. This may indicate contamination of their statistics by wave activity, as similar results have not been reported by other groups. Some studies have examined the turbulent fluxes of heat and moisture. Sampling issues make the derivation of statistically significant mean values extremely difficult. Nevertheless, the variability of the correlation products can provide useful information. Time series of eddy potential heat fluxes at different altitudes within a cirrus cloud in a less stable environment, taken from Gultepe and Starr (1995), are shown in figure 17.6. In this case, the mean fluxes are close to zero. Again, specific segments of a flight leg can yield a different assessment. Notable deviations from the mean build up a pattern, which is suggestive of small-scale convection within a mesoscale updraft-downdraft couplet. Gultepe et al. (1995) evaluated sensible heat and latent heat fluxes for additional cirrus cases where the fluxes were separated into small and larger scale components, as well as into contributions from upward and downward moving air. The magnitude of the derived heat flux was comparable to that observed in marine stratocumulus clouds. Based on observations of a cirrus case during the 1986 FIRE campaign, Gultepe and Rao (1993) estimated components of the moisture and heat budgets for a cirrus cloud field. They concluded that advection was a dominant process in determining the budgets for that case. Turbulent heat and moisture fluxes were significant only in the lower levels of the cloud. However, the authors

360

Cirrus

Figure 17.6. Example of time series of eddy potential heat flux for different altitudes in a cirrus observed on October 19,1986 during FIRE; triangles indicate upward air motion, downward motions are denoted by dots (from Gultepe and Starr 1995).

reported large error bars for the turbulence terms (about 50%) in the budget equations. 17.4.3. Spectral Characteristics Power-spectral analysis Power spectra of velocity components are calculated using fast Fourier transform (FFT) or maximum entropy methods. Spectra for u and v at longer wavelengths (a few kilometers to about 100km) tend to show a -5/3 slope, in accordance with the theory of stratified turbulence (section 17.2.2). Field experiments generally do not sample the longer scales adequately; flight legs of 100km, or less, are typical. However, extensive data collected on commercial airliners during the Global Atmospheric Sampling Program (GASP) contain an excellent sample for the larger scales. The GASP data analysis is described by Nastrom and Gage (1985). Additional information is given by Flatau et al. (1990). Results are shown in figure 17.7, where the power spectral density for the u component is compared for in-cloud (longer than 50s) flight segments and cloud-free air. The -5/3 behavior at longer wavelengths is apparent. The spectral peaks at 8 and 16km are due to the inertial navigation system of the aircraft, and a steeper slope is seen at smaller scales. The smaller scale inertial subrange is not resolved by these data. An important result is that the energy density is greater at all resolved scales for the in-cloud data in comparison to the clear-air observations. This indicates that the upper-level ice clouds were associated with sources of mesoscale variability


361

Figure 17.7. Power spectral density of the w-velocity component versus wave number and wavelength. The analyzed aircraft data were gathered during the Global Atmospheric Sampling Program. The dotted line marks a spectral slope of -5/3 (spectral data provided by G. Nastrom).

(such as the jet stream, elevated frontal zones, or deep convection) and/or themselves contribute to enhancing the variability on these scales. In the transition region (scale lengths from 2km to about 10km) between the more three-dimensional regime at small scales and the quasi-two-dimensional turbulence regime at larger scales, spectra of M, v, and w often exhibit a great deal of structure. The many intermediate spectral peaks and slopes suggest a mixture of wave influence, local convective activity, and sporadic wave-breaking events. On some occasions, spectral slopes around -3 indicate the existence of a buoyancy subrange. Sassen et al. (1989) provide good examples from three case studies for the variability of spectral behavior in the transition range, revealing the ubiquitous mesoscale organization of the cloud systems. At shorter wavelengths (some hundred meters and less), spectral slopes again tend to approach a -5/3 roll-off, pointing toward the existence of an inertial subrange at smaller scales where a tendency toward isotropy in the flow is also found. Figure 17.8 shows spectra in the active developing turbulence of a wave-breaking event and in the region of less intense background turbulence nearby at the same height level. The w-spectrum is noticeably damped at the transitional frequencies for the less intense (decaying) turbulence. In the active turbulence-generation region (fig. 17.8a), the spectra show higher energy levels and indicate isotropy up to a wavelength of roughly 500m (-0.3 Hz). These relatively large quasi-isotropic eddies are found only occasionally in cirrus, mostly in situations with strong wind shear. In contrast, isotropy extends only to a scale of about 20m (~8Hz) in the less intense background turbulence region (fig. 17.8b). As a synopsis, a comparison of w-spectra for selected regions with highest turbulence intensity in different types of cirrus is shown in figure 17.9. The power spectral densities span about 2.5 orders of magnitude for the different cases at

362

Cirrus

Figure 17.8. Power spectra of the u, v, and w air velocity components measured by the FALCON aircraft during mission ICE 207 for a (a) wave-breaking event and (b) in background turbulence. For clarity, the power spectral densities (PSDs) have been averaged logarithmically over the spectral bins (courtesy of M. Quante).

the frequency of IHz, corresponding to a wavelength between 140m to 180m. By far, the highest energy level was found for the jet-stream cirrus case. In general, identification of dominant processes responsible for observed cloud structure from aircraft measurements alone is not an easy task. This is due to the limited coverage that does not yet allow an adequate depiction of the three-dimensional flow field nor a systematic characterization of the full life cycle of elements within mostly heterogeneous cirrus cloud fields. Figure 17.10 provides an example for spectra marking distinctly different dynamical regimes for

Figure 17.9. Power spectral density of vertical velocity for different types of cirrus encountered during ICE/EUCREX and ARM RCS. Only quasi-homogeneous segments (20-50 km long) with highest turbulence intensity for each case are shown; results are logarithmically averaged. (Courtesy of M. Quante; ARM data provided by G. Mace.)


363

Figure 17.10. (a) Ice water content as measured by Particle Measurement System optical probes and the vertical air velocity component, w, along a flight leg at 8.3km height by the Hercules C-130 during mission ICE 207. (b) Power spectral densities of w for the indicated segments (courtesy of M. Quante; IWC data provided by PR. A. Brown of UK Meteorological Office.)

segments within the same cloud system with different ice water content (IWC) and size spectra (not shown). The corresponding IWC and w time series are also shown. For segment b (320-450s, about 19km), the spectral energy at all frequencies exceeds that for the other segments by about one order of magnitude where the origin of the strong turbulence is most likely again due to a breaking wave encountered during an approach into a more dense jet-stream cirrus field. The spectral slope for this segment is slightly steeper than -5/3, indicating that the turbulence cascade was not yet fully developed. The highest vertical velocity fluctuations are aligned with a local peak in IWC, suggesting that turbulence was strongly influencing the cloud development. The energy-containing frequency at about 0.075 Hz corresponds to a length scale of about 2km, the depth of the shear layer. Highest IWC is found in the region with moderate intensity turbulence that maintains a mixed layer—likely a result of previous mechanical generation events. Smith and Jonas (1996) found just the opposite behavior

364

Cirrus

Table 1 7.3. Typical values (ranges) of the dissipation rate in cirrus and in clear air reported by different groups Dissipation rates (e) (m2/s3) In cirrus 0.9xlO-4...1.6xlO~4 1 x HT4 . . . 6 x ICT4

0.4 x HT4 . . . 2.5 x ID"4 (0.01 x 10-4) . . . 8 x 10-4 In clear air 1 x 10~5

Cirrus

The Story of Cirrus Flux

Cirrus Flux. Der Junge, den es nicht gab

Chrysler Cirrus, Dodge Stratus, Plymouth Breeze, 1995-2000

Cirrus

The Story of Cirrus Flux

Cirrus Flux. Der Junge, den es nicht gab

Chrysler Cirrus, Dodge Stratus, Plymouth Breeze, 1995-2000

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