Polynyas: Windows to the World

POLYNYAS WINDOWS TO THE WORLD Elsevier Oceanography Series Series Editor: David Halpern (1993–) FURTHER TITLES IN TH...

Author: Walker O. Smith Jr. | David Barber

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POLYNYAS WINDOWS TO THE WORLD

Elsevier Oceanography Series Series Editor: David Halpern (1993–)

FURTHER TITLES IN THIS SERIES For more information see our website: http://www.elsevier.com/locate/eos 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73.

Tan Weiyan Shallow Water Hydrodynamics R.H. Charlier and J.R. Justus Ocean Energies, Environmental, Economic and Technological Aspects of Alternative Power Sources P.C. Chu and J.C. Gascard (Editors) Deep Convection and Deep Wafer Formation in Oceans P.A. Pirazzoli and J. Pluet World Atlas of Holocene Sea-Level Changes T. Teramoto (Editor) Deep Ocean Circulation – Physical and Chemical Aspects B. Kjerfve (Editor) Coastal Lagoon Proceses P. Malanotte-Rizzoli (Editor) Modern Approaches to Data Assimilation in Ocean Modeling J.H. Stel, H.W.A. Behrens, J.C. Borst, L.J. Droppert and J.P. van der Meulen (Editors) Operational Oceanography D. Halpern (Editor) Satellites, Oceanography and Society P. Boccotti Wave Mechanics for Ocean Engineering Richard E. Zeebe and Dieter Wolf-Gladrow CO2 in Seawater: Equilibrium, Kinetics, Isotopes N.C. Flemming (Editor-in-Chief) Operational Oceanography: Implementation at the European and Regional Scales V.C. Lakhan (Editor) Advances in Coastal Modeling G.J. Goni and P. Malanotte-Rizzoli (Editors) Interhemispheric Water Exchange in the Atlantic Ocean H. Dahlin, N.C. Flemming, K. Nittis and S.E. Petersson (Editors) Building the European Capacity in Operational Oceanography Á. Borja and M. Collins (Editors) Oceanography and Marine Environment of the Basque Country A.G. Kostianoy, J.C.J. Nihoul and V.B. Rodionov Physical Oceanography of Frontal Zones in the Subarctic Seas W. Fennel and T. Neumann Introduction to the Modelling of Marine Ecosystems H. Kawahata and Y. Awaya (Editors) Global Climate Change and Response of Carbon Cycle in the Equatorial Pacific and Indian Oceans and Adjacent Landmasses

Elsevier Oceanography Series, 74

POLYNYAS WINDOWS TO THE WORLD

Edited by WALKER O. SMITH, JR. Virginia Institute of Marine Sciences, College of William & Mary, Gloucester Pt., USA and DAVID G. BARBER Department of Geography, University of Manitoba, Winnipeg, Canada

Amsterdam – Boston – Heidelberg – London – New York – Oxford Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo

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Contents List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiii

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xv

1

The Role of Sea Ice in Arctic and Antarctic Polynyas D.G. Barber and R.A. Massom

1

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 2. Polynyas, Sea Ice and Climate Variability/Change . . . . 3. An Inventory of Arctic and Antarctic Polynyas . . . . . . 3.1. Northern Hemisphere Polynyas . . . . . . . . . . . 3.2. Southern Hemisphere Polynyas . . . . . . . . . . . 4. Detailed Case Studies . . . . . . . . . . . . . . . . . . . . 4.1. The North Water (NOW) Polynya (NW Greenland) 4.2. The Mertz Glacier Polynya (East Antarctica) . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

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Physical Oceanography of Polynyas W.J. Williams, E.C. Carmack and R.G. Ingram 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2. Polynya Formation Processes . . . . . . . . . . . . . . . 2.1. Mechanically Forced Polynyas . . . . . . . . . . . 2.2. Convectively-Forced Polynyas . . . . . . . . . . . 2.3. Feedback Processes Within Polynyas . . . . . . . 2.4. Marginal Ice Zone ‘Polynyas’ . . . . . . . . . . . 3. Physical Oceanography . . . . . . . . . . . . . . . . . . 3.1. Water Mass Transformation Within Polynyas . . . 3.2. Transport of Dense Water Away from the Polynya 4. Biological Importance of Polynyas . . . . . . . . . . . . 5. Future Research in a Changing Environment . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

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Polynya Modelling A.J. Willmott, D.M. Holland and M.A. Morales Maqueda v

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vi

Contents 1. 2. 3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flux Model Approach . . . . . . . . . . . . . . . . . . . . . . . . . Parameterisations for the Collection Thickness H . . . . . . . . . . Opening of a One-Dimensional Polynya . . . . . . . . . . . . . . . 4.1. Constant Collection Thickness . . . . . . . . . . . . . . . . . 4.2. Parameterisation (3.2) for H . . . . . . . . . . . . . . . . . . 4.3. Parameterisation (3.3) for H . . . . . . . . . . . . . . . . . . 4.4. Discussion of the Opening Models . . . . . . . . . . . . . . 5. Two-Dimensional Steady-State Solutions . . . . . . . . . . . . . . 6. Two-Dimensional Steady-State Solutions with Ocean Currents . . 7. Unsteady 2-Dimensional Flux Models . . . . . . . . . . . . . . . . 8. Polynya Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. A Polynya Flux Model with a Prognostic Frazil Ice Concentration 10. Coupled Atmosphere–Polynya Flux Model . . . . . . . . . . . . . 11. General Circulation Modelling Approach . . . . . . . . . . . . . . 12. Ice GCM Equations . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1. Mass Conservation . . . . . . . . . . . . . . . . . . . . . . . 12.2. Momentum Conservation . . . . . . . . . . . . . . . . . . . . 13. Numerical Methods in Ice GCMs . . . . . . . . . . . . . . . . . . . 14. Regional Ice GCM Applications . . . . . . . . . . . . . . . . . . . 14.1. Coastal-Ocean Polynya . . . . . . . . . . . . . . . . . . . . . 14.2. Open-Ocean Polynya . . . . . . . . . . . . . . . . . . . . . . 15. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

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Meteorology and Atmosphere–Surface Coupling in and around Polynyas P.J. Minnett and E.L. Key 1. Introduction . . . . . . . . . . . . . . . . . 1.1. Polynya Formation . . . . . . . . . . 2. Measurements of Meteorological Variables 2.1. Winds . . . . . . . . . . . . . . . . . 2.2. Surface Temperature . . . . . . . . . 2.3. Surface Humidity . . . . . . . . . . . 2.4. Profiles . . . . . . . . . . . . . . . . . 2.5. Clouds . . . . . . . . . . . . . . . . . 2.6. Aerosols . . . . . . . . . . . . . . . . 2.7. Radiation . . . . . . . . . . . . . . . 3. NWP Models . . . . . . . . . . . . . . . . . 4. Surface Interactions . . . . . . . . . . . . . 4.1. Radiative Fluxes . . . . . . . . . . . 4.2. Turbulent Fluxes . . . . . . . . . . . 5. Outlook for the Future . . . . . . . . . . . . 6. Summary . . . . . . . . . . . . . . . . . . . Appendix A: Acronyms . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .

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87 89 92 94 94 95 95 95 96 98 98 102 106 107 108 108 108 111 113 116 117 119 121 122 122

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127 129 130 131 134 136 137 139 143 145 146 147 148 150 152 152 153 154

Contents 5

vii

Gas Fluxes and Dynamics in Polynyas L.A. Miller and G.R. DiTullio

163

1. Introduction . . . . . . . . . . . . . . . . . . . . . 2. Gases in Cold, Salty Water . . . . . . . . . . . . . 3. Carbon . . . . . . . . . . . . . . . . . . . . . . . . 3.1. The Ross Sea . . . . . . . . . . . . . . . . . 3.2. The North Water . . . . . . . . . . . . . . . 3.3. The Northeast Water . . . . . . . . . . . . . 3.4. Other Polynyas . . . . . . . . . . . . . . . . 4. Sulfur . . . . . . . . . . . . . . . . . . . . . . . . . 5. Methylhalides . . . . . . . . . . . . . . . . . . . . 6. The Future for Air–Sea Gas Exchange in Polynyas Acknowledgements . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . 6

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Biogeochemistry of Polynyas and Their Role in Sequestration of Anthropogenic Constituents M. Hoppema and L.G. Anderson . . . . . . . . . . . . . . . . .

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Physical Control of Primary Productivity in Arctic and Antarctic Polynyas K.R. Arrigo 1. Introduction . . . . . . . . . . . . . . . . . . . . 2. Formation of the Four Major Polynyas . . . . . . 2.1. The NEW polynya (Arctic) . . . . . . . . 2.2. The NOW Polynya (Arctic) . . . . . . . . 2.3. The RSP Polynya (Antarctic) . . . . . . . 2.4. The MGP Polynya (Antarctic) . . . . . . . 3. Role of Physicochemical Properties of Polynyas 3.1. Effect of Temperature . . . . . . . . . . . .

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163 164 166 170 173 175 177 178 180 180 182 182

193

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Antarctic Polynyas . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Weddell Polynya in the 1970s . . . . . . . . . . . . . . . . 2.2. Recurrent Offshore Polynyas . . . . . . . . . . . . . . . . . 2.3. Coastal Polynyas in the Weddell Sea . . . . . . . . . . . . 2.4. Ross Sea and Terra Nova Bay Polynyas . . . . . . . . . . . 2.5. East Antarctic Coastal Polynyas . . . . . . . . . . . . . . . 2.6. Summary and Concluding Remarks for Antarctic Polynyas 3. Polynyas in the Arctic Ocean . . . . . . . . . . . . . . . . . . . . 3.1. Storfjorden Polynya . . . . . . . . . . . . . . . . . . . . . . 3.2. Northeast Water Polynya . . . . . . . . . . . . . . . . . . . 3.3. North Water Polynya . . . . . . . . . . . . . . . . . . . . . 3.4. Cape Bathurst Polynya . . . . . . . . . . . . . . . . . . . . 3.5. St. Lawrence Island Polynya . . . . . . . . . . . . . . . . . 3.6. Laptev Sea Polynya . . . . . . . . . . . . . . . . . . . . . . 3.7. Comparison of the Different Arctic Polynyas . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

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193 195 195 199 201 204 206 208 209 209 210 211 213 214 214 214 215

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viii

Contents 3.2. Effect of Light and Nutrients . . . . . . . . . . . . . . 3.3. Effect of UV Radiation . . . . . . . . . . . . . . . . . 4. Cloud Reduction over Polynyas with High Latent Heat Flux 5. Timing of Polynya Expansion . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Primary Production and Nutrient Dynamics in Polynyas J.-E. Tremblay and W.O. Smith 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 2. Phytoplankton Characteristics of Individual Polynyas 2.1. North Water Polynya . . . . . . . . . . . . . . . 2.2. Northeast Water Polynya . . . . . . . . . . . . . 2.3. Cape Bathurst Polynya . . . . . . . . . . . . . . 2.4. Other Arctic Polynyas . . . . . . . . . . . . . . 2.5. Ross Sea Polynya . . . . . . . . . . . . . . . . . 2.6. Mertz Glacier Polynya . . . . . . . . . . . . . . 2.7. Terra Nova Bay Polynya . . . . . . . . . . . . . 3. Ecological Consequences of Polynya Production . . . 3.1. North Water Polynya . . . . . . . . . . . . . . . 3.2. St. Lawrence Island Polynya . . . . . . . . . . . 3.3. Ross Sea Polynya . . . . . . . . . . . . . . . . . 4. Comparison of Arctic and Antarctic Polynyas . . . . . 5. Summary and Conclusions . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Zooplankton Processes in Arctic and Antarctic Polynyas D. Deibel and K.L. Daly 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Zooplankton in Arctic Ocean Polynyas . . . . . . . . . . . . . . . . . . . 2.1. Species Composition and Abundance . . . . . . . . . . . . . . . . 2.2. Individual and Community Biomass . . . . . . . . . . . . . . . . . 2.3. Individual Feeding Rates, Diet and Community Grazing . . . . . 2.4. Faecal Pellet Production and Vertical Flux . . . . . . . . . . . . . 2.5. Seasonal Energy Storage and Egg Production Rates . . . . . . . . 2.6. Secondary Production and Generation Time . . . . . . . . . . . . 3. Zooplankton of the Southern Ocean . . . . . . . . . . . . . . . . . . . . 3.1. Species Composition and Abundance . . . . . . . . . . . . . . . . 3.2. Individual and Community Biomass . . . . . . . . . . . . . . . . . 3.3. Individual Feeding Rates and Diet . . . . . . . . . . . . . . . . . . 3.4. Faecal Pellet Production and Vertical Flux . . . . . . . . . . . . . 3.5. Seasonal Life History, Energy Storage and Egg Production Rates . 3.6. Secondary Production and Generation Time . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

239 243 243 247 248 248 249 255 256 256 257 257 258 259 261 263 263

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Contents 10

ix

Pelagic Bacterial Processes in Polynyas H.W. Ducklow and P.L. Yager

323

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Overview: Microbial Food Webs in Polar Seas . . . . . . . . 2.1. Methods and Terminology . . . . . . . . . . . . . . . . 2.2. Food Web Structure and Function . . . . . . . . . . . . 2.3. Bacterial Growth in Cold Water . . . . . . . . . . . . . 3. Bacterial Processes in the Ross Sea Polynya (RSP) . . . . . . 4. Bacterial Processes in Greenland Polynyas (NEW and NOW) 4.1. The Northeast Water (NEW) Polynya . . . . . . . . . . 4.2. The North Water (NOW) Polynya . . . . . . . . . . . . 5. Other Polynyas . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Summary and Prospects . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

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Benthic Processes in Polynyas J.M. Grebmeier and J.P. Barry . . . . . . . . . .

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The Impact and Importance of Production in Polynyas to Top-Trophic Predators: Three Case Histories N. Karnovsky, D.G. Ainley and P. Lee 1. Introduction . . . . . . . . . . . . . . . 2. Ross Sea Polynya . . . . . . . . . . . . 2.1. Physical Characteristics . . . . . 2.2. Organic Production . . . . . . . . 2.3. Middle and Upper Trophic Levels 3. North Water Polynya . . . . . . . . . . 3.1. Physical Characteristics . . . . . 3.2. Organic Production . . . . . . . . 3.3. Middle and Upper Trophic Levels

323 326 326 328 330 331 338 338 346 349 349 350 350

363

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. General Polynya Patterns and Processes . . . . . . . . . . . . . . . . . . 2.1. St. Lawrence Island Polynya (SLIP) . . . . . . . . . . . . . . . . . 2.2. Ross Sea Polynya (RSP) . . . . . . . . . . . . . . . . . . . . . . . 3. Means to Assess Interpolynya Differences . . . . . . . . . . . . . . . . . 4. Rate Processes and Their Controls . . . . . . . . . . . . . . . . . . . . . 4.1. Benthic Oxygen Demand . . . . . . . . . . . . . . . . . . . . . . . 4.2. Retention Versus Export of Carbon to the Benthos . . . . . . . . . 4.3. Depth as the Major Factor for Variance in Benthic Carbon Cycling 4.4. Comparison of SLIP and RSP to other Polynyas . . . . . . . . . . 4.5. Comparison of Polynyas to Retreating Marginal Ice Zones and Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

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Contents 4. Northeast Water Polynya . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Polynyas and Climate Change: A View to the Future W.O. Smith and D.G. Barber

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1. Introduction . . . . . . . . . . . . . . . . . . 2. Polynyas and Climate Change: The Arctic . . 3. Polynyas and Climate Change: The Antarctic 4. Conclusions . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . .

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Contributors Ainley, D.G. Anderson, L.G. Arrigo, K.R. Barber, D.G. Barry, J.P. Carmack, E.C. Daly, K.L. Deibel, D. DiTullio, G.R. Ducklow, H.W. Grebmeier, J.M. Holland, D.M. Hoppema, M. Ingram, R.G. Karnovsky, N. Key, E.L. Lee, P. Massom, R.A. Miller, L.A. Minnett, P.J. Morales Maqueda, M.A. Smith Jr., W.O. Tremblay, J.-E. Williams, W.J. Willmott, A.J. Yager, P.L.

(391) (193) (223) (1, 411) (363) (55) (271) (271) (163) (323) (363) (87) (193) (55) (391) (127) (391) (1) (163) (127) (87) (391, 411) (239) (55) (87) (323)

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Foreword Polynyas are large regions of open water and thin ice that recur from year to year at the same geographic locations in both summer and winter. Winter polynyas are astonishing, in that it seems counter-intuitive that large areas of open water and thin ice can exist under conditions of very cold strong winds. Such polynyas are physically impressive natural phenomena, akin to tornadoes, severe storms or flash floods. My first encounter with a winter polynya occurred on a cold windy day in March, south of Nome, Alaska. We had flown by helicopter over the 2-km wide coastal polynya to film the frazil ice plumes that occurred in the open water. We then landed on the first year ice downwind of the polynya, where our objective was to sample the frazil ice thickness at the polynya edge. Standing on the ice surface a few tens of meters from the polynya edge, I realized that the combination of the strong cold winds, blowing snow and ice crystals, and the crashing of the waves breaking on the first year ice meant that our sampling plan was too dangerous to carry out. Because of such hazardous conditions combined with the isolated nature of the polynyas, most winter studies are done using remote sensing, over-winter moorings, or icebreakers. Within the polynya, the presence of thin ice and open water means that a large winter heat exchange can occur between the ocean and atmosphere, and if the water is at the freezing point, the polynya also serves as a strong source of ice and produces a salt flux to the underlying ocean. In Ross and Weddell Seas, the dense water formed within the polynyas interacts with the ice shelves in such a way as to generate the cold Antarctic Bottom Water. Descriptions of such physical processes and their impact on the ocean dominated the early polynya modeling. Biologists then realized that because of solar insolation, the large thin ice regions generated in winter by, for example, the Ross Sea polynya, melted before the surrounding pack ice and provided in nutrient-rich waters, large areas for the early onset of primary production. Studies of this enhanced production at a number of Arctic and Antarctic locations yielded a number of successful multi-disciplinary studies. This book provides the first comprehensive, multidisciplinary look at polynyas, and their interactions with the ocean, atmosphere and biology. It does this through a systematic examination of the geographic distribution of polynyas, how they form, the factors that maintain them, and from the results of a series of field and theoretical investigations, their physical and biological properties. The editors represent both the biological and physical aspects of the field: Walker Smith studies biological interactions within polynyas and David Barber studies their physical properties. The thirteen chapters divide into three themes: the first five discuss the physical properties, the next seven describe the biological, and the last discusses polynyas and global warming. Each chapter includes an introduction for the non-specialist, a description of potential future research, and an excellent bibliography. The physics chapters describe the distribution of the polynyas and the diversity of mechanisms that drive them, the difference between northern and southern hemisphere polynyas, the associated oceanography, the polynya modeling efforts, and the atmospheric coupling and gas exchange across the interface. The biology chapters describe the biogeochemistry, the primary productivity, the nutrient dynamics, the zooplankton bacterial and benthic processes and the role of predators. xiii

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Many of the chapters contain case studies that describe the physics and biology within specific polynyas. In the Arctic, these include the St. Lawrence Island polynya in the Bering Sea, the Northeast Water polynya off northeast Greenland, the North Water polynya in Baffin Bay and the Kashevarov polynya in the Okhotsk Sea. In Antarctica, they include the Mertz Glacier, Ross Sea and Cosmonaut polynyas. The book concludes with a disturbing discussion of the future of polynyas that would occur from global warming, namely that as the air temperatures warm and the amount of sea ice decreases, polynyas will become less prominent and their role in the regional biology and physical oceanography will be reduced. Seelye Martin Washington, DC March 2007

Preface Polynyas have been recognized as unique regions within polar regions—areas with reduced ice cover (relative to the surrounded areas) that confer unusual physical, chemical and biological characteristics and processes, and often enhanced exhibit enhanced rates. Their importance to global processes (e.g., gas ventilation, deep water formation, support of polar food webs) is indisputable, and in recent years a number of national and international programs have sought to understand their features in the context of the entire polar system. A variety of meetings and symposia have been held on these regions in the past two decades, and research within polynyas continues today. Indeed, the idea for the book originated from a symposium in Quebec City, Canada in September, 2001. The meeting was perhaps overshadowed by world events, but its importance was long-lasting. The objective of this volume is to synthesize our knowledge and to suggest avenues of future research. The volume consists of 13 independent papers, covering a wide range of research within polynyas, including physical, meteorological, chemical, biological, and modeling studies. Polynyas are in ways enigmatic, in that they are ephemeral features, and difficult to study in situ. Despite this, they have significance far beyond their geographic confines, and are critically important components of polar systems. It also has been suggested that they may be among the first of polar systems to be impacted by climate change, and as such are convenient regions to monitor the impacts of large-scale change. The encouragement and support of all of those who attended the Quebec meeting, and who greatly helped bring this volume to completion, is acknowledged and appreciated. This book is dedicated to all those who have worked, struggled and loved working in the harsh reality of Arctic and Antarctic polynyas. Walker Smith

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Chapter 1

The Role of Sea Ice in Arctic and Antarctic Polynyas D.G. Barber1 and R.A. Massom2 1 Clayton H. Riddell Faculty of Environment, Earth, and Resources; Canada Research Chair in Arctic System

Science; Centre for Earth Observation Science, 476 Wallace Building, Fort Gary Campus, University of Manitoba, Winnipeg, MB R3T 2N2, Canada 2 Australian Government, Australian Antarctic Division and Antarctic Climate and Ecosystems Cooperative Research Centre, Private Bag 80, c/o University of Tasmania, Hobart, Tasmania 7001, Australia

Abstract Polynyas are persistent and recurrent regions of open water and/or thin ice or reduced ice concentration, tens to tens of thousands of square kilometers in areal extent, that occur within the sea ice zones of both hemispheres at locations where a more consolidated and thicker ice cover would be climatologically expected. Rather than simply constituting recurrent “windows” in the sea ice, polynyas are profoundly affected by, and intimately linked to, local and even regional ice conditions (i.e., the “icescape”). They respond sensitively to thermodynamic and dynamic forcing by the ocean and atmosphere and entail ecologically important “oases” that enable birds and mammals to overwinter at high latitudes and encourage enhanced primary production in the spring. In this review, we introduce the concept of polynyas from the perspective of the sea ice conditions/processes that define them. We discuss the unique characteristics of polynyas in both polar regions, and assess their possible response/contribution to climate variability and change. An inventory of Northern Hemisphere polynyas is presented, based primarily of satellite data analysis but also on information from the literature and aboriginal peoples. Summary statistics on polynya opening and closing dates are also provided, along with information on the availability of light relative to the seasonal cycles of sea ice. In the Southern Hemisphere, we present an update of an inventory of Antarctic polynyas and discuss how coastal, glacial and deep-ocean processes affect their and distribution. Two important polynyas are examined in more detail, i.e., the North Water (NOW) polynya in the north and the Mertz Glacier polynya in the south. These case studies focus on details of the different physical processes driving their creation, maintenance and dissolution. Each of these polynyas has been the focus of dedicated in situ research programmes in recent years.

1 Introduction Polynyas are important features of the sea ice covers of both polar regions. Their overall importance, not only locally but also globally, lies in their unique characteristics. Polynyas Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74001-6

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© 2007 Elsevier B.V. All rights of reproduction in any form reserved

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D.G. Barber and R.A. Massom

are persistent and recurrent regions of open water and/or thin ice, tens to tens of thousands of square kilometers in extent, that occur at locations where a more consolidated and thicker ice cover would be climatologically expected (Smith et al., 1990; Martin, 2001). They differ from leads in that they recur at approximately the same location each year and persist for periods of weeks to months, with intermittent openings and closings with intermittent openings and closings due mainly to changes in atmospheric forcing. Leads, on the other hand, are linear openings that occur ephemerally on synoptic or shorter time-scales and tend not to recur in the same location (an exception being flaw leads—see below). While Arctic and Antarctic polynyas are fundamentally similar, striking hemispheric differences exist in the processes and phenomena responsible for their formation and maintenance. Moreover, considerable intra-hemispheric differences occur. These differences strongly reflect the contrasting environmental and particularly sea ice conditions present. Rather than simply constituting recurrent “windows” in the sea ice, polynyas are profoundly affected by, and intimately linked to, local and even regional ice conditions, which we term the “icescape”. In this review, we gather together available information and present some new material to construct the first combined inventory of Arctic and Antarctic polynyas, in an effort to highlight these characteristics. Emphasis is placed on modes of formation and maintenance, illustrated by more-detailed case studies of two key polynyas. Consideration is also given to inter-annual variability, possible causes and impacts of change in polynya characteristics, and the potential contribution and sensitive response of polynyas to climate change and/or variability. We begin this synthesis with an assessment of the setting of polynyas—the sea ice covers of both polar regions. In the Arctic ice forms annually throughout most of the oceanic areas north of the Arctic Circle. Maximum extent occurs around the end of March with an area of about 14 × 106 square kilometres (km2 ), with summer time melt resulting in a minimum of about 7 × 106 km2 towards the end of September (Gloersen et al., 1992). The “southward” growth of the perennial pack and “northward” advance of annual ice from the continental shelves means that changes in areal extent occur most often over the ocean shelves. These regions are the realm of Arctic polynyas. Several modeling studies predict a reduction in sea ice areal extent over the next several decades, resulting in a seasonally ice-free Arctic as early as 2050 (Vinnikov et al., 1999; Flato and Boer, 2001). Observational studies, based on the passive microwave record, confirm these predictions for both rates of reduction and, to a certain extent, geographic location (Rigor et al., 2002; Comiso, 2003a; Drobot and Maslanik, 2003). Parkinson et al. (1999) showed that an average reduction of approximately 3% per decade occurred in the areal extent of Arctic sea ice occurred over the period 1978 to 1998. Major anomalous reductions in summer-ice extent were observed in 1998, followed by record lows in 2002 (Serreze et al., 2003), 2003 (Barber and Hanesiak, 2004), 2004 (Stroeve et al., 2005), with the minimum extent on record being 2005 (M. Serreze, pers. comm.). The Arctic sea ice zone has experienced an associated shortening of the sea ice season over the past 25 years (Parkinson, 2000). Reports of a substantial reduction in Arctic ice thickness (volume), however, are more controversial. While analysis of submarine sonar data suggested a decline in thickness over the past 40 years (Rothrock et al., 1999), this may be due to a sampling bias (Holloway and Sou, 2002). Yu et al. (2004) provide compelling evidence for an overall Northern Hemisphere volume decrease of 32%, most of which resulted from a reduction in thickness of ice over two metres (m) thick. This coincided with an increase in the areal extent of open water and young ice forms of 20–30% (Yu et al., 2004). It is expected that this rate of change will increase due to the ice-albedo feedback mechanism (Curry et al., 1995). The export of sea ice from the

The Role of Sea Ice in Arctic and Antarctic Polynyas

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Arctic basin via Fram Strait may also be increasing as the central Arctic pack becomes more mobile (Kwok et al., 2004). Pressure patterns driven, at least in part, by the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) modes play a role in this export (Kwok et al., 2004). In the Southern Hemisphere sea ice areal extent varies annually by a factor of about 5, from a maximum of 18–20 × 106 km2 in September–October to 3–4 × 106 km2 each February (Gloersen et al., 1992; Comiso, 2003b). As such, the Antarctic pack comprises a high proportion (∼80%) of first-year ice. It is therefore largely a seasonal sea ice zone, with areas of perennial ice limited to the Ross, Amundsen and western Weddell Seas. A major difference between the Southern and Arctic Oceans is that the former is unrestricted by land masses equatorward and is bounded at its southern extremity by a vast frozen continent. Its sea ice cover extends from a maximum southerly extent of about 75◦ S northwards as far as approximately 55◦ S at maximum extent, with a meridional width ranging from a few hundred kilometres in the Indian Ocean sector to approximately 1600 km in the Weddell Sea (Gloersen et al., 1992). The largely-divergent drift behaviour of the Antarctic pack combines with relatively high vertical ocean-heat fluxes (Gordon and Huber, 1990; Martinson, 1993) to produce an ice cover that is lower in concentration and thinner on average than in the Arctic, i.e., approximately 1 vs 3 m (Dieckmann and Hellmer, 2003). A significant hemispheric contrast also exists in the relative lengths of the annual sea ice growth and decay seasons (Gloersen et al., 1992). The Arctic growth and decay cycle typically follows a symmetrical pattern with ice-extent maxima and minima occurring approximately six months apart (i.e., in March and September, respectively). That of Antarctic sea ice is asymmetrical, however, with the autumn–winter growth period exceeding the spring–summer decay period (i.e., February–September vs. September–February, respectively). While total Antarctic sea ice extent has increased slightly over the satellite era (i.e., the past 30 years; Zwally et al., 2002; Parkinson, 2004), strong regional contrasts are apparent. The only Antarctic sector to have exhibited a strong negative trend over this period is that to the west of the Antarctic Peninsula (Smith and Stammerjohn, 2001; Comiso, 2003b). This decrease has coincided with an extraordinary regional-scale warming trend of >2◦ C since the 1940s (Vaughan et al., 2003; King et al., 2004). By the same token, a positive ice extent trend has occurred in the Ross Sea (Zwally et al., 2002). De la Mare (1997) inferred a decline of about 25% in the area covered by Antarctic sea ice in summer from the 1950s to 1970s, based on whaling ship locations. While such assertions have been questioned (Ackley et al., 2003), research using methanesulfonic acid (MSA) in ice-sheet cores as a proxy indicator of ice extent suggests that the East Antarctic ice edge may indeed have retreated by >1◦ of latitude since 1950 (Curran et al., 2003). Modeling studies by Wu and Budd (1998) and Wu et al. (1999) also indicate that Antarctic sea ice was more extensive in the last century. As with the Arctic, a complex picture is emerging of atmospheric variability as it affects sea ice distribution. Dominant modes of atmospheric variability occur on a range of scales (Kidson, 1999; Simmonds, 2003; Simmonds and King, 2004), and with teleconnections to lower latitude phenomena (Yuan and Martinson, 2000; Liu et al., 2002a, 2002b; White et al., 2002; Carleton, 2003). On the decadal scale, recent change has been observed in large-scale tropospheric circulation in the Southern Ocean in the form of a strengthening and contraction of the circumpolar vortex, and a concomitant strengthening of the circumpolar westerlies (Hurrell and van Loon, 1994; Thompson and Solomon, 2002; Gillett and Thompson, 2003). This is associated with the Southern Annular Mode (SAM), or Antarctic Oscillation (Fyfe et al., 1999; Gong and Wang, 1999; Hall and Visbeck, 2002). The SAM is analogous to the Arctic Oscillation (AO) and is the dominant mode of variability in atmospheric circulation

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in the Southern Hemisphere. The importance of this and other modes of atmospheric variability on sea ice distribution e.g., the El Niño-South oscillation is becoming increasingly apparent (Kwok and Comiso, 2002; Liu et al., 2004). Changes in these dominant modes of atmospheric variability likely have a profound impact on polynya dynamics and thermodynamics, but one that is only just beginning to be addressed.

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Polynyas, Sea Ice and Climate Variability/Change

Polynyas form as a result of complex interactions of the ocean, atmosphere and sea ice, and are intimately linked to ice conditions, water-mass dynamics/thermodynamics and atmospheric circulation patterns. This is a critical characteristic of polynyas, and one that underlies their potential sensitivity to, and modulation of, climate change/variability. Polynyas are thought to constitute highly sensitive, and informative, windows into polarocean processes and their response to regional- to global-scale climate variability/change. Their use as sentinels of high-latitude environmental change is strongly dependent on our level of understanding of the complex processes responsible for their formation and maintenance and their role in driving and responding to that change. Examination of the suite of forcing variables associated with different polynyas provides us with key information on how oceanic and atmospheric forcing may be changing within particular areas of both polar regions. Polynyas lend themselves well to long-term and focused monitoring in that they typically occur at scales that are suitable for both detailed surface observations and observation from space. Observatories that utilize these polynyas can also tell us a great deal about the complexities of the physical-biological coupling occurring within them. While synoptic- and meso-scale meteorological processes play a fundamental role in coastal polynya formation (Pease, 1987), the impact of longer-term atmospheric modes and the role of ocean-to-atmosphere heat fluxes (Minnett and Key, 2007) are much less well understood, as are feedback effects. An example of the latter is the sea ice–cloud–albedo feedback, whereby clouds emanating from polynyas due to enhanced ocean-atmosphere moisture fluxes (Dare and Atkinson, 2000; Morales Maqueda et al., 2004) create an atmosphere-tosurface radiative flux that ablates sea ice. This in turn enhances water-vapor flux and cloud formation, leading to a positive feedback to further enhance ice melt. Polynyas have generally been broadly categorized into latent- and sensible-heat forms. Latent-heat polynyas occur in areas in which ice is removed from the region of origin by winds and/or ocean currents as it forms. The heat required to balance loss to the atmosphere, and hence maintain the area of “open water”, is provided by the latent heat of fusion of the continually-forming ice (Smith et al., 1990; Lemke, 2001). Production of new ice is also a significant contributor to the downstream advection of latent heat within the polynya and the formation of deep water through thermohaline processes involving brine rejection. Classical sensible-heat polynyas, on the other hand, form where the transport of oceanic heat to the surface by upwelling, vertical mixing or deep-ocean convection prevents ice formation and/or enhances melt of an existing ice cover (Smith et al., 1990). Following Gordon and Comiso (1988) and Morales Maqueda et al. (2004), we broadly categorise polynyas as either shelf water or deep water, given that some have both sensible- and latent-heat components. As their name suggests, deep-water polynyas occur offshore from the continental shelf which is the domain of shelf-water polynyas. Because the ocean-to-atmosphere heat fluxes through a polynya are several orders of magnitude greater than those through the surrounding ice pack in winter, polynyas dominate the regional heat budget and also influence the atmospheric circulation (Kottmeier and


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Engelbart, 1992; Gallée, 1997). Immense heat and salt fluxes in Arctic polynyas play a key role in coupling atmospheric heat loss to regional ice-mass balance and oceanic salt production (Martin et al., 2004). In the Northern Hemisphere, shelf water polynyas in the Chukchi, Bering, Beaufort, and Barents Seas contribute to the maintenance of the cold halocline layer of the Arctic Ocean (Martin and Cavalieri, 1989; Cavalieri and Martin, 1994; Winsor and Björk, 2000). Moreover, Arctic polynyas are important sites of dense shelf-water formation, which contribute to the production of Arctic intermediate and deep water (Martin et al., 1998; Weingartner et al., 1998; Gladyshev et al., 2000; Golovin, 2002; Signorini and Cavalieri, 2002; Winsor and Chapman, 2002). Given these attributes, Arctic polynyas are not only highly sensitive to climate change/variability, but also have the potential to have a profound wider impact on climate. Polynyas are a common feature of the circumpolar Antarctic sea ice zone. While superficially similar to their Arctic counterparts, they differ in a number of important respects, both in terms of their mode of formation and their overall impact. Most Antarctic polynyas are encountered adjacent to the coastline and on the narrow continental shelf (Massom et al., 1998), and are of the latent-heat variety. In simple terms, they form on the downwind side of morphological blocking features in response to divergent ice conditions forced by the prevailing wind field (Zwally et al., 1985). Katabatic winds also play a major role in certain locations and close to the coast. These gravity-driven winds are generated by intense radiative cooling of air masses above the inland ice-sheet plateau, with their strength being derived from down-slope acceleration of the air as they drain seawards (Tauber, 1960). They are persistent in both strength and direction, and are channeled by the ice-sheet topography to typically emerge over the coastal sea ice zone via outlet glacier valleys and ice shelves (Parish and Bromwich, 1989; Yu et al., 2005). Indeed, their strength is largely determined by local orographic conditions (Tauber, 1960), but also involves interaction with patterns of large-scale atmospheric circulation (Parish and Bromwich, 1998). On coming into contact with the ocean surface in polynyas, strong cold winds promote high rates of sea ice formation and also continually advect the new ice away from the polynya leading edge as quickly as it forms. This results in a thin ice cover and substantially higher ice production rates than those that occur in adjacent areas of consolidated sea ice by a factor of ten or more (Cavalieri and Martin, 1985; Zwally et al., 1985; Ushio et al., 1999). Indeed, Antarctic latent-heat polynyas have often been described as “ice factories” of the pack ice zone. Where polynyas are elongated, linear features that occur over continental-shelf regions at the interface between moving pack ice and landfast ice, they are termed flaw/leads (as noted above). Flaw leads form in response to prolonged periods of offshore winds, and tend to remain open more intermittently than most recurrent polynyas. Antarctic examples are found to the north of Dumont d’Urville in Adélie Land (Massom et al., 2001) and off the Prince Olav Coast between approximately 40◦ and 50◦ E (Ishikawa et al., 1996.) In the Arctic the Circumpolar Flaw lead (CFL) polynya system recurs in the East Siberian, Laptev Sea, Kara Sea (Dethleff et al., 1998) and off the Mackenzie Shelf in the southern Beaufort Sea (Barber and Hanesiak, 2004). Generic physical processes encountered in latent-heat polynyas, and related to frazil ice formation, are described by Martin et al. (1992) and Martin (2001). A number of factors determine shelf-water polynya size and shape (see Willmott et al., 2007). Polynya dimension in the cross-wind direction is determined by the configuration of the coastline/blocking feature over which the wind-/current-driven ice divergence occurs (Martin, 2001). Model studies suggest that shelf-water polynya size is controlled on synoptic time-scales by the balance between sea ice production, its downwind export/advection to

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the polynya edge, and the possible upwind “back-accumulation” of the piled-up new ice (Pease, 1987; Ou, 1988; Darby et al., 1995). Intermittent polynya closure occurs on synoptic scales with the cessation of strong winds and/or a reversal of synoptic-wind direction, i.e., by polynya freezing and infilling, respectively (Massom et al., 2001). Inter-annual variability relates to changes in the mean patterns of zonal wind velocity in the coastal zone (Cavalieri and Martin, 1985). Although relatively small compared to the Antarctic sea ice zone overall (in terms of areal extent), coastal shelf polynyas play a disproportionately large role in a range of key physical, biological and biogeochemical processes (Bromwich et al., 1998). For one thing, intense heat loss to the atmosphere through polynya “windows” can range from 200 to >500 W m−2 depending on wind speed and temperature (Fahrbach et al., 1994; Markus et al., 1998; Budillon et al., 2000; Dare and Atkinson, 2000; Roberts et al., 2001). This combines with high ice production and associated brine rejection rates to significantly increase the density of the water column underlying polynyas (Bindoff et al., 2000a). A key factor in this respect is their persistence and recurrence. As a result, Antarctic coastal polynyas play a central role in the formation of dense shelf waters (Gordon et al., 1993; Bindoff et al., 2001). Moreover, a small number of polynyas are key sites of Antarctic Bottom Water (AABW) formation. Recent analysis of available data by Rintoul (1998) suggests that three polynya systems are largely responsible for total global AABW formation—Adélie Land (contributing 24% by volume of the global ocean volume cooler than θ = 0◦ C, where θ is potential temperature), the southern Weddell Sea (68%) and the Ross Sea (8%). The significance of these polynyas is underlined by the fact that Orsi et al. (1999) estimate that AABW occupies 3.5% of the volume of the global ocean, while Worthington (1981) estimates that it affects >41% of the global oceanic volume via advection and mixing. Antarctic polynyas have also been proposed as key sites of deep-ocean ventilation and possibly the sequestration of atmospheric CO2 into the deep ocean (Goodison et al., 1999). Recent modeling studies have confirmed the critical importance of polynyas to the maintenance of global-ocean thermohaline circulation (Marsland et al., in press), and have demonstrated that the presence of open water within modeled sea ice contributes significantly to the sensitivity of the climate response (Grigg and Holbrook, 2001; Wu et al., 2003). As noted above, Antarctic polynyas are themselves likely to be highly sensitive to any change in forcing parameters, related to climate change/variability (Wu et al., 2003). A major concern, based upon recent model projections of global warming, is the possibility of a cessation of thermohaline circulation as a result of changing sea ice conditions (Broecker et al., 1998). Any long-term change in Antarctic polynya behavior has major implications not only for high-latitude physical, biological and biogeochemical processes but also the global climate system, involving complex and poorly understood feedback mechanisms. The wider implications are immense, given that processes occurring in the Southern Ocean have a profound influence on regional and global ocean circulation and climate (Rintoul et al. 2001a, 2001b; Jacobs, 2004). Uncertainties are currently large, however, and the inter-annual variability in polynya contributions is poorly understood. While polynya characteristics potentially contain important signals related to possible global climate change, a major challenge relates to the difficulty in distinguishing long-term trends from short-term variability related to the major modes of atmospheric circulation. This is compounded by the spatio-temporal complexity of the variations and processes involved, and the short-term nature of the satellite data record.


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An Inventory of Arctic and Antarctic Polynyas

In the Northern Hemisphere, polynyas can be broadly categorized into three distinct types: (a) ice bridge polynyas, (b) land bridge polynyas, and (c) ocean strait polynyas. We provide an overview of each below as a means of illustrating the similarities and differences between the various ice conditions (“icescapes”) responsible: (a) Ice bridge polynyas. Recent work on the North Water (NOW) polynya off north west Greenland showed that the presence of an “ice bridge” in Nares Strait is critical to its formation (Wilson et al., 2001). If the ice bridge does not form, then the region continues as a marginal ice zone with advection of sea ice from the Lincoln Sea south through Kennedy Channel and into northern Baffin Bay. In fact, it has been suggested that the NOW polynya actually creates a polynya north of the Nares Strait in the Lincoln Sea when the ice bridge is not formed in the North Water (NOW) polynya region (e.g., Kozo, 1991). When the ice bridge forms, strong atmospheric flow from the north removes ice as quickly as it is formed to create the North Water polynya (Barber et al., 2001). When the bridge breaks in spring, the area again returns to a marginal-ice zone. Processes controlling the formation of this ice bridge are not well understood. It appears, however, to involve a complex interplay between the source of sea ice types available within the Nares Strait and the dynamic and thermodynamic processes acting upon the sea ice. In the case of an ice-bridge polynya, it is important that the downstream region has sufficient room to accept the advection of newly-formed ice. (b) Land bridge polynyas. A process very similar to (a) occurs when a strong atmospheric flow across the ocean surface is bounded by a land “bridge”. The St. Lawrence Island polynya (e.g., Pease, 1987) in the Bering Sea is a good example. The east–west trend of the land provides a bridge against which the strong northerly atmospheric flow advects young ice south faster than it can consolidate. Newly-formed ice then accumulates in the southern (downwind) reaches of the polynya. Feedbacks are generated within the polynya through increasing cloudiness and buoyancy fluxes to enhance both the longwave flux to the surface and the wind flow into the polynya from the north. (c) Ocean strait polynyas. These occur when a strong oceanic (e.g., tidal) flow is present in regions restricted by land such as straits, e.g., in the Canadian Archipelago. A good example of this is the Fury and Hecla Strait polynya at the northern end of Foxe Basin. This polynya is recurrent and exists as long as strong oceanic fluxes keep the immediate downstream area of the polynya (Foxe Basin) clear of ice. If the ice is mobile and can be advected southward, then newly-formed ice is swept into the receptor area downstream. It is also important that the “upstream” source region of ice (in this case the Gulf of Boothia) provides little or no ice to the strait. Thus, an ice bridge also plays an important role in the formation and maintenance of this type of polynya. This polynya categorization is not directly applicable to Antarctic polynyas. Due to the relative absence of multiyear sea ice in the Southern Hemisphere, other “icescape” features play a more important role in polynya formation. These include coastal promontories, icebergs and glacier tongues, with associated fast ice. The latter is perennial in certain regions. Antarctic deep-water polynyas have a dominant sensible-heat component and tend to form in areas where cold and fresh surface waters are separated from underlying warmer, saltier waters by a weak pycnocline (Morales Maqueda et al., 2004). Only three deep-water polynyas occur or have recently occurred in Antarctica—the Weddell Sea polynya (originally

8


centered on the Greenwich meridian and 66◦ S), the smaller Maud Rise polynya (at approximately 65◦ S, 2◦ E) and the Cosmonaut Sea polynya (centered on approximately 65◦ S, 45◦ E). Hypotheses of specific processes responsible for their formation and maintenance are discussed in a later section. In creating this polynya inventory, we used a variety of techniques, including satellite data and, in the Arctic, published literature (including Inuit knowledge) and historical (e.g., whaling) records. Arctic polynya characteristics were derived from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) data using the Polynya Signature Simulation Method (PSSM; Markus and Burns, 1995). This measures sub-pixel scale polynyas using 85- and 37-GHz brightness-temperature data, creating an enhanced resolution of about 6.25 km. This technique takes advantage of the higher resolution at 85 GHz (about 15 km) while using the lower frequency data (at about 30 km resolution) to compensate for the sensitivity of the higher frequency to atmospheric effects. We use these satellite datasets to determine polynya average opening and closing dates, sea ice areal extent, amount of light available at that particular latitude and the role of changing sea ice cover on that available light. The light fields were modeled using a one-dimensional radiative-transfer model coupled to the sea ice concentration and areal extent information generated from the PSSM. The light estimate is based on clear-sky conditions. Some of the polynyas have virtually no published information, other than their existence, while others are too small to be resolved in satellite passive-microwave data. Due in large part to their remoteness, relatively little is known about Antarctic polynyas compared to their northern counterparts, with a few notable exceptions. Our knowledge of their distribution and behaviour is based almost exclusively on analysis of satellite data. In this section, we synthesize these data and summarize available information to underline the over-arching role of sea ice and other ice, i.e., the “icescape”, in the formation and maintenance of each polynya. This inventory cannot be exhaustive, given limitations on the length of this chapter; rather, it represents a compendium of polynya characteristics and driving processes. Additional information on atmosphere-polynya interactions, surface heat and moisture fluxes over polynyas, and observational and modeling studies is provided by Morales Maqueda et al. (2004). 3.1

Northern Hemisphere Polynyas

We were able to identify 61 distinct and recurring polynyas in the Northern Hemisphere (Figure 1). Some are well known, e.g., the North Water and Cape Bathurst polynyas, while others are referenced infrequently within the scientific literature or historical whaling records. In this section, we provide tabular summaries of existing information about each polynya and new information derived from satellite passive microwave data as a means of assessing icecover information and available radiation. Numbers in parentheses refer to the numbers of polynya locations shown in Figure 1. The North Water (NOW) polynya (41) (Table 1) is the largest polynya in the Canadian Arctic and one of the most biologically-productive polynyas in the Northern Hemisphere. Although this polynya is primarily created by latent-heat processes, upwelling of warm water also contributes (Barber et al., 2001; Melling et al., 2001). It has the largest per-unit-area biological production of any waters in the Northern Hemisphere. We provide a detailed synthesis of its function later in this chapter. The St. Lawrence Island polynya (SLIP) (6) (Table 2) is a latent-heat polynya that forms on the southern coast of St. Lawrence Island (Bering Sea) in winter. It is located over a shallow continental shelf, with depths averaging about 50 m. The areal extent is typically


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Figure 1: Distribution map showing the number and names of Northern Hemisphere polynyas detected and identified from (a) an analysis of DMSP SSM/I data using the PSSM method (Markus and Burns, 1995) and (b) a literature review. This listing provides a minimum estimate of the number of recurrent polynyas. Some of these polynyas no longer exist in a fashion analogous to their recent history (e.g., the NEW polynya).

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Table 1: NOW polynya summary statistics. In all tables Stdev = standard deviation, and OW is open water Polynya variables Location (lat/long) Mean opening date Mean closing date Mean opening date/Stdev Mean merging date/Stdev Maximum area Minimum area January OW area July OW area Sea ice types

Sensible heat process

Latent heat process

Mean daily solar insolation

76–79◦ N, 70–80◦ W (estimate from map) November May Week 19.83; Stdev = 1.52 Week 28.92; Stdev = 2.47 Maximum extent in July (ca. 80,000 km2 )/max. spatial extent 20,000 km2 N/A ∼4659 km2 ∼27,1641 km2 In January, the North Water area is almost completely covered with thin drifting ice, 50% of which is 200 W m−2 in 100–200 m deep waters, i.e., sufficient heat to avert ice development N/A N/A N/A N/A ∼1003 W m−2

Table 19: Okhotsk Sea polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process

Latent heat process

Mean daily solar insolation

Head at 55.7◦ N, 149.9◦ E and tail intersects 55.0◦ N at ∼146.5◦ E December March/April N/A Tadpole-shaped with 50 km diameter “head” N/A 73,110 km2 100,044 km2 N/A This polynya also has an oceanic heat source due to upwelling. Tidally-driven heat flux from intermediate layers to surface helps to maintain the polynya Primarily wind-driven. Wind is a substantial influence on the dynamics of the polynya, as seen from the highly variable ice concentrations over the area ∼1753 W m−2

surface gravity drainage winds (after Parish and Bromwich, 1987) in Figure 2 to highlight the clear association. Information on the location, mean areal extent, month of maximum extent and degree of recurrence and persistence is given in Table 22. Available data on dominant modes of formation of each polynya is also included in this table. Primary determinants of polynya formation are coastal configuration, relative to prevailing winds and ocean currents, ocean bathymetry (largely via its impact on grounded iceberg and resultant pack- and fast-ice


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Table 20: Laptev Sea polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Mean opening date/Stdev Mean merging date/Stdev Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation

Centre of Laptev Sea 76.4◦ N, 125.8◦ E Mid-March Remains until the beginning of May. May last longer? Week 16.26; Stdev = 2.90 Week 25.42; Stdev = 2.64 N/A Up to 100 km wide N/A ∼108 km2 ∼116,659 km2 N/A Primarily created by continuous southerly winds throughout the winter ∼1127 W m−2

distributions), and ice-sheet topography as it affects the channeling and outlet emergence of katabatic winds from ice sheet to ocean. Of the 28 East Antarctic shelf-water polynyas identified, all apart from those in Porpoise Bay and adjacent to the Amery Ice Shelf occur on the downwind (typically western) side of islands (e.g., Drygalski, Mill, Bowman and Terra Nova Islands) and coastal/near-coastal protrusions or blocking features (see Table 22 for details and locations), and are driven by prevailing easterly winds poleward of the Antarctic Circumpolar Trough and/or katabatic winds. Blocking features include ice-sheet promontories, ice shelves (e.g., the West, Shackleton and Voyeykov Ice Shelves), iceberg tongues (e.g., the Dalton, Blodgett and Dibble Iceberg Tongues), icebergs grounded on shoals together with areas of fast ice (e.g., at Cape Darnley), and “ice tongues”. An ice tongue forms when a glacier flowing into the sea does not immediately fracture into icebergs, but rather floats into the ocean. Examples associated with large polynyas are the Mertz Glacier (at approximately 67.7◦ S, 145◦ E) and the Drygalski Ice Tongue (at 75.5◦ S, 163.5◦ E). There is a strong relationship between polynya formation and katabatic winds (Figure 2), with at least 18 of the 28 East Antarctic polynyas being associated with outlet confluence zones of these winds. The influence of katabatic winds can extend over 100 km offshore (Adolphs and Wendler, 1995; Wendler et al., 1997), although they typically lose momentum some tens of kilometres from the coast (Bromwich and Kurtz, 1984). Notable examples of katabatic wind-driven polynyas in Antarctica are those in Terra Nova Bay in the Ross Sea (Bromwich et al., 1992; van Woert, 1999a, 1999b), adjacent to the Ross Ice Shelf (Bromwich et al., 1998; Fichefet and Goosse, 1999), in Commonwealth Bay (Adolphs and Wendler, 1995) and adjacent to the Mertz Glacier tongue (Bindoff et al., 2000a, 2001; Massom et al., 2001). Significant variability occurs not only in East Antarctic polynya size but also in the degree of recurrence and persistence (Table 22). Eight of the smaller polynyas occur only occasionally. These are (from west to east), the Lützow-Holm Bay (Syowa), Taylor Glacier, Amery Ice Shelf, Paulding Bay, Porpoise Bay, Blodgett Iceberg Tongue, Ninnis Glacier, and Slava Bay. The mean size of the remaining 20 shelf-water polynyas ranged from about 1000 km2 for the Cape Hudson polynya to about 23,000 km2 for the Mertz Glacier polynya (based upon an ice-concentration threshold of 75%). In general, the larger polynyas tend to

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Table 21: Summary characteristics of several of the smaller polynyas in the Northern Hemisphere Name

Polynya variable

Cumberland Sound

Location (lat/long) Mean opening date/Stdev Mean merging date/Stdev January OW area July OW area Mean daily solar insolation Location (lat/long) January OW area July OW area Mean daily solar insolation Location (lat/long) Mean opening date/Stdev Mean merging date/Stdev January OW area July OW area Mean daily solar insolation Location (lat/long) Mean opening date Mean closing date Maximum area January OW area July OW area Mean daily solar insolation Location (lat/long) Mean opening date/Stdev Mean merging date/Stdev January OW area July OW Area Mean daily solar insolation Location (lat/long) January OW area July OW area Mean daily solar insolation Location (lat/long) Mean daily solar insolation

Frobisher Bay

Roes Welcome Sound

Lincoln Sea

Barents Sea

Kara Sea

Ob Bank

65◦ N, 65◦ W (estimates from map) Week 13.89; Stdev = 2.75 Week 28.16; Stdev = 2.03 ∼34 km2 ∼15,942 km2 ∼1363 W m−2 62.5◦ N, 65◦ W (estimates from map) ∼1014 km2 ∼18,477 km2 ∼1439 W m−2 64◦ N, 88◦ W (estimates from map) Week 48.91; Stdev = 1.49 Week 25.41; Stdev = 1.44 ∼1281 km2 ∼107,107 km2 ∼140 W m−2 82.5◦ N, 55◦ W (estimates from map) November, stabilizing in Jan., mid-Sept./Oct. April 150 × 200 km (golf-club shaped) ∼65 km2 ∼500 km2 ∼986 W m−2 75◦ N, 40◦ E (estimate from map) Week 4.18; Stdev = 5.04 Week 21.87; Stdev = 1.61 ∼752,166 km2 ∼984,779 km2 ∼1205 W m−2 70–80◦ N, 60–80◦ E (estimates from map) ∼1113 km2 ∼240,521 km2 ∼1116 W m−2 80.3◦ N, 14◦ W ∼1012 W m−2

be more stable and exhibit less inter-annual variability (i.e., a higher degree of recurrence). In all cases, polynya maximum areal extent occurs in winter/early spring, varying from June to October. For a circumpolar picture, we draw upon the work of Arrigo and van Dijken (2003a). While the icescapes responsible for polynya formation are not examined in this mainly biological study, it represents the first detailed circumpolar Antarctic polynya inventory. Based upon analysis of satellite PSSM-derived daily images of sea ice distribution, Arrigo and van Dijken (2003a) identified 52 polynyas. This number was subsequently reduced to 37 postpolynyas which, as their name suggests, occur each year after “conventional” winter polynya activity. Although not polynyas in the strictest sense, these spring-time features are included here by virtue of their biological and biogeochemical importance and their intimate rela-


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Table 22: Locations, mean size (in km2 ), month of maximum extent, degree of recurrence (REC) and persistence (PER), and formation mode of 28 shelf (latent-heat) polynyas in East Antarctica (from 40–160◦ E), based upon the 75% ice-concentration threshold in the winter months of 1987 to 1994. The mean areas have been calculated considering only the months when the polynya was present. Recurrence refers to the number of years from 1987 to 1994 that the polynya appears during winter with an ice-concentration core of 0,

(4.2)

where the subscript c has now been omitted on the frazil ice velocity, for convenience. Inequality (4.2) ensures that the polynya will open to a steady-state width. The frazil ice mass conservation equation (2.2) simplifies to ht + uhx = F,

(4.3)

subject to h(0, t) = 0, t ≥ 0. To understand the fundamental behaviour of the polynya opening problem we further assume that F is constant. 4.1

Constant Collection Thickness

When H is constant the solution of (4.1) and (4.3) subject to the initial condition and coastal boundary condition is X 1 X 1 t= (4.4) − Lp − ln 1 − , u U u Lp where Lp = H U/F is the steady-state width discussed by Pease (1987). The width Lp can be calculated immediately from (4.1) by noting that in the steady-state case H U = hc u

(4.5)

at x = Lp , and that hc = F (Lp /u), where the transit time for frazil ice across the polynya is Lp /u. Clearly (4.5) states that the flux of frazil ice arriving at the polynya edge balances the flux of consolidated new ice away from the polynya edge. During opening, the latter flux exceeds the former because the frazil ice depth is small, and the polynya edge moves offshore. We define the polynya opening time Tp to be the time taken for the polynya to open to a width (1 − )Lp , where typically = 0.05. From (4.4) we see that Lp 1 1 Tp = (4.6) (1 − ) + Lp − ln −1 . u U u

Polynya Modelling 4.2

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Parameterisation (3.2) for H

Biggs et al. (2000) show that X = L 1 − exp(−U t/L) ,

(4.7)

where cuU (u − U ) L= (4.8) F is the steady-state width associated with collection thickness (3.2). Notice that L depends cubically on ice drift speeds and will be extremely sensitive to measured errors in these quantities. Once again the width (4.8) can be deduced immediately from (4.5) at X = L. Using the fact that hc = F (L/U ) we find that the flux balance (4.5) becomes FL U (4.9) + c(u − U )2 = F L u from which (4.8) follows. The polynya opening time is given by T =

L ln −1 . U

4.3

Parameterisation (3.3) for H

(4.10)

Biggs and Willmott (2004) show that collection thickness parameterisation (3.3) leads to the following solution for the polynya width X = L˜ 1 − exp −U t/L˜ , (4.11) where the steady-state width L˜ is given by hu cuU (u − U ) ˜ 1+ . L= F c(u − U )2

(4.12)

The opening time in this case is found to be L˜ T˜ = ln −1 . U 4.4

(4.13)

Discussion of the Opening Models

To compare the opening times predicted by the model using a constant collection thickness with that using parameterisation (3.2) we demand that the steady-state width in each model is identical. This can be achieved by requiring H = cu(u − U ),

(4.14)

in the constant collection thickness model. In deriving (4.14) we are of course assuming the ice velocities and ice production rates are identical in each model. Biggs et al. (2000) show that T > Tp and that the relative difference between the opening times T − Tp U 1− U = +1 ≤ < 1, T u ln u

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A.J. Willmott et al.

Figure 3: Temporal evolution of a one-dimensional polynya edge for two values of the ratio U/u: (a) 0.2, (b) 0.6. After Biggs et al. (2000). depends solely on the ratio of the ice drift velocities. Figure 3 shows the evolution of the polynya to its steady state for both models with U/u as a parameter. Figure 4 shows contour plots of Lp , L, Tp and T in air temperature (Ta )–wind speed (Ua ) space. The solutions plotted in Figures 3 and 4 use an expression for F proposed by Pease (1987) which linearly depends on Ta via the sensible heat term. From this figure we deduce that 1. L is a more sensitive function than Lp of Ua . The weak dependence of Lp on Ua is explained by the fact that both U and F increase almost linearly with increasing Ua . Increasing Ua tends to open the polynya, while increasing F tends to close it and the two processes cancel. However, L grows quadratically with Ua (see (4.8)). 2. Both Tp and T are strong functions of Ua and Ta . Note that Tp increases as Ua increases, while the opposite is true for T , and this is due to the increased dependence of L (compared with Lp ) on Ua . In a constant H model, an increase in Ua leads to a linear increase in both U and u and (4.6) shows that Tp depends on Ua via the factor F −1 . However, T grows linearly with Ua (see (4.10)). We see from (4.8) and (4.12) that L˜ > L for given U , u and F . Hence (4.10) and (4.13) show that T˜ > T . For example, with U = 0.3 m s−1 , u = 2 U and F = 0.27 m day−1 we find that L = 11.5 km, while L˜ = 21 km. The corresponding opening times are T = 31.9 h and T˜ = 59 h.

5 Two-Dimensional Steady-State Solutions Polynya flux models in two dimensions were first introduced by Darby et al. (1994) and the model representation of the influence of coastline orientation on steady-state polynya shape was investigated in Darby et al. (1995). With the notation introduced in Section 2, the problem solved by Darby et al. (1995) can be formulated as (H U − hc uc ) · n = 0,

(5.1)

which simply states that a balance between the mass fluxes of frazil and consolidated new ice must exist at the steady-state polynya edge in a direction normal to the edge. Darby et al. (1995) introduced the concept of an alongshore polynya length scale La that measures the alongshore distance over which a polynya adjusts to its asymptotic width. In this paper the frazil and consolidated ice velocities are assumed constant. The length scale La depends

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Figure 4: Contour plots of the new and H -constant equilibrium widths L and Lc , (a) and (b) respectively (in km), and the corresponding spin-up times t 0.05 and tc0.05 , (c) and (d) respectively (in hours), as a function of air temperature Ta (◦ C) and wind speed Ua (m s−1 ). For plots (b) and (d), the constant collection thickness Hc is chosen so that L = Lc at the control wind speed of Ua = 20 m s−1 . After Biggs et al. (2000). on the direction of travel of both frazil ice and the consolidated new ice. When frazil ice and consolidated ice both drift due offshore, La is zero. Variations in the coastline shape that occur over length scales much smaller than La are not reproduced in the steady-state polynya edge. However, the steady-state polynya reproduces the shape of capes and coastal embayments provided their alongshore lengthscale is greater than or equal to La . A simple theory was developed by Morales Maqueda and Willmott (2000) for the derivation of La by considering the polynya edge response to small coastline departures from a straight line. Biggs et al. (2000) revisited the steady-state polynya problem of Darby et al. (1995), instead using (3.1) as the collection thickness. They study polynya formation for two simple coastline configurations, namely a semi-infinite and a finite-length coastal barriers. In all

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cases, their steady-state solutions are similar to those obtained with constant H , although, for identical u, U and F , polynya areas are normally larger when H is not a constant. Note that in (3.1) the subscript c has been dropped on the frazil ice velocity because it is assumed to be constant. A peculiarity of the steady non-constant H solutions is that corners in the polynya edge can form. As discussed above, the frazil ice collection thickness is not uniquely defined at these points.

6 Two-Dimensional Steady-State Solutions with Ocean Currents So far, we have neglected in our discussion the effects of coastal currents on the drift of frazil and consolidated sea ice on a polynya, a problem that was investigated by Willmott et al. (1997). These authors calculate free-drift frazil ice velocities within a polynya using (2.3) and (2.4) and derive analytical expressions for the polynya shape for the case of a semi-infinite straight coastline when uo , U and τ are uniform in the alongshore direction. They also determine the analytical formula for the alongshore polynya length scale La in this case. In Darby et al. (1995) frazil ice trajectories were a family of straight lines and this fact was exploited in the derivation of La . In the present problem, frazil ice trajectories are generally curved and are not parallel to each other (Figure 5). As a result, La will depend on the particular geometry of these trajectories. This is illustrated in Figure 5, where three steady-state polynya solutions are shown for identical wind stress and frazil ice production rates but different alongshore ocean current distribution. Note how the polynya shape varies significantly from a situation in which the coastal current has no shear (panel a) to cases in which there is a marked shear in the x-direction (∂vo /∂x = 0.3310−5 s−1 in panel b and ∂vo /∂x = −0.3310−5 s−1 in panel c). The Willmott et al. (1997) model is applied to the simulation of the Northeast Water Polynya (NEW), which sometimes form off the northern Greenland coast during winter and early spring between the Henrik Krøyer Islands and Ob Bank (Figure 6). Among the many sensitivity experiments described in this paper, two are specially worthy of note. In the first one, frazil ice production rates are calculated as a function of frazil ice thickness. This is a potentially important process, as surface heat losses are expected to drastically diminish when frazil ice depths and concentrations are high. This experiment is in some way a precursor of the coupled atmosphere–polynya model described in Section 10. In the second one, the sensitivity of the polynya shape to spatially varying consolidated new ice drift is investigated. Not surprisingly, the polynya edge is shown to be highly responsive to variations in this flux.

7 Unsteady 2-Dimensional Flux Models Morales Maqueda and Willmott (2000) developed the first 2-dimensional model for the opening of a coastal polynya. In this study, (2.1) is solved using the method of characteristics for the case when H is constant and ocean currents are neglected. Morales Maqueda and Willmott (2000) allow both the offshore wind stress and the frazil ice production rate to vary spatially and temporally. However, the authors obtain analytical solutions for the opening of the polynya adjacent to an island, represented as a straight coastline of finite length, in the case when the wind stress (and hence ice drift velocities) and frazil ice production rate are constant. In this special case a simple expression is obtained for the steady-state area, Ae , of an island polynya, namely H |U | Def . Ae = (7.1) F

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Figure 5: Polynya solutions along a straight coastline located at x = 0 for uniform wind stress (bold arrow on the y axis) and frazil ice production (Willmott et al., 1997). The arrows within the polynya indicate the direction and relative magnitude of the oceanic currents. In the top panel, the longshore ocean currents are uniform; in the middle panel, the current speed increases offshore; in the bottom panel, the speed decays offshore. The dotted lines within the polynya represent selected frazil ice trajectories. The length and direction of the heavy arrows on the consolidated new ice region denote the Lebedev–Pease width (H |U |/F ) and the direction of motion of the consolidated ice, respectively.

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Figure 6: Simulation of the NEW polynya (Willmott et al., 1997). The point (0 km, 100 km) is located at (82.1◦ N, 21◦ W). The heavy line delineates the coast of Greenland and idealised landfast ice boundaries are marked by the dashed lines. The thin dotted lines correspond to selected frazil ice trajectories. The thin dashed line is the contour of the polynya observed on 6 May 1991.

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Figure 7: Simulated St. Lawrence Island Polynya (SLIP, located at approximately 170W–63N) at a time, t, (counted from the moment the polynya started to open) when the polynya has reached 99% of its steady-state area in February (using climatological data). Also shown is the consolidated new ice region (non-hatched area). Within the polynya, the dashed lines are frazil ice trajectories drawn about 10 km apart. The thick (thin) vector represents the consolidated new ice (frazil ice) velocity. The small blank area adjacent to the coast corresponds to the land fast ice. After Morales Maqueda and Willmott (2000). In (7.1), Def is the “effective cross-sectional length of the island”, defined as the maximum separation between the coastal points in a direction perpendicular to the consolidated ice velocity (see Figure 7). The remaining notation in (7.1) is defined in earlier sections. Expression (7.1) demonstrates that the steady-state area of a polynya depends on not only the wind speed and the air temperature (they are both required to calculate F , and the former sets |U |) but also on Def . In the neighbourhood of polynyas it is usually assumed that the consolidated and frazil ice are in free-drift (i.e. internal ice stresses are negligible within the sea ice). The frazil ice velocity is frequently related empirically to the surface wind velocity U a according to u ≈ 0.06U a . Zubov’s law is often invoked to determine the consolidated ice velocity U = ε[cos U a − sin k ∧ U a ], where k is an upward unit vector, ε ≈ 0.03 and ≈ 28◦ is a turning angle, positive to the right of the wind in the Northern Hemisphere. Thus, changing the direction of U a leads to a change in the direction of U and hence in the magnitude of Def (see Figure 7). In the case of St. Lawrence Island Def can theoretically be as large as 150 km when U is oriented to the east-southeast. However, in practice the climatological winter wind stress over St. Lawrence Island exhibits a small variation in orientation (a northerly wind) and Def ≈ 116 km to within ±5 km (Morales Maqueda and Willmott, 2000). Recognising the drawbacks with assigning a constant value to H (see Section 3), 2-dimensional polynya opening models have been developed that incorporate parameterisations (3.1), (3.3) (Biggs and Willmott, 2004) and (3.4) (Walkington and Willmott, 2005b).

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Biggs and Willmott (2004) show that (3.1) is not robust in models for the opening of 2-dimensional polynyas. The origin of the problem is the following. For certain ice drift velocities the steady-state polynya edge exhibits a corner. When opening to such solutions it is found that the characteristic curves associated with (2.1) do not completely span the polynya domain. As a consequence there are segments of the evolving polynya edge that cannot be reached by characteristics leaving the coast. Recognising the drawbacks of (3.1), Biggs and Willmott proposed an alternative collection depth parameterisation (3.3), and use it to study the opening of an island polynya. A “prototype island” is represented by a straight coastline of length D, and the polynya opening time T is compared with that in Morales Maqueda and Willmott (2000) (T¯ , say), when the steady-state polynya areas are identical. When D is short (long) it is found that T¯ > T (T¯ < T ). Furthermore, Biggs and Willmott (2004) show that (3.3) performs well in simulations of the opening of the St. Lawrence Island polynya compared with satellite observations of the opening time. Finally, at the stage of writing this chapter, the study by Walkington and Willmott (2005b) is not yet complete.

8

Polynya Closing

In many Arctic and Antarctic regions the ice drift direction alternates offshore in response to the changing wind stress, leading to polynya opening and then closing. Consider a polynya that has opened under the action of offshore wind-stress. At the instant when the wind stress becomes onshore, there will be a distribution of frazil ice within the polynya. During an onshore wind stress regime two processes contribute to the closure of a polynya. Frazil ice will drift onshore and continue to form thermodynamically. This ice will “pile-up” at the coast to form a coastal boundary of consolidated ice that moves offshore. Second, the offshore consolidated ice pack will now drift onshore under the action of the wind stress. It is clear that there will be two distinct phases during the polynya closing process. In the initial stage 0 < t < tcrit frazil ice arriving at the coast originated from the polynya interior at the onset of closure (see Figure 8). In other words, during this phase all the frazil ice that was inside the polynya at the instant it begins to close (t = 0) moves onshore, while continuing to grow thermodynamically, to pile-up at the coast. This first phase takes place over the time tcrit , a quantity that must be determined as part of the closing problem. In the final phase of the closing regime (tcrit < t < Tclose ) frazil ice arriving at the coast originated from the polynya edge that is travelling onshore with speed U under the action of the wind stress. Thus, the closing time to be calculated is Tclose . Tear et al. (2003) study the one-dimensional closing problem under the following assumptions, (1) the onshore and offshore ice drift rates are uniform, possibly distinct during opening and closing, (2) the frazil ice production rate is constant, (3) surface ocean currents are neglected. These three assumptions enable Tear et al. (2003) to obtain a large number of analytical solutions in the cases when the collection thickness is either constant or parameterised by (3.2). Consequently, the closing solutions obtained by Tear et al. (2003) convey the fundamental behaviour of the polynya closing problem. Relaxing assumptions (1) and (2) will not alter the formulation of the closing problem although solutions in this case will almost certainly have to be calculated numerically. The salient points to note about the solutions of the polynya closing problem are:

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Figure 8: Schematic of polynya closure (Tear et al., 2003). Diagrams of the polynya for (a) 0 < t < tcrit and (b) tcrit < t. The width of the consolidated ice region adjacent to the coast, formed by frazil ice drifting onshore, is s(t). 1. The closing time is shorter than the opening time. There are two situations which can violate this conclusion. The first is when the ice drift rates onshore during polynya closure are significantly smaller than the offshore drift rates during opening. This situation is perhaps less likely to occur in reality. The second situation is when a polynya opens to a small fraction of its steady-state width. In this case, the opening and closing time are almost identical because there is very little frazil ice inside the polynya at the onset of closure to pile-up at the coast. 2. If a constant collection thickness is used, it is possible that during the closing cycle the thickness of frazil ice will exceed the named value of H (of course, this could happen during the opening cycle, in which case the closing problem becomes irrelevant) thereby invalidating the governing equations. The parameterisation (3.2) for H avoids this problem. In their investigation of the polynya closure problem with constant collection thickness, Tear et al. (2003) calculate the ratio Tclose /Topen of closing and opening times as a function of the parameters ε, μ = Uc /uc = Uo /uo , λ = −Uc /Uo = −uc /uo and γ = Fc /Fo , where Uc (uc ) and Uo (uo ) are the consolidated (frazil) ice velocities during closure and opening, respectively, and Fc and Fo are the corresponding frazil ice production rates. The parameter ε

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Figure 9: Contours of Tclose /Topen for γ = 1, and (a) λ = 0.5; (b) λ = 0.75; (c) λ = 1; (d) λ = 1.25. The broken line shows the behaviour of μcrit . After Tear et al. (2003). determines the width (1−ε)Ls of the polynya, where Ls is the steady state width. They show that this ratio is virtually insensitive to γ but varies considerably with λ, decreasing significantly as λ increases, and that for 1 ≤ λ the ratio is always smaller than 1 (Figure 9). Note that in Figure 9 there are regions where no valid solution is obtained (blank region enclosed by the broken line) because the frazil ice thickness exceeds the prescribed consolidated ice thickness at some instant during the polynya closure. Tear et al. (2003) subsequently examine this quotient when parameterisation (3.2) is used (Figure 10). Qualitatively, the behaviour is similar to that in Figure 9. The authors also compare closure times for both collection depth new in ε–μ space, with λ as a paparameterisations. Figure 11 shows contours of Tclose /Tclose new rameter and γ = 1. From this figure it is clear Tclose > Tclose when λ ≥ 1. When λ < 1, new < T Figure 11 reveals that Tclose close when μ is sufficiently small (i.e. consolidated ice speeds significantly smaller than frazil ice speeds), but the inequality in closing times reverses for larger values of μ. The closing time of a 2-dimensional polynya is considered by Biggs et al. (2004) when the collection depth H of consolidated new ice at the polynya edge (during opening) and at the coast (during closing) are assumed to be constant. For a polynya adjacent to an island, represented by a straight line barrier of length D, Biggs et al. (2004) demonstrate that the closing time T 2D is:

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new /T new for γ = 1, and (a) λ = 0.5; (b) λ = 0.75; (c) λ = 1; (d) Figure 10: Contours of Tclose open λ = 1.25. After Tear et al. (2003).

• shorter than the opening time, in agreement with the 1-dimensional case studied by Tear et al. (2003); • relatively insensitive to ice drift orientation and the quotient |U |/|u| where u and U are the frazil and consolidated ice velocities respectively; • weakly dependent on D, except when the initial polynya area, prior to closing, is much smaller than the steady-state area and when D ≤ La , where La is the alongshore adjustment length scale (see Section 5); • sensitive to F /FO , where F and FO are the constant frazil ice production rates during polynya closing and opening respectively. Biggs et al. (2004) exploit these results to derive an approximate expression for T 2D (Ta2D , say) that can be readily evaluated in terms of H , u, U , F and FO . The caveat is that Ta2D is only valid for a polynya with area, prior to closing, that is close to the steady-state value. It must be stressed that Ta2D can be calculated without recourse to running a numerical polynya flux model.

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new for γ = 1, and (a) λ = 0.5; (b) λ = 0.75; (c) λ = 1; Figure 11: Contours of Tclose /Tclose (d) λ = 1.25. The broken line shows the behaviour of μcrit . After Tear et al. (2003).

9

A Polynya Flux Model with a Prognostic Frazil Ice Concentration

The fact that we readily identify polynyas as regions of low sea ice concentration provides the incentive to extend the flux model formulation to include variable frazil ice concentration. Within a 1-dimensional context a model of this type has been developed by Walkington and Willmott (2005a). The authors show that the generalisation of the equation (4.1) governing the width X of an opening coastal polynya is AH U − ac hc uc dX = . (9.1) dt AH − ac hc In (9.1) A is the concentration of the consolidated ice pack in the neighbourhood of the polynya edge, and is assumed to be constant. Concentration of frazil ice at the polynya edge is denoted by ac . The remaining notation is defined in Section 4. Walkington and Willmott (2005a) consider the case when the consolidated and frazil ice drift rates are both constant, with u > U > 0. Note the subscripts c is dropped on the frazil ice velocity for notational convenience. The frazil ice depth and concentration fields are determined from the equations ht + uhx = Fw (1 − a) + aFi

(9.2)

Polynya Modelling at + uax =

Fw (1 − a), h0

107 (9.3)

where Fw and Fi denote the frazil ice production rates in open and ice covered regions, respectively, and h0 is a constant ice accumulation depth (see Section 12 of this article for a further discussion of this term and Lemke et al., 1990). In general, the solution for X is numerically calculated by Walkington and Willmott (2005a). However, exploiting the fact that |Fi /Fw | ≈ 10−2 1 allows Walkington and Willmott (2005a) to calculate an approximate analytical solution for X when; (a) A and H are specified constants; (b) AH = ac hc + c(u − U )2 . Case (a) is analogous to the collection depth parameterisation used by Pease (1987), while case (b) generalises the parameterisation of Biggs et al. (2000). Walkington and Willmott (2005a) demonstrate that the introduction of a variable frazil ice concentration reduces the net ocean to atmosphere heat flux within the polynya, thereby increasing both the opening time and steady-state polynya width, compared with flux models that neglect concentration.

10 Coupled Atmosphere–Polynya Flux Model The large net ocean to atmosphere heat flux within a coastal polynya has the potential to increase the temperature of the atmosphere in the lower convective boundary layer. A consequence of this atmospheric warming will be to reduce the sensible heat flux from the ocean to the atmosphere, and this in turn will decrease the frazil production rate. We therefore anticipate that the polynya opening time and the steady-state area could be significantly altered in a coupled atmosphere–polynya model. Renfrew and King (2000) considered the impact of a typical, prescribed, heat flux within a Weddell Sea polynya on a one-dimensional convective boundary layer model of the lower atmosphere. This atmospheric boundary layer model is used as the starting point for the development of a coupled atmosphere–polynya model by Walkington and Willmott (2005a). The polynya model described in Section 9 is coupled by Walkington and Willmott (2005a) to the atmospheric boundary layer model of Renfrew and King. In the coupled model, frazil ice production is found to decrease with offshore distance in response to warming of the lower atmosphere above the polynya. Further, a new qualitative feature is found in a coupled atmosphere–polynya model, that is absent in a polynya model where the coupling is absent, namely the concept of a critical wind speed, above which a steady-state polynya cannot exist. The existence of the critical wind speed is most clearly illustrated in the case when the prescribed air temperature at the coast is near the freezing point. In this case, a relatively small amount of heat into the atmospheric boundary layer creates a large relative change in the net heat flux, allowing the frazil ice production rate to vanish completely at some offshore location. Now, the net ocean to atmosphere heat flux is predominately due to the sensible heat flux which is directly proportional to the (prescribed) wind speed 10 m above the ocean surface, u10 . Thus, increasing u10 increases the warming of the atmospheric boundary layer, making it more likely that frazil ice production will be suppressed beyond some offshore location in the polynya. In the absence of frazil ice production a polynya will open indefinitely. In summary, the polynya extent and opening time in a coupled model is increased compared with these quantities in a decoupled coastal polynya model. Finally, we note that the

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temperature in the atmospheric boundary layer above a polynya increases with the offshore distance, thereby creating an offshore atmospheric gradient. Over relatively narrow coastal polynyas (order 20 km, or less) the perturbation wind generated by this buoyancy forcing is found to be insignificant, although this conclusion is likely to change over features such as the Weddell Polynya (Timmerman et al., 1999).

11

General Circulation Modelling Approach

Here we switch from discussing flux polynya models to general circulation (GCM) polynya models. A sea-ice GCM is a numerical model—it uses discretized versions of the conservation equations of mass and momentum of sea ice, coupled with a numerical technique, to generate solutions to these equations. The solutions are obtained over a discrete, regular spatial grid that covers the region under study, and the solutions are generated at discrete, regular time intervals. The numerical solution then provides a spatio-temporal description of the evolution of the sea-ice cover. A sea-ice GCM receives its forcing from atmospheric and oceanic components, either of which may in themselves be GCMs, or simply as prescribed data sets. In the latter case, the modelling system is referred to as a “stand-alone” ice GCM. In the former case, the system is referred to as a “coupled” GCM. A coupled system will also contain a discretisation and solution technique for the atmosphere and/or ocean mass and momentum conservation equations. The distinguishing characteristic of a GCM polynya model, compared to a flux polynya model, is that the GCM describes a polynya in terms of the concentration of sea ice as distributed over some regular spatial grid, although the former is being developed to include variable concentration. Here we define the sea-ice concentration to be a number between zero and one, describing the fraction of a grid cell covered by sea ice. The non-covered fraction is referred to as a lead. When many neighbouring grid cells have, collectively, a low sea-ice concentration, the patch of grid cells would be identified as a polynya within a GCM simulation. By contrast to GCM polynya modelling, a flux polynya model describes a polynya in terms of a contour line, where the ice concentration switches sharply from a near zero value to a near unity value. Again, as a point of difference, the GCM approach does not have this concept of a sharp edge, nor of a collection thickness at that edge.

12

Ice GCM Equations

The two key mathematical principles governing an ice GCM are that mass and momentum are conserved. We consider the ice as a two-dimensional fluid floating upon the ocean surface and underlying the atmosphere. The ice has a vertical thickness h(r, t), an areal concentration c(r, t), and a horizontal velocity u(x, t), where r = (x, y) is a two-dimensional position vector, and x, y, and t have their usual meanings. The coordinate system used in the derivations that follow is an Eulerian one—the x and y axes are fixed in space. The density of the ice is denoted ρ and is assumed to be a constant. The mass of ice per unit area is the product quantity m = ρhc. 12.1

Mass Conservation

A mass conservation relation for the ice is now derived. A conservation law, expressed in an Eulerian framework, demands that the time rate of change of a property is equal to the

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difference in flux of that property entering and leaving an arbitrarily small element of area. For ice mass, this statement reduces to ∂m (12.1) + · (mu) = 0, ∂t and substituting the earlier definition of m, while noting that ρ is a constant, gives the result ∂hc + · (hcu) = 0. (12.2) ∂t Assuming for the moment that u is known (we will determine some aspects of its behaviour when we discuss the momentum equation in Section 12.2), we then have a single equation describing the behaviour of two unknowns, h and c. This single equation is, in a sense, an underdetermined system—it has no unique solution for h or c. Even if we specify initial condition on h and c, we will only know how the product of these fields evolve in space and time, but not the individual fields themselves. Clearly, we need to impose a second, auxiliary relation governing the behaviour of one or both of these variables. Writing down a statement of mass conservation requires no specific knowledge of the behaviour of the fluid being studied (i.e., all fluids conserve mass). To further progress, we have to rely on specific knowledge of the ice behaviour. More precisely, we have to make some reasonable assumptions about its behaviour. We assume that as ice moves about its domain, being advected by the velocity field u, the ice thickness does not change. This is equivalent to the statement that the ice thickness behaves as a Lagrangian field, and so ∂h Dh = + u · h = 0. (12.3) Dt ∂t Part of the basis for this statement is that ice, being a rigid material, tends to keep its shape and form, unless forced to deform by an overwhelming mechanical stress. In any instance that the ice does not behave according to (12.3), then the ice must obviously become thinner or thicker. Because we are going to treat ice as a plastic material (as in the context of the momentum equation to be discussed in Section 12.2), there is no mechanical process by which the ice can become thinner. That is, in a divergent ice flow field (∇ · u > 0), ice will not undergo thinning. However, there is a mechanical process by which ice can become thicker in a convergent ice flow field (∇ · u < 0)—the ice can undergo plastic failure. This means we have to refine the conditions under which (12.3) is valid and only apply it when ice is not being thickened by flow deformation. Still, (12.3) is not yet a satisfactory equation to use as a second auxiliary relation in an ice GCM as it is not valid then, under all flow regimes. Following with our goal of deriving such an auxiliary relation, we can nonetheless use the thickness equation (12.3) to derive a relation for the ice concentration. By multiplying (12.3) by c and subtracting the resulting equation from our mass-conservation equation (12.2), we arrive at the concentration-conservation statement ∂c + · (cu) = 0. (12.4) ∂t It is the equation pair (12.2) and (12.4) that are commonly used to solve for the two variables h and c in an ice GCM. While equation (12.2) provides a solution for the product quantity hc, in a GCM we divide this quantity by the now-known quantity c from (12.4), to arrive at knowing h. The equation pair (12.2) and (12.4) certainly describe the behaviour of ice that is not undergoing flow deformation. We derived this pair under the assumption (i.e., equation (12.3)

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that ice thickness does not change as ice is advected about a domain. The ice concentration field, of course, can change depending on the nature of the flow field driving the ice. Specifically, the concentration can range over the physically meaningful set of values [0, 1]. Less obvious, the equation pair (12.2) and (12.4) also describes the evolution of the thickness and concentration fields even when ice is undergoing flow deformation, and that the ice is thickening. Given a physically meaningful initial ice concentration, this concentration cannot evolve to become less than zero, because (12.4) cannot produce a negative value of c. That is, once c decreases from a positive value and reaches zero, then the flux-divergence term becomes identically zero and the time rate of change of c also becomes zero. Thus negative concentrations cannot be realised. At the other end of the [0, 1] concentration range, enforcing concentration to be less than or equal to unity is more problematic. There is nothing inherent about equation (12.4) that causes it to enforce such a condition. Instead it must be imposed as a separate constraint c ≤ 1.

(12.5)

Once this constraint is reached, i.e., c = 1, the mass-conservation equation (12.2) effectively transforms into the thickness-conservation equation: ∂h (12.6) + · (hu) = 0. ∂t We now have a situation where the thickness is controlled by equation (12.3) when the ice concentration is less than unity and by equation (12.6) when it is equal to unity. In the former case, the ice thickness does not change due to mechanical forcing. In the latter case, it may indeed change, depending on the details of the mechanical forcing imposed by the flow field u. In an ice GCM, this ‘switching’ behaviour in terms of ice thickness evolution is automatically guaranteed to occur as long as we impose the constraint (12.5) as we solve the equation pair (12.2) and (12.4). A temporary oversight in the derivation of the mass-conservation equation was the lack of treatment of sources and sinks of mass. We now redress this by considering the forcing terms that arise from ice thermodynamics—sources and sinks of heat energy that create and destroy ice mass. Symbolically, we add forcing terms hc and c to the right-hand side of the governing mass (12.2) and concentration (12.4) equations as: ∂hc + · (hcu) = hc , (12.7) ∂t ∂c (12.8) + · (cu) = c . ∂t The formulation of the term hc is relatively straightforward as it simply demands that one carries out a careful heat energy budget at the interfaces between the ice and atmosphere, between the ice and ocean, and subsequently determine the amount of freezing and or melting that occurs for the ice mass. The treatment of c is more problematic. This term describes how external heat energy changes the areal concentration of the ice. In essence, while the hc term describes the change in mass of the ice, the c term describes the ratio of change in ice concentration c to that of thickness h. As one can well imagine, the c term is not derivable from first principles, but instead must be formulated to best parameterise those process which specifically contribute to lateral melt (i.e., changes in c) versus those that contribute to vertical melt (i.e.,

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changes in h). The development of these parameterisations is an active area of research. Basic details of the formulation of the thermodynamic forcing terms as commonly used in ice GCMs is given by Holland (1998). 12.2

Momentum Conservation

We now derive the principle of momentum conservation for the ice. For momentum, which is the product quantity mu, the statement begins as ∂(mu) + · ((mu)u) = 0, (12.9) ∂t which states that the time rate-of-change of momentum in an arbitrarily small element of area is equal to the difference in momentum advected into and out of the area. In ice GCMs it is more common to formulate the momentum conservation law in terms of the time rateof-change of velocity rather than momentum, as in (12.9). To arrive at such a formulation, we manipulate the mass-conservation equation (12.2) by multiplying it by the vector u and subtracting the resulting equation from the momentum-conservation equation (12.9). The resulting equation is ∂u + mu · u = 0. (12.10) ∂t This relation is valid in an inertial frame of reference, that is one moving at a constant velocity (possibly zero), in a straight line. To use this equation in an Eulerian reference frame that is attached to the rotating Earth, a coordinate transformation must be employed (Stommel and Moore, 1989). This leads to the form

m

∂u (12.11) + mu · u + mf k × u = 0, ∂t involving the Coriolis force. Here, f denotes the Coriolis parameter, which is a function of latitude φ according to f = 2 sin φ, and is the angular rotation rate of the Earth. A unit vector k points in the local-vertical direction. The coordinate transformation also involves a centrifugal force, but that force occurs only in the local-vertical direction (along with the gravity force), and here we are describing only forces in the local horizontal direction. The description of the momentum conservation law, up to this point, has only involved body forces (i.e., ones that are proportional to the mass of the ice). There are also surface and interfacial forces acting on the ice. On the top surface there is a stress due to the velocity shear between the ice motion and the wind; on the bottom surface there is an analogous stress due to the ocean current. These stresses are formulated as quadratic drag laws, using empirical coefficients. The usual forms of these atmospheric τ and oceanic τ iw drags are (McPhee, 1976): m

τ

= ρa ca |ua − u|(ua − u),

τ iw = ρi ciw |u − uo |(u − uo ),

(12.12)

where ρa and ρi are the air and ice densities, respectively, ca and ciw the drag coefficients, and ua and uo the velocities, of the air and ocean, respectively. The remaining interfacial forces acting on sea ice are those due to internal stresses. The detailed description of these forces is often referred to as an ice rheology. Recall that the ice momentum equation has been derived for an arbitrary small horizontal area element. Then, in a Cartesian coordinate frame, one can describe normal and shear stresses acting along

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each edge of a horizontal, rectangular area element. These stresses are collected into a stress tensor, σ , which appears in the present setting as a rank-2 tensor: σxx σxy . σ = (12.13) σyx σyy The normal stresses are represented by the diagonal elements σxx and σyy , and the shear stresses are represented by the σxy and σyx . As equal stresses on opposite faces effectively cancel one another out, it is the divergence of the stress tensor ∇ · σ that determines the net contribution of the interfacial stresses to the ice momentum balance. In the instance that both the normal and shear stresses are ignored, then there is no rheology in place and the flow is described as being in “free drift”. Where only the normal stresses are defined, and the stresses are finite for convergent flow (∇ · u < 0) and zero in divergent flow (∇ · u > 0), then the rheology is typically called a “cavitating fluid” (Flato and Hibler, 1992). Finally, when both the normal and shear stresses are included, and the shear stress is finite and proportional to the normal stress, then the rheology is referred to as “plastic”. There are elaboration on this latter type, namely “viscous-plastic” (Hibler, 1979), “granular-flow” (Tremblay and Mysak, 1997), and “elastic-viscous-plastic” (Hunke and Dukowicz, 1997), but such rheologies represent relatively minor modifications to the physical principles underlying a plastic rheology. The astute reader may have noticed that, by introducing interfacial stresses σ , we have introduced another closure problem. We have no explicit relationship between the stresses and the other known variables. The typical closure in this instance is to mathematically formulate the ice as having plastic behaviour. This implies that the interfacial stresses are independent of the velocity field and their gradients, and that the ice can support only a limited amount of interfacial stress before it undergoes failure. We denote that maximum stress as σmax and chose to relate it as (Hibler, 1979): σmax = P ∗ h e−k(1−c) ,

(12.14)

P∗

and k are empirical constants that describe the strength of the ice as functions where of thickness h and concentration c. Notice that the strength parameterisation is a strongly varying function of ice concentration. For concentration just slightly under unity, the ice will have almost no strength. This implies, by contrast, that only ice near unity concentration will experience non-zero interfacial stresses. The manner in which the rheology operates is that under convergent flow (∇ · u < 0) the normal stresses will be able to reach to a value of σmax , and the stresses will tend to resist ice converging. If the ice is forced sufficiently hard, for example by the a strong wind stress τa , then it is possible that the interfacial stresses will not be sufficiently strong to oppose the forcing. In this case the ice undergoes plastic failure, and the convergence of the flow goes ahead unhindered. Returning to the formulation of our ice momentum-conservation relation, and adding these surface and interfacial stresses, we arrive at the final expression ∂u + mu · u + mf k × u = τ − τ iw + ∇ · σ . (12.15) ∂t This completes our brief discussion of the equations governing the ice behaviour in a GCM. The reader should pay special attention, in the context of polynya modelling, to the equations we have derived for ice concentration (12.4) and velocity (12.15). It is principally these two equations that, working in tandem, produce the essential features of the polynya m

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simulations that we will discuss in Section 14. The simulation of concentration is, after all, fundamental to the existence of a polynya, which in an ice GCM context is simply an area of grid cells having a low ice concentration as compared to a surrounding area of cells having a higher one. While state-of-the-art ice GCMs employ a wide variety of treatments of ice thermodynamics and dynamics, including those having a thickness distribution (which applies the above equations individually to each thickness category within a grid cell), these elaborations and many others are well discussed in the literature (see reference list). Our objective has been to provide the reader with an introduction to the most fundamental laws and assumptions that underlie most ice GCMs currently in use, and that are directly relevant to polynya dynamics.

13

Numerical Methods in Ice GCMs

There are many approaches to solving the ice GCM equations on a digital computer. Most approaches to date have involved the finite-difference method, whereby the dependent variables c, h, and u, are evaluated at discrete grid points. Other approaches are possible, for example, the finite element and spectral methods, but have not been employed as often as their finite-difference counterpart. For the finite-difference method, the continuum variables are mapped onto discrete grid points in both space and time. Clearly, the finer the underlying grid, the better the resolution, and the more accurate and relevant the numerical solution. Presently available computing resources dictate the upper limit on grid resolution. Most ice GCM polynya studies to date have been performed over regional domains, typically of expanse of about 103 km in each horizontal direction, and have been able to therefore use relatively high grid resolution of 10 km, and in some cases finer. Some studies have been performed within the context of global domains and have accordingly used much more modest resolution in the region of polynyas, leading to a sometimes poor simulation of the polynya. One method to overcome this is to use a warped domain whereby the underlying mesh is stretched to provide highest resolution in the known geographical area of a polynya (e.g., Martin et al., 2004). So as to provide a concrete example on how to move from the continuum equations of Section 12 into their discrete-domain analogs, and how to obtain their numerical solutions, we here succinctly describe one particular numerical scheme (Holland, 2001c and references therein). To further limit the scope of the discussion, while still giving an overview of that one particular numerical technique, we restrict our attention to just the ice concentration equation (12.4), and further limit that equation to its one-dimensional, x-direction counterpart as ∂c ∂(cu) (13.1) + = 0. ∂t ∂x As a starting point for obtaining a numerical solution of (13.1), we represent the continuum dependent variables in terms of Taylor series expansions in both their time and space independent variables. For the time discretisation, we represent the time-derivative term in (13.1) starting with the expansion: 1 ∂ 2c ∂c (13.2) (x)δt + (r)δt 2 + . . . , ∂t 2 ∂t 2 where we have discretized continuum time according to t = kδt, and k is a discrete time index and δt is a discrete time increment. The discrete concentration is then referred to by ck+1 (r) = ck (r) +

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Figure 12: One-dimensional grid showing discrete locations of scalar type variables (at “O” positions, marked by full-integer indices) and vector type variables (at “X” positions, marked by half-integer indices). The two types of variables are spatially staggered with respect to one another. The spatial indexing is “i” and increases to the right. the k superscript index as, for example at the forward time as ck+1 . Formulating an analogous expression for the ice concentration at discrete time index k −1, we can subtract the resulting expression from (13.2) and arrive at a finite difference estimate of the time derivative as: ck+1 (r) − ck−1 (r) ∂c(r, t) = + O(δt 2 ), ∂t 2δt

(13.3)

where O(δt 2 ) represents neglected terms of order δt 2 and higher. We state then that the accuracy of this approximation is second-order in δt. This particular formulation of time discretisation is usually referred to as the “leap-frog” approach. The approach does have a drawback—it provides two solutions, one the physically correct solution and the other an oscillating incorrect one. The latter can easily be removed by application of a low-pass filter in time (Asselin, 1972). For spatial discretisation, we first note that the spatial derivative term in (13.1) involves both the concentration c and velocity u. For spatial discretisation, it is common practice to stagger the grids upon which scalar (i.e., concentration) and vector (i.e., velocity) quantities are defined. One such staggered arrangement is show in Figure 12. We represent the xderivative of the product term cu in (13.1) starting with the expansion

k k k ∂cu δx 1 ∂ 2 cu δx 2 k + ci+1/2 ui+1/2 = ci ui + (13.4) + ..., ∂x 2 2 ∂x 2 2 where we have discretized continuum space according to x = iδx, and i is a discrete space index and δx is a discrete space increment. The index can also have half-integer values, for example i + 1/2, corresponding to the location of the velocity grid points. Formulating an analogous expression for the product cu at discrete time index i − 1/2, we can subtract the resulting expression from (13.4) and arrive at a finite difference estimate of the space derivative as k

k uki−1/2 ci+1/2 uki+1/2 − ci−1/2 ∂cu (13.5) = + O(δx), ∂x δx which is a first-order in δx approximation. One difficulty with (13.5) is that unlike the velocity field, the concentration is not defined at half-integer points. It is defined at full-integer points. To overcome this, we estimate the discrete concentration, at half-integer indices, using the “upwind” values k ci+1/2 = cik k ci+1/2

=

k ci+1

where uki+1/2 > 0, where

uki+1/2

and

(13.6)

< 0.

This approximation involves another first-order error in δx. Our estimate of ∂(cu)/∂x still remains as being first-order accurate. An attractive property of the upwind scheme is that it is positivity-preserving and so it is monotone. It does not create any artificial extrema. An

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unattractive property is its low-order accuracy and its generally overly diffusive behaviour. This deficiency can be overcome by the application of an “anti-diffusive” corrections step which essentially eliminates the first-order truncation error, thus leaving the solution as being non-diffusive and second-order accurate (Smolarkiewicz, 1984). Putting together the temporal (13.3) and spatial (13.5) discretisations, our original continuum equation for ice concentration (13.1) becomes the computer-usable, discrete version k

δt k k+1 k−1 k k ci+1/2 ui+1/2 − ci−1/2 ui−1/2 + O δt 3 + O(δxδt). ci = ci + 2 (13.7) δx This numerical equation can be demonstrated to be consistent with the original continuum relation. By consistent we mean that in the limit as the grid resolution δx and time step δt both approach zero, the numerical equation reproduces exactly the continuum equation. In other words, consistency means that we are, at least, solving the correct equation. An important practical aspect of implementing the time and space discretisation is that the solution be numerically stable—that is it does not “blow up”. In the present case, our discrete equation will be stable, provide that the condition is met: δx

k δt u

i+1/2

1,

(13.8)

where uki+1/2 denotes the maximum velocity encountered anywhere on the grid (Durran, 1998). Stability is more than a practical requirement as it also gives confidence that the numerical solution obtained will ultimately converge to the solution of the original continuum equation. Our concentration equation is a linear equation in the sense that we are considering the velocity variable u to be a given quantity, i.e., akin to a forcing field. In that instance, the Lax-Equivalence theorem tells us that provided that our numerical equation is consistent with the continuum equation, and that the numerical solution is stable, then we have a necessary and sufficient condition for convergence (see details in Durran, 1998). Convergence simply means that in the limit as the grid resolution δx and time step δt both approach zero, the numerical solution becomes closer and closer to the continuum solution (which formally we do not know, and hence is why we use a numerical method). In other words, convergence means that we are obtaining the correct solution, albeit with a finite accuracy. This completes the description of the numerical solution of the one-dimensional concentration equation (13.1), and by generalisation, to the two-dimensional version (12.4). An analogous discretisation and solution technique is applied to the mass equation (12.2). The numerical treatment of the momentum equation (12.15) follows along much of the same treatment, but there are important, additional considerations pertaining to this equation. First, the treatment of the terms involving the Coriolis force require some spatial averaging, at least for staggered grids (as would be the case here) where the u and v components of u are not collocated. When solving the u velocity component, the Coriolis term involves the orthogonal velocity component v, which on a staggered, two-dimensional grid, will not be collocated with u. Additionally, for numerical stability, when using an explicit scheme like the leap-frog, the constraint f δt 1 must be adhered (Durran, 1998). Secondly, the numerical implementation of the rheology can lead to unrealistically small time steps, if not treated carefully. The plastic behaviour of ice has fast timescales associated with it, particularly during the transition from divergent to convergent flows, and thus can require excessive time resolution. The general technique for dealing with this problem is to add additional non-physical terms to the ice momentum equation, either of a viscous nature

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or an elastic one, such that the plastic behaviour is over taken by the viscous or elastic behaviour in problematic circumstances. The numerical trick lies in the ability to set the viscous or the elastic constants such that sufficiently large times steps can be taken. Having completed our brief sketch of the ice conservation laws and their numerical solution, we now review some past applications of these to the study of various polynya of the polar regions.

14

Regional Ice GCM Applications

While observed polynya range widely in their spatial and temporal scales, most ice GCM studies have concentrated on the larger-scale, more persistent variety. The names and locations of some of these are presented in Figure 13. The figure is not comprehensive, nor the discussion that follows, as modelling studies other than those to be presented been carried out for some of these same polynya. As well, there have been studies of polynya in areas other than those marked in the figure. The studies chosen, however, reflect those that have as their fundamental focus the explicit goal of modelling the ice cover. There are many polynyarelated studies that assess the impacts of a specified polynya shape and size in an atmosphere and/or ocean GCM. That genre of indirect polynya modelling, including the description of a multitude of feedback processes, will not be addressed here. We now describe a number of studies aimed at simulating polynya. For the most part, they solve ice equations similar to those outlined in Section 12 and use a numerical technique analogous to that of Section 13.

Figure 13: Locations of regional Arctic (left panel) and Antarctic (right panel) polynya which have been simulated with ice GCMs. The light yellow areas represent continent; the dark blue represent ocean areas that have frequent ice cover; and the light blue represent ocean areas that rarely have ice cover. The six Arctic polynya displayed are: 1, Storfjorden (Svalbard); 2, North-East Water (NEW); 3, North Water (NOW); 4, Hudson Bay; 5, St. Lawrence Island; and 6, Okhotsk Sea. The six Antarctic polynya displayed are: 7, Mertz Glacier; 8, Terra Nova Bay; 9, Ross Sea; 10, Ronne Shelf; 11, Weddell Sea; and 12, Cosmonaut Sea.

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Coastal-Ocean Polynya

The following is a brief survey of ice GCMs used in predicting the shape of coastal-ocean polynyas, both in the Arctic and Antarctic. There are ten such polynya discussed in this subsection. Their geographic locations are noted in Figure 13. They are referred to below by the numbers that appear on the maps in the figure. 1. Storfjorden (Svalbard): Remote sensing observations have shown a persistent polynya in Storfjorden. This polynya is a relatively small scale feature, but is included here as it serves as a typical example of a wind-driven coastal polynya. A simplified mathematical model of the ice dynamics and thermodynamics (Zyranov et al., 2003), similar to the ice GCM approach outlined in the previous sections, has shown that the opening and development of the polynya is reasonably accurately simulated. The study clearly illustrates that wind forcing is the dominant mechanism responsible for the existence of this polynya. 2. North-East Water (NEW): The waters off the northeast coast of Greenland are fed massive amounts of sea ice by the arctic ice advecting southward through the Fram Strait. Despite that feed, observations have indicated pockets of low ice concentration along this coast and a weakness in concentration across the adjacent Fram Strait. A coupled ice–ocean model (Holland et al., 1995), forced by monthly climatological atmospheric data, reproduced a polynya in this region, as well as a trough in ice concentration across the Fram Strait (see Figure 14). The key finding in the study was that the inclusion of a plastic rheology was fundamental to the creation and maintenance of the polynya. In a follow-on numerical sensitivity study, with the model ice in “free drift”, no polynya was formed. 3. North Water (NOW): Located in northern Baffin Bay, juxtaposed by Northern Greenland and Ellesmere Island, the north water polynya is among the more permanent, regularly recurring of polynya. An ice–ocean model forced by monthly climatological atmospheric data (Yao and Tang, 2003) was able to reproduce localised regions of thin ice where winds forced the model ice away from coastlines or fast ice zones. Although the modelling study did not specifically address the role of ice rheology in the maintenance of the polynya, satellite observations strongly suggest that an upstream blocking effect due to ice rheology also contributes to the low ice concentrations that constitute the NOW polynya. 4. Hudson Bay: Although it forms in an inland sea, the recurring polynya of the northwestern Hudson Bay, is a coastal polynya. A coupled ice–ocean model (Saucier et al., 2004) forced by inter-annually varying atmospheric data over the period 1996–1998 was able to reproduce aspects of the observed polynya. The simulation showed that wind forcing is the dominant mechanism responsible for the opening and the maintenance of this polynya. 5. St. Lawrence Island: In the Bering Sea, in the lee of the St. Lawrence Island, there is a regularly occurring polynya. A coupled atmosphere–ice model (Lynch et al., 1997) was able to simulate aspects of this polynya over the four-day period February 24–27, 1992. The persistence in direction of northerly winds was found to be the key mechanism in establishing this polynya downstream of the wind and in the lee of the island. Of secondary but notable importance, feedback processes between the opening of the modelled polynya and the stability of the modelled lower atmosphere were found to be important in producing a more accurate simulation of the polynya as compared to the satellite-observed one.

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Figure 14: An ice GCM simulation of a coastal polynya located in the northeast waters (NEW) of the Greenland Sea (Holland et al., 1995). The colours represent ice concentration, with dark blues being of 34% or lower and deep reds being of 86% or higher. The green colours represent land (Greenland on the left and the island of Spitsbergen near the center). The polynya are the low-ice concentration features along the northeast coast of Greenland.

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6. Okhotsk Sea: Along the northwestern shoals of the Okhotsk Sea, a variety of satellite observations have provided direct evidence of a polynya. A coupled ice–ocean model (Martin et al., 2004), forced by atmospheric data sets as well as by tides, was applied to this area. Under conditions of offshore wind-forcing, a polynya was simulated. While the winds provided a key mechanism for opening the ice cover, the tidal forcing was also noted to play an important role. In particular, the model simulations demonstrate that tidal mixing allowed deep, warm waters to reach the ocean surface and to contribute to polynya maintenance, via ice melting. 7. Mertz Glacier: In the lee of the Mertz Glacier floating ice tongue, to the north of Adelie Land, East Antarctica, there forms a persistent polynya. The polynya exists both due to the effect of the floating glacier as a barrier which prevent the sea ice from gathering on the western side of the glacier, but also due to the strong offshore katabatic winds in this area. A global coupled ice–ocean model (Marsland et al., 2004), using a warped grid that focused model resolution on the polynya area, was able to well reproduce the location and extent of this polynya. 8. Terra Nova Bay: Located off the east coast of Victoria Land, is a relatively small 0.005 × 106 km2 but persistent polynya. Its size is poorly correlated with the largescale wind forcing, suggesting that local katabatics are the main forcing mechanism. This polynya is in strong contrast to many Antarctic coastal polynyas which are forced by large, synoptic-scale winds. A coupled atmosphere–ice model (Gallee, 1997) has been able to simulate the dominantly katabatic-forced nature of this polynya. A secondary result from the modelling is that a feedback process between the polynya and the katabatic winds was seen. The feedback provided for a strengthening of the katabatics through an “ice-breeze” effect. 9. Ross Sea: In late spring, a polynya usually develops on the western Ross Sea continental shelf immediately north of the Ross Ice Shelf. A global ice–ocean model (Fichefet and Goosse, 1999) was able to simulate some aspect of this polynya, despite the relatively coarse resolution of the model in the area of the polynya. As a primary mechanism of forcing, strong winds were identified in the simulation. However, the import of warm deep waters from far offshore was also noticed to have measurable impact on the polynya through sensible heat effect. 10. Ronne Shelf: North of the Ronne Ice shelf, a polynya is usually evident during summer. It was particularly well pronounced in the satellite observational record during summer 1997–1998. A coupled ice–ocean model (Hunke and Ackley, 2001), forced by interannual winds of the 1997–1998 period, was able to reproduce the anomalously low ice cover of that summer. While offshore-winds were clearly the dominant factor controlling the polynya, it was also noticed that an ocean-albedo effect contributed substantially to the further development of the polynya. 14.2

Open-Ocean Polynya

The following is a brief survey of ice GCMs used in predicting the shape of open-ocean polynya, for the Antarctic only. There are two such polynya discussed in this subsection. Open-ocean polynya have a more rare occurrence than their coastal-ocean counterparts. Their geographic locations are noted in Figure 13. 11. Weddell Sea: This polynya is the largest ever observed. And it is among the most variable of all polynya. During the period 1974–1976, it persisted as a large area

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Figure 15: An ice GCM simulation of an open-ocean polynya, located near the Maud Rise seamount in the Weddell Sea (Holland, 2001a). This a plan view of the ocean surface, of horizontal dimensions 500 km in both directions. The black, dashed oval represents the outline of the Maud Rise, a topographic feature located well below the sea surface. The colours represent ice concentration, with dark greens being of 85% or lower, and deep reds representing up to full, 100% ice coverage. The blue patch represents an area of zero ice concentration—it is the polynya.

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of 0.20 × 106 km2 of low ice concentration. Since then, it has only reappeared as an occasional, transient area of 0.02×106 km2 (Lindsay et al., 2004). While, this polynya forms in the open ocean, well away from any coastal land boundary, the presence of a large seamount is believed to play a central role in its formation. A coupled ice– ocean simulation (Holland, 2001a, 2001b) has demonstrated that ocean currents near the seamount can induce a stress onto the ice cover causing it to open (Figure 15). In another coupled ice–ocean study (Beckmann et al., 2001), tidal currents were also demonstrated to have an impact on weakening the ice concentration in the vicinity of the seamount. 12. Cosmonaut Sea: Located in the Cosmonaut Sea, in an area to the east of the Weddell Polynya, is another open-ocean polynya. It’s appearance is also of a transient nature, having reached a peak size of 0.13 × 106 km2 in the winter of 1980, when it formed an embayment of open water in the surrounding ice. A regional atmosphere– ice model (Bailey et al., 2004) has demonstrated that the opening of the sea ice is highly correlated with the divergence in atmospheric flow associated with the passage of atmospheric, synoptic systems. From this brief survey, we can see that polynya occur for a complex of reasons, ranging from direct wind influences including strong katabatics, ocean currents, upwelling of ocean sensible heat, tidal influence, seamounts, and ice rheology, as a minimal list of factors. Future polynya research will undoubtedly refine this list as well as the details of the physics necessary for improved parameterisation. That will ultimately lead to more accurate and robust simulation of polynya in GCMs.

15

Conclusions

We have presented two modelling approaches now in common use to predict the shape of polynya. We have nominally labelled these approaches as ‘flux’ and ‘GCM’. These approaches, although quite distinct, are each in principle capable of describing the two archetype polynya, that is, those that form in the coastal ocean and those in the open ocean. Precisely which approach a researcher may wish to employ in practice, depends very much on the details of the application at hand. The flux modelling approach has, as its central goal, the description of a contour edge that delineates the separation of a frazil ice zone from a consolidated ice zone. This modelling approach, as described, uses specified states of the atmosphere and ocean. These states effectively serve as forcing fields to drive the governing ice equations. The equations themselves are conservation laws for mass and momentum of the frazil and consolidated ice. The laws are simplified, as for example, terms in the equations relating to Coriolis and ice rheology are omitted, as such terms are not judged to be of key importance in the simulation of the polynya contour edge. A key element in the solution of these equations is to first formulate an appropriate closure relating the thickness of the consolidated ice to that of other, known system parameters, such as the frazil ice thickness and the velocity of the frazil and consolidated ice. With such a parameterisation in hand, one is able to describe the temporal and spatial evolution of the polynya contour edge. The GCM modelling approach seeks to describe, not the exact contour delineating icefree and consolidated-ice waters, but rather the spatial variation in ice concentration. As such, the exact location of a polynya edge is inferred, based on some prior specification of what

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particular concentration distinguishes ice-free waters from those of consolidated-ice waters. This approach relies on solving the complete mass and momentum equations, including the specification of Coriolis and ice-rheology forces, over a spatial grid. For adequate resolution of a polynya edge, a relatively high-resolution grid is required. This can lead to substantial computing overhead, and is certainly one drawback to using this approach. On the other hand, the ability of a GCM to easily deal with arbitrary forcing fields and geometries, as well as a complete momentum balance, are among its main strengths. Each of these modelling approaches has strengths and weaknesses. We further elaborate on the most crucial aspects of these. A main strength of the flux approach is that it provides a relatively straightforward manner to describing the shape of a polynya edge. Using but a few simple conservation laws and forcing fields, one can readily describe the shape of a polynya edge with only modest computing effort, and in some instances the solution may even be obtained analytically. Because of its ease of application, and the relative transparency of its governing equations, this approach readily lends itself towards gaining conceptual insight into polynya dynamics. A weakness is that one has to parameterise the collection thickness, which involves some uncertainty because the nature of the parameterised relationship between model dependent variables is not at all obvious. The approach also has restricted application in terms of complexity. That is, with spatially varying forcing fields embedded in a spatially complex geometry, a purely numerical approach is ultimately needed, and at that point the GCM approach may indeed become far more practical. We have focused on the modelling of the shape of polynya, and have paid particular attention to the formulation of the mass and momentum balances of the ice. In doing so, we have assumed knowledge of many external factors, such as the state of the atmosphere and the ocean. These external factors provide the thermodynamic and dynamic forcing onto the sea ice. While the existence of a polynya does indeed modify the state of the atmosphere and ocean, the description and modelling of that level of feedback process is beyond that explored in this chapter. There is an evolving literature describing such interactive polynya modelling, but it is outside of the present scope. This chapter is intended to serve as an introduction to researchers interested in polynya modelling, and as such, it has focused on presenting those elements of polynya modelling felt to be of foundational importance to describing the shape of a polynya. We hope that the reader is now sufficiently familiar with the basic principles underlying the flux and GCM approaches to be able to begin using them to predict the shape of observed polynya, as well as to predict how such polynya may evolve in a changing global climate.

Acknowledgements AJW is grateful for the support of the UK NERC via research grants NER/T/S/2002/00425 (Rapid climate change programme) and NER/T/S/2000/00585 (Autosub under ice).

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Chapter 4

Meteorology and Atmosphere–Surface Coupling in and around Polynyas P.J. Minnett and E.L. Key Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL 33149, USA

Abstract Polynyas and the overlying atmosphere interact through a series of feedback mechanisms which impart a distinctive polar maritime character to the boundary layer over and downwind of the open water area. Enhanced turbulent fluxes across the ice-free interface introduce heat and moisture into the otherwise cold, dry polar atmosphere, modifying clouds through plume formation and radiative exchanges between the atmosphere and underlying surface. Anthropogenic aerosols of remote origin and local biogenic emissions provide additional direct and indirect radiative forcing, which may also influence precipitation rates, cloud optical depth, and ozone concentration. These combined effects modulate the efficacy of polar regions’ ability to act as a “heat sink” for the climate system, establishing a link between the regional polynya meteorology and global conditions. Models, gridded analyses, and remotely-sensed and validating measurements which describe the meteorology and feedback mechanisms in and around polynyas are discussed in this chapter, with an outlook toward future efforts and novel measurement and analytical techniques.

1 Introduction Polynyas are intimately linked to the state of the atmosphere above and about them. Surface winds and heat exchanges, both turbulent and radiative, directly couple polynya formation and maintenance with the overlying atmosphere (Andreas and Ackley, 1982). Above and downwind of the open water, increased turbulent fluxes facilitate heat and moisture transfer into the boundary layer, leading to cloud and plume formation, which influences cloud frequency, type, and distribution (Arbetter et al., 2004; Key et al., 2004). This, in turn, modifies the components of the surface radiation through increased emission in the longwave from cloud base and scattering in the shortwave by cloud droplets, and, thus, the cloud radiative forcing (CRF; Ramanathan et al., 1989). Variations in the surface albedo resulting from polynya formation, when the relatively dark, absorbing water surface replaces bright, reflective snow and ice, also affect the radiative profile (Andreas and Ackley, 1982), which feeds back into cloud radiative forcing and ultimately polynya growth (Kottmeier and Engelbart, 1992). Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74004-1

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Due to inherent cloud and ice cover variability, surface meteorological and radiative measurements within polynyas are limited by technological and logistical constraints and provide only a fragmented history. Rapidly transiting ice, icebergs, and currents preclude the use of above-surface mooring instrumentation, while the demolition of on-ice buoys by wildlife, ridging, and melt shortens data transmission and makes deployment cost-prohibitive. In situ, surface measurement is usually accomplished from ice-breaking, or ice-strengthened, research vessels; however, repeat deployments to polynyas are few, hindering robust study of seasonal or inter-annual variability, or boundary layer characterization during recent ice retreat and oscillating pressure patterns. Aircraft surveys provide additional measurements, particularly within-cloud microphysical fields and radiative fluxes necessary for accurate radiative transfer modeling. Naturally, these data sets are range- and time-limited and rarely visit polynyas or provide multi-year measurements within the same polynya. Such frequent, regional polynya coverage falls within the domain of a few high- and moderate-resolution polar orbiting satellites which image polynya areas with rapid orbital repeat sampling. The majority of polynyas, however, occupy spatial scales (O(1–10 kilometres (km))) which many current and most heritage spaceborne sensors are unable to adequately resolve. A few active sensors, e.g. SAR (acronyms are defined in the Table at the end of this chapter), are capable of penetrating multi-layered, persistent cloud and image the polynya surface at high resolution (O(10 metres (m))), but have relatively narrow swaths (O(10–100 km)) and infrequent coverage (3–5 days). This sampling issue not only affects the satellite time series, but also those analyses and models which utilize retrieved meteorological fields. The small size of most polynyas relative to the grid scale of Numerical Weather Prediction (NWP) and climate models, the frequency of polynya formation close to coasts, the scarcity of polar measurements to be assimilated by the models, and also those independent measurements necessary to validate their predictions, mean that the model fields are of uncertain accuracy and validity in the vicinity of polynyas. Since polynyas and many sub-grid scale features (e.g., leads, straits, islands of the Canadian Archipelago) are inadequately captured by the large model grids and their meteorology insufficiently sampled by in situ and space-borne instrumentation, their influence on local and regional atmospheric fields is largely unknown and limited to a few representative polynyas. There is a great temptation to extend the meteorological measurements taken at coastal and inland reporting stations or central Arctic buoys (http://iabp.apl.washington.edu) to the polynyas themselves. While this is a good first approximation, and one that works better for some variables (e.g., barometric pressure) than others, it is likely to fail to account for the influence of the polynya on the local meteorology. Furthermore, in polar regions, meteorological reporting stations and buoys are sparsely distributed, often influenced by local topography or ice conditions, and in some cases sporadically maintained, or only seasonally operational. A handful of specialized land-based installations, such as the ARM site at the North Slope of Alaska (Stokes and Schwartz 1994; Stamnes et al., 1999), feature instruments that do not constitute part of standard measurement suites, such as lidars and wind profilers, which add to our knowledge of the vertical distribution of certain atmospheric fields. Extensive observations from drifting ice camps (Gordienko, 1962; Arctic Climatology Project, 2000) and ice stations contribute significantly to the meteorological archive; but by definition characterize conditions over-ice which are not necessarily representative of polynya meteorology. Over the past two decades, sampling efforts within polynyas have increased, furthered by national and international committees, such as the International Arctic Polynya Programme. Repeat expeditions to the Northeast Water, North Water, and Cape Bathurst polynyas provide

Meteorology and Atmosphere–Surface Coupling in and around Polynyas

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foundation datasets on which much of our understanding of polynya meteorology is based. In the Antarctic, a combination of opportunistic data from re-supply missions to coastal stations and polynya-centric field programs (e.g., ROPEX; Mertz Polynya Experiment: Roberts et al., 2001; Terra Nova Bay expeditions: Parish and Bromwich, 1989) describe the boundary layer over the larger, recurring southern hemisphere polynyas. The surface meteorological forcing provides a unifying mechanism for the formation and maintenance of polynyas in both hemispheres. In situ measurements of atmospheric properties and related surface fluxes require data from multiple sensors, mounted on a stable platform, such as an ice-breaking research vessel. Due to changing ice and light conditions, however, the majority of available shipboard meteorological time series are limited to April to October in the Arctic and September to March near Antarctica when ease of passage and the number of daylight hours are maximized. Even during spring and summer, polar regions present particular challenges to accurate meteorological measurements. The environment is harsh to both instruments and observers alike, both often functioning at the limits of their working ranges and endurance. Instruments can freeze or suffer the effects of ice build up or ice melt. Under conditions of blizzard and frost, instruments can be covered by an insulating or obscuring blanket. While careful maintenance of the instruments can help mitigate these effects, sometimes it is impractical or unsafe to try to clean or service the sensors when they are most in need of attention. Additional problems in shipboard measurement may arise from the ship’s motion, flow distortion around the ship, and icing. Some steps can be taken to limit these effects, such as careful siting of the instruments on foremast towers and mounting radiometers on gimbals, but sometimes it is necessary to simply discard segments of time-series of measurements where the measurements are known to be uncorrectable, e.g. when an anemometer is in the lee of the ship’s superstructure, or a pyranometer is in shadow. 1.1

Polynya Formation

The positions of the recurrent polynyas are shown in an earlier chapter (Barber and Massom, 2007). Many of these open water areas occur close to coasts and are influenced by local winds and surface heat fluxes. Polynyas have been partitioned into two types according to the physical mechanisms for their formation and maintenance: sensible-heat and latent-heat polynyas (Smith et al., 1990). These designations indicate the primary source of heat used to melt the ice, or limit its formation. Sensible-heat polynyas require a source of heat in the water column that is brought to the surface by upwelling, possibly in response to an alongshore or offshore wind. Paradoxically, latent-heat polynyas are a result of ice formation caused by heat loss to the atmosphere: this releases the latent heat of fusion, and surface winds or currents move the newly formed ice downstream. For this mechanism to work there has to be a barrier that prevents, or restricts, the inflow of ice into the open water. In the North Water, this is an ice arch that causes a log-jam in Smith Sound, to the north, and prevents the inflow of ice from Nares Strait (Barber et al., 2001; Vincent and Marsden, 2001). In the Northeast Water Polynya, there are two barriers: to the south an ice shelf that extends as a glacier tongue which is grounded on Belgica Bank, and which obstructs inflow of ice by the anticyclonic circulation over the bank, and to the north a land-fast ice shelf over the Ob Bank that deflects the flow out of the Arctic Ocean through Fram Strait (Schneider and Budéus, 1995; Minnett et al., 1997; Willmott et al., 1997). Winds blowing over St. Lawrence Island Polynya open a polynya on the downwind side of the island (Pease, 1987) while the Sea of Okhotsk Polynya is formed by offshore winds blowing from Siberia over the shallow

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continental shelf (Martin et al., 1998). Some polynyas, such as the Hells Gate Polynya and Lady Anne Strait Polynya in the Canadian archipelago (Smith and Rigby, 1981), Kashevarov Bank Polynya in the Sea of Okhotsk (Martin et al., 2004), and the Great Siberian Polynya in the East Siberian and Laptev Seas (Kowalik and Proshutinsky, 1994), maintain ice-free conditions through association with very strong tidal flows that presumably mix the heat in the otherwise stratified water column to the surface; although, log-jam type blockages of ice flow in the narrows between islands may be important in the case of Hells Gate and Lady Anne Strait. There are those that may exhibit different characteristics at different times or under different conditions. For example, there is evidence that the North Water has a sensible heat component during winter (Steffen, 1985), while in summer it is a latent heat polynya (Ingram et al., 2002). Similar mechanisms give rise to recurrent polynyas on the Antarctic coast and continental shelves (König-Langlo et al., 1998). For example, the Ross Bay Polynya is formed by katabatic winds removing coastal ice and maintained by latent heat release of ice crystals forming in the open water area (Bromwich and Kurtz, 1984). Other open water areas, such as the Brunt Ice Shelf (Markus and Burns, 1995), Mertz Glacier (Massom et al., 1998), Prydz Bay (Nunes Vaz and Lennon, 1996), and Terra Nova Bay (Kurtz and Bromwich, 1983) polynyas, form in response to similar wind forcing events and are maintained through a combination of winds and barriers to ice inflow, such as grounded ice and promontories. Tidal flows in shallow coastal areas also deform ice cover leading to the creation of open water areas like the Ronne Ice Shelf Polynya in the Weddell Sea (Renfrew et al., 2002). Further offshore, sensible-heat mechanisms dominate the Cosmonaut and Weddell Sea polynyas, which are seemingly sustained by local thermohaline convection driven by a sinking cold, brine layer under newly-forming ice and consequent upwelling of relatively warm water from depth (Martinson et al., 1981; Comiso and Gordon, 1996). Vertical motion induced by current flow around bottom topography, such as the Maud Rise Seamount, can also lead to recurrent offshore polynya formation (Holland, 2001). While the physical processes that cause polynyas to form and be sustained can vary and may involve the overlying atmosphere to differing degrees, they are united by a common definition: sizeable areas of open water in otherwise ice-covered seas. It is this lack of an insulating ice layer that permits robust air-sea interaction throughout the open water area and complex air–ice–ocean feedbacks at its periphery.

2

Measurements of Meteorological Variables

While the instruments used to take measurements of surface meteorology in and around polynyas are not described in detail here, an overview of common sensors and operational considerations is provided. Further information about advanced instrumentation is outside the scope of this chapter but available in DeFelice (1998), http://www.arm.gov/sites/nsa.htm, and papers cited within this text. Generally, measurement of polar meteorology is achieved through shipboard expeditions, aircraft surveys, or manned observing stations. These sampling platforms, however, are relatively sparsely distributed, expensive to maintain, and are being replaced by automatic stations. Meteorology recorded by unmanned sensors incur additional uncertainty as weather conditions or biological fouling may affect operations and data quality. A good example is the effects of freezing rain on anemometers, which may become inoperative once they are encased in ice. This is frequently seen in the data from the automated station on the low-lying island of Henrik Krøyer Holme in the Northeast Water


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Polynya (H. Valeur, pers. comm., 1994; Minnett et al., 1997). Descriptions of some standard sensors and more advanced, research instrumentation at the ARM site at Barrow, Alaska, are accessible at http://www.arm.gov/sites/nsa.stm. Additional information about instruments, such as those deployed on aircraft, is provided in the appropriate references cited below. Sampling meteorological fields from a ship requires a number of sensors, which are sited in such a way as to minimize structural contamination—e.g., cloud imagers are placed in areas devoid of overhanging or obscuring deck or antennas, while anemometers are located on masts which raise the sensor off the deck to minimize shadowing and flow distortion around the ship’s superstructure. Aerosol sensors aboard ship are placed away from the smokestack, though screening of the data is necessary for periods of shifting wind direction when the smoke may affect not only particle count but also tandem radiation measurements. Usually, basic meteorological variables, such as surface air temperature, relative humidity, barometric pressure, and wind speed and direction are recorded by an instrument suite, such as can be found both on ships and at weather stations at high latitudes. However, the accuracy of these measurements in freezing conditions and sub-zero temperatures is low, requiring specialized sensors for the most reliable values. For instance, relative humidity, as measured by a frost point hygrometer, is not susceptible to freezing failures and provides a highly accurate measurement of relative humidity over ice, not provided by traditional dielectric film sensors. Wind vanes, sonic anemometers, and 2π -radiometer domes are also prone to malfunction at cold temperatures should seaspray or hoarfrost coat the instrument. Unfortunately, new technology has not addressed these measurements. Thus, rigorous maintenance becomes necessary for shipboard operations because of the added threats of salt and moisture, which do not affect most land-based weather stations. Instrumentation for measuring cloud is manifold, ranging in complexity from the robust all-sky camera unit (Figure 1) used for capturing time-lapse images of hemispheric cloud cover to the combined radar-lidar array currently only deployed at the ARM NSA-AAO site for cloud microphysical studies. Autonomous all-sky imaging units, also used at the NSA site, though practical, are subject to misclassification errors by cloud identification algorithms (Buch et al., 1995). To assess the influence of cloud cover on the underlying snow, ice, or ocean surface, coincident broadband measurements of hemispheric downwelling shortand longwave radiation must be collected. Calculations of net cloud forcing, which is measure of the radiative flux and surface response, require highly accurate, rapid sampling of upwelling radiation, and thus, albedo. Despite the importance of albedo and radiative fluxes to estimates of the surface heat budget, few measurements are available in the Arctic, and even less within dynamic polynya areas. 2.1

Winds

Wind serves both to open and maintain some polynyas first through divergence, then through enhanced ice formation and redistribution; the combination of latent heat release from freezing and the advection of new ice downwind is crucial to the expansion of open water. Since few in situ measurements of polynya wind speed and direction are readily available and satellite retrievals of wind do not provide adequate spatio-temporal resolution, most studies of wind-related effects on polynya size, shape, and duration have been based on models (e.g., Lynch et al., 1997; Goosse and Fichefet, 2001). Many of these assume geostrophic wind patterns, though several regional or box models incorporate the prevailing flow to study the optimal ice removal and thus polynya size, as a function of wind speed and small deviations in wind direction. In reality, the wind fields over polynyas are quite complex, as the wind

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(a)

(b)

Figure 1: Examples of meteorological sensors used to make measurements in polynyas. The instruments mounted on the mast on the foredeck of the Canadian Coast Guard Ship Pierre Radisson (left) measure wind speed and direction, air temperature and humidity and atmospheric pressure. The cross-beam supports 2π-radiometers to measure hemispheric incident short- and long-wave radiation. These are mounted on gimbals, with pendula, to keep the sensors pointing upwards and remove the effects of ship motion. The all-sky camera (right) consists of a down-looking video camera pointing at a hemispheric mirror that provides an image of the entire dome of the sky. The images are recorded by a time-lapse recorder for subsequent analysis. stress over bare, snow-covered, and ponded ice and open water may differ markedly (Chao, 1999). Katabatic flow off nearby ice shelves may contribute additional complexity, particularly along and offshore the Antarctic coast; however, in the Arctic, these wind events are often unsampled over polynyas and must be extrapolated from point measurements at coastal weather stations. Since many coastal weather stations are located in sheltered areas, ill-exposed to the prevailing wind, the wind time series are not necessarily representative of large-scale flow offshore. Extrapolating measurements from shore to a polynya may bias the wind speed and favor directions along prominent topographic axes. For instance, shipboard measurements within the North Water Polynya reveal dominant northerly winds averaging speeds 1.5–4.5 m s−1 greater than those measured at coastal stations, most of which are situated in fjords or leeward of low-relief (O(100 m)) topography. While wind directions within these sheltered locales did capture much of the northerly flow, secondary peaks in wind speed manifested in directions coincident with local topography. It is possible that in some of these cases, the data record has captured katabatic drainage off of Ellesmere or Greenland ice fields and glaciers. In situ measurements and modeling studies of the Greenlandic wind field (Bromwich et al., 1996; Heinemann, 1999) characterize katabatic flow as a boundary layer jet, increasing in speed from the central ice cap towards the coast, reaching speeds of up to 25 m s−1 , and exerting influence over long horizontal scales. Although these thermallyinduced jets occur year-round, either in response to nighttime radiative cooling or daytime ice melt, they exhibit pronounced seasonal and diurnal variability.


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Figure 2: OLS Image of katabatic winds blowing over open water in the Ross Sea, Antarctica. Streamers of frazil ice forming at the surface are oriented in the direction of air flow. (Courtesy of arcane.ucsd.edu). In Antarctica, a combination of coastal katabatic wind forcing and synoptic cyclones accounts for the wind-driven component of polynya formation along the coast (Figure 2). The measured ice-free area fluctuates with the passage of these wind events unless assisted by grounded-ice features, which restrict or block the return flow of ice into the polynya. The Ronne Ice Shelf and Terra Nova Bay Polynyas are formed by the combination of highspeed wind drainage from converging, steeply-sloping coastal topography, semi-permanent cyclonic upper air flow, and upwind ice tongues within the Ross and Weddell Seas. A number of polynyas have also been identified under the Atmospheric Convergence Line, at approximately 60◦ S, where ice divergence and subsequent oceanic upwelling facilitates the formation of hybrid latent-sensible heat polynyas. Similar strong wind conditions are also noted in the Arctic, particularly in the Bering Sea in the vicinity of St. Lawrence Island, where a latent-heat polynya forms in shallow inshore waters in response to local wind forcing. The polynya may take shape either on the northern or southern shore of St. Lawrence Island, and shows a distinct size dependence on not only wind speed but also direction. While the island itself is of low relief (less than 600 m), the two largest mountains on the island shadow the only two weather stations, and thus distort the only generally available wind data from this area. Assumptions of geostrophy over a large scale have been made in the Bering Sea, using meteorological station data from Siberian and Alaskan coasts, although topographic effects may introduce error into the calculation—Siberian stations, such as Anadyr and Uelen, are located in enclosed bays and on icy capes while Alaskan stations may either be found in valleys or on exposed coastal plains.

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Figure 3: Schematic of surface ice drift during negative (low) and positive (high) phases of the Arctic Oscillation. From AMAP (2002). Even distant topography can influence wind measurements. For example, the Brooks Range in northern Alaska sits 150 km from Barter Island, a coastal weather station near to the Beaufort Flaw Lead and Banks Island and Cape Bathurst Polynyas. A narrow range of wind directions introduce strong forcing into the data record at this station, where winds reach more than 20 m s−1 in both the easterly and westerly directions as channeled by the orientation of the mountain range (Dickey, 1961). Using these measurements as an indicator of at-sea wind is at odds with the large-scale flow of the Beaufort atmospheric anticyclone, which favors primarily alongshore (easterly) winds over the open water, except when the wind pattern is shifted southward during positive phases of the Arctic Oscillation (Figure 3). At that time, the flow becomes onshore near the US–Canadian border while an offshore arm blows over Wainwright, to the west, bringing with it Pacific air masses, cyclones, and aerosols. Analyses by Adolphs and Wendler (1995), Andreas and Cash (1999), and Marsden et al. (2004) address the role of wind forcing in setting up a polynya microclimate which aids maintaining the open water area. Assumptions of geostrophy and boundary layer length scales guide these analyses, which proffer that cyclonic/anticyclonic wind fields develop over the center of polynyas, guiding ice redistribution and encouraging upwelling of relatively warm subsurface water along the periphery. 2.2

Surface Temperature

As strong winds blow over sea ice, creating areas of ice divergence, open water surfaces are exposed to the overlying atmosphere. Continued wind forcing enhances heat transfer from the relatively warm upper ocean into the atmospheric boundary layer. Despite this negative heat exchange from ocean to atmosphere, the air–sea temperature differences may be large (|T | > 20 K), especially in comparison to air–ice temperature differences. Analyses of similar differences in polynya and land-based air temperatures reveal more than simple divergence in heat capacity and content: seasonal and episodic variations, the latter related to katabatic drainage events and topographic shadowing of coastal weather stations, also factor into polynya–land meteorological differences. Although topographic influences and downsloping flow may account for a small fraction of land–sea air temperature differences,


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Figure 4: Evolution of surface air temperature over the North Water Polynya (blue line) and nearby coastal stations (all other colors). The polynya-coastal difference in air temperature shown at the right captures the progression in ice and snow melt in late spring and early summer. Air temperatures over the coast rise as bare land is exposed, while the open water and continued ice melt within the polynya modulates temperatures around 0◦ C. From Key (2004). it is mostly the disparity in heat capacities of soil, bare rock, snow, ice, and ocean that produce a near-uniform negative temperature gradient between land and sea in summer. Over the polynya, cooler surface air temperatures prevail with smaller diurnal ranges than measured at neighboring coastal sites. Whereas the increase in solar radiation over summer melts snow and warms bare soil and rock, most of this energy within polynyas is expended on ice melt, further moderating polynya temperatures to generally within a few degrees of 0◦ C (Figure 4; Anderson, 1993; Key, 2004). In areas where open water is extensive and semi-permanent, land–sea air temperature differences from the oceanic surface layer, whereas coastal land margin heat content quickly dissipates. During transitional seasons—spring and fall—when the rapid radiation changes and ice development are anti-phase, other factors may govern the land–sea air temperature differences. In the St. Lawrence Island Polynya during spring, for example, advection of warm sub-polar air by large-scale southerly flow and convective heat release from the shallow Bering Sea under intense wind forcing may account for the warm air temperatures over the polynya with respect to land. Also, at this time of year, cloud cover over the Bering Sea is extensive and persistent, so that the local snow cover at coastal weather stations does not receive adequate solar radiation to accelerate the melt. Late summer and early fall observations of the Barrow Flaw Lead, after significant summertime insolation had induced wide-scale melt and a large open water area in the Beaufort Sea, show a high amplitude diurnal signal of several degrees in surface air temperature. Although these daily fluctuations are still smaller than those measured at coastal weather stations, their appearance suggests that under minimal cloud and ice conditions, Arctic air surface temperatures exhibit diurnal behavior similar to that measured over sub-polar ice-free oceans. Both the Arctic and Antarctic share a proclivity towards near-surface temperature inversions (King, 1990; Serreze et al., 1992), which, though occurring year-round, manifest most strongly in winter months. These inversions complicate remote sensing retrievals of surface air temperature and cloud cover, since fog, stratus, and stratocumulus clouds (Kottmeier and Engelbart, 1992) within the inversion tend to have warmer cloud top temperatures than the underlying surface. Space-borne sounder and in situ radiosonde profiles indicate that the depth of the boundary layer beneath the inversion layers varies from 300–500 m, while the inversion layer depth itself ranges seasonally, reaching a maximum depth in early spring (Anderson, 1993). Turbulent mixing, which increases with decreasing sea ice cover, erodes this maximum over and downwind of polynyas and leads (Liu and Key, 2003), allowing additional heat and moisture fluxes into the troposphere (Andreas, 1980).

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In cloud-free conditions, the temperature of the ice-ocean surface, as opposed to the near-surface air-temperature, can be determined by infrared radiometers on polar-orbiting satellites. The long-time series of the AVHRR on the NOAA satellites has two channels in the thermal infrared which can be combined to provide a correction for the effects of the intervening atmosphere, and therefore obtain clear-sky measurements of ice and water surface temperatures (Key and Haefliger, 1992; May et al., 1998). AVHRR’s approximately 1 km2 resolution at the surface is very well suited to regional polynya sampling. While the measurements of microwave radiometers on spacecraft are largely unaffected by clouds, they suffer from relatively poor spatial resolution. The delineation of larger polynyas can be achieved with high frequency channels (85 GHz on SSM/I, 89 GHz on AMSR-E) and analytical techniques to provide ground resolution of less than 10 km (Markus and Burns, 1995). The measurement of surface temperature requires information from the lower frequency bands at 6.9 GHz (AMSR-E) that have correspondingly poor surface resolution and contamination by emission from land through the antenna side-lobes. This renders them unsuited to provide useful measurements of surface temperature in polynyas. 2.3

Surface Humidity

Humidity is a quantity not often measured at the surface in polar regions, due in part to the low saturation levels with respect to water and uncertainties associated with common measuring techniques. With the exception of certain specialized sites, such as Halley Station (75◦ 35 S, 26◦ 25 W) where a frostpoint hygrometer records relative humidity with respect to both ice and water (King and Anderson, 1999), most coastal weather stations near polynyas derive humidity from dewpoint and drybulb temperatures, which decrease in accuracy with decreasing surface air temperatures (King and Turner, 1997). Other sensor types which may be used on shipboard and aircraft surveys of polynyas to detect relative humidities must be cold temperature-calibrated (Anderson, 1994), and yet may still be unable to measure accurately humidities below the ice point. Vertical profiles of humidity are monitored through radiosonde launches at upper air stations and from ships as well as satellite sounding units (e.g., TOVS, Francis and Schweiger, 2000; AMSU-B, SSM/T2, Heygster et al., 2003). Aside from the systematic low bias of rawinsonde humidities (Cullather et al., 1998; Miloshevich et al., 2001; Miloshevich et al., 2004) and uncertainties in the calibration of TOVS (Overland et al., 2002), the spatial distribution of humidity profiles (less than 5 surface observations per 2.5 pixel per month at high latitudes, Kistler et al., 2001, or 100 × 100 km2 pixels, Francis, 2002) does not adequately sample rapid fluctuations, the boundary layer, or downwind polynya influences on moisture. Consequently, model and re-analysis products tend to overestimate low level humidity (Rogers et al., 2001), leading to a positive bias in longwave radiative forcing and surface air temperatures (Overland et al., 1997). However, due to the general lack of available relative humidity measurements, most studies use NWP output for climatological analyses (e.g., Barber et al., 2001), model initialization (e.g., Häkkinen and Geiger, 2000; Armstrong et al., 2003), and parameterization of cloud cover (e.g., Overland et al., 1997; Hall, 2004). The few in situ time series collected within polynyas have focused on the turbulent transfer of humidity into the upper boundary layer (Pinto et al., 1999; Curry et al., 2000) and the consequent effect on cloud formation downwind of the open water area (Burk et al., 1997; Key et al., 2004). Large air–sea temperature differences across the polynya interface, coupled with wind forcing, generate a significant sensible heat and evaporative flux into the atmosphere (Burk and Thompson, 1995; Popov, 2001). The moisture entering the


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atmospheric surface layer is vertically transported by turbulent eddies, resulting in an increasing relative humidity profile peaking at the top of the boundary layer. These convective plumes, which may measure from 600 to 1000 m in height, are comprised mainly of supercooled water while still coupled to the relatively warm polynya atmosphere but can become mixed phase clouds as they are advected downwind beyond the polynya boundary and over ice (Pease, 1987; Mailhot et al., 2002; Zulauf and Krueger, 2003). Acting as a source of moisture and CCN, the plumes seed cloud formation, both over the polynya and downwind, increasing cloud incidence and fraction (Fett et al., 1997; Key et al., 2004). Relative humidities measured over polynyas generally exceed upwind coastal observations by 20% (Key, 2004), and also exhibit greater variability at a range of temporal scales, fluctuating between 45 and 100% depending on the temperature-induced changes in the saturation vapor pressure, proximity of the polar vortex and synoptic mesoscale weather systems (Overland et al., 1997). 2.4

Profiles

As in other regions, most of our knowledge of atmospheric profiles of temperature and humidity at high latitudes has been obtained from radiosonde ascents. Radiosondes are routinely launched from Arctic upper air observing stations, which tend to be clustered at the coast or inland settlements. The relatively few launches made offshore, either over ice or open water, are often restricted to ships and large floating ice stations by the operational requirements of sounding units (e.g., available helium, power, antenna line-of-sight, and sheltered sonde preparation areas). Satellite-derived profiles augment this sparse in situ data distribution, though at a much-reduced spatial, vertical, and temporal resolution. Large pixel sizes of spaceborne soundings, such as the TOVS 100 km product, often sample over varied ocean, ice, and landscapes, merging the atmospheric characteristics of a marine, icecovered and coastal region into a single profile. Despite complications presented by differing scenes and minimal vertical resolution, TOVS temperature profiles collocated with radiosondes launched over the Beaufort Flaw Lead and ARM NSA site show good agreement above the boundary layer (Figure 5; Francis, 2002; Key et al., unpublished). At the surface, air temperature values reflect whether snow, ice, ocean, or bare soil are present, introducing discrepancies between the various profiles equivalent to several degrees at the lower boundary. Relative humidity retrievals, on the other hand, are quite disparate, describing a more localized environment, both in terms of cloudiness and moisture flux in the boundary layer. Profiles collected at locations a minimal distance apart but over different surfaces are as dissimilar as the spaceborne profile is from either of the in situ soundings. Furthermore, a known dry bias in radiosonde relative humidity among the Vaisala RS-80 series (Miloshevich et al., 2001) is surpassed by the remotely-sensed humidity profile, as well as by NWP products, indicating a tendency for these large-pixel profiles to bias towards drier land-based environments. Accurate retrievals of relative humidity, at least from the high resolution radiosonde soundings and lidar profiles, are useful for identifying laminar stratus (Herman and Goody, 1976) and multilayered clouds, especially during overcast and foggy (Curry et al., 1995a) conditions. With improvements to the radiosonde calibration unit for recent radiosondes and profiles derived from spectra collected in the rotational water vapor window (19–25 μm) by the extended-range AERI (AERI-ER, Tobin et al., 1999), evolving turbulent moisture fluxes can be detected within and near polynyas.

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Figure 5: Beaufort Flaw Lead (BFL). Blue temperature and cyan humidity curves from an independent radiosonde launch within the BFL are shown in reference to radiosondes launched from the NSA-AAO ARM site (top left) and the Barrow NWS station (top right), as well as MODIS retrievals (bottom left) and the NCEP Final Analysis one-degree product (bottom right). In all cases, the independent sounding was interpolated to match the vertical resolution of the comparable dataset. Spatio-temporal distances between the soundings are given for reference in each panel.


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Figure 6: Analysis of surface-based cloud observations for field expeditions to Arctic Polynyas during the sunlit seasons over several years. The bar graph gives cloud amount for each observational data-set: NEW92—Northeast Water Polynya in 1992; NEW93—Northeast Water Polynya in 1993; CH98—Cape Hershel in 1998; NOW98—North Water Polynya in 1998; SLIP99—St. Lawrence Island Polynya in 1999; NOW99—North Water Polynya in 1999; BFL00—Barrow Flaw Lead in 2000. These results indicate that the Arctic is predominantly cloudy. The data set with the highest incidence of clear skies (CH98) is the only land-fast site, located on the coast of Ellesmere Island at the northern edge of the North Water Polynya. From Key et al. (2004). 2.5

Clouds

Cloudiness is frequent and extensive in the Arctic, and particularly over polynyas, where clouds are reported in 70–85% of daylight measurements (Figure 6). As mentioned in Section 2.3, enhanced moisture and CCN fluxes across the air–sea boundary in polynyas contribute to this elevated cloud occurrence (Bromwich et al., 2001), which influences not only the polynya but also areas downwind (Figure 7; Adolphs and Wendler, 1995). In situ measurements over both Arctic (Key et al., 2004) and Antarctic (Arbetter et al., 2004) polynyas reveal a shift in cloud distribution towards multiple cloud types at several atmospheric levels and an elevated occurrence of cumuliform cloud types, compared with observations over sea and landfast ice (Figure 8; Key et al., 2004). Internal cloud properties, such as optical depth, cloud liquid water content, cloud droplet phase, and particle effective radius determine the radiative character of the cloud, as well as the nature and magnitude of its effect on the boundary layer and underlying surface. Studies conducted with a polar-optimized radiative transfer model (Streamer: Key and Schweiger, 1998) indicate that downwelling radiation is more sensitive to changes in the cloud particle effective radius than variations in the cloud liquid water content (Key, 2004), especially at smaller solar zenith angles. This susceptibility to cloud microphysical content thus figures significantly into cloud radiative forcing and cloud-albedo feedback (Curry et al.,

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(a)

(b) Figure 7: An atmospheric plume emanating from the polynya southeast of Bennett Island in the Eastern Siberian Sea, as imaged by the NOAA-6 satellite (top). From Dethleff (1994). The lower panel shows simulated mean total vertical turbulent temperature flux (K; m s−1 ) from a 200 m lead, orientated along the wind direction, from simulations using a cloud-resolving model (scaled by 103 ). From Zulauf and Krueger (2003).


(a)

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Figure 8: Spatial distribution of cloud forms over three polynyas and one flaw lead. Each pair of images consists of a top panel showing east–west distribution of stratiform (blue), cumuliform (red), cirriform (yellow), multiple cloud types (green), and clear sky (black) across each open water area. The bottom panel of each pair depicts north–south gradients in observed cloud form for, from left to right, (a) the St. Lawrence Island Polynya, Beaufort Flaw Lead, (b) North Water (summer), North Water (fall), and Northeast Water. From Key et al. (2004).

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Figure 8: (Continued.)


(b)


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1993, 1995b; Key, 2004). As these effects play a substantial role in the maintenance or modification of sea ice patterns, including those critical to polynya formation such as the stability of ice bridges and grounded ice tongues and the rate of ice melt at the polynya boundaries (Shine and Crane, 1984), additional measurements for model testing and parameterization improvement are necessary. Aircraft surveys, such as BASE (Pinto and Curry, 1995) and FIRE-ACE (Curry et al., 2000) provide comprehensive, though brief, time series of cloud and radiation properties during daylit months. However, long-term measurements of cloud and radiative components germane to calculations of the forcing are very limited over polynyas and largely confined to those derived from satellite measurements (e.g., Schweiger et al., 1999; Francis, 2002; Spangenberg et al., 2004). In fact, consistent measurement of any cloud parameter within the Arctic has generally relied on the rapid repeat of polar-orbiting satellites for adequate coverage. While today and in the near future (Section 5), a number of space-borne sensors will supply Arctic cloud products, most existing cloud histories are based on radiances retrieved from the extensive record of the NOAA AVHRR series. Two climatological products, from the International Satellite Cloud Climatology Project (ISCCP: Schiffer and Rossow, 1983) and the Cloud And Surface Parameter Retrieval software (CASPR: Key, 2002) estimate cloud amount and microphysics using AVHRR as a baseline dataset. Additional information from the TOVS sensors and gridded meteorological fields help constrain the algorithms which generate the final cloud fields. In the case of ISCCP, however, the measurement of cloud cover remains hampered by the lack of thermal and visible contrast between sea ice and cloud cover and the effect of illumination within a complex ice-ocean scene. When collocated with in situ assessments of cloud cover, the ISCCP diurnal cycle overestimates cloud cover in low illumination and underestimates during the rest of the day by as much as 50% (Key et al., 2004). Despite these discrepancies, remote sensing imagery is the preferred data source for regional analyses of cloud cover. 2.6

Aerosols

The proximity of the Arctic to large sources of North American and Eurasian pollution aerosols and the exchange of air masses with mid-latitudes during the migration of the polar front introduce a large concentration of sulfuric aerosol to high northern latitudes during winter and early spring (Figure 9, Shaw, 1982). These anthropogenic aerosols, which are mostly composed of sulfur dioxide and soot particles (Hara et al., 2003), are quickly scavenged by cloud formation in the boundary layer in April and May (Bigg and Leck, 2001; Dong and Mace, 2003). Those sulfur dioxide particles, which are vertically mixed above 100 m (Bigg and Leck, 2001), may be bolstered by additional inputs from Eurasia in winter and spring and remain aloft for days to weeks, during which time they catalyze ozone destruction through the activation of atmospheric halogenic species (Barrie et al., 1994). Over Alaska, the Beaufort Sea, and the Canadian Archipelago, thin horizontal (O(100 km)) layers of industrial pollution form within 2–4 km of the ocean–ice surface (Shaw and Khalil, 1989) in winter and spring. These seasonal Arctic Haze layers may increase the aerosol optical depth over the area to as high as 0.5 (Stone, 1997); act as ice forming nuclei (Bigg and Leck, 2001); enhance the reflectivity of the cloud; increase the longwave emissivity (Garrett et al., 2002); and alter the cloud’s radiative forcing properties. After this peak in primarily anthropogenic aerosol is depleted in late spring, a second surge of aerosol occurs in May and June, associated with phytoplankton production. These inputs are derived primarily from polynyas and leads, where gas exchange, nutrient and

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Figure 9: Five-day aerosol trajectories showing the transpolar influx of Eurasian aerosol to the western Arctic (from Shaw, 1982). The trajectories indicate pathways from the industrial regions of Europe over the Laptev Seas and East Siberian Sea and into the Western Arctic. The day of year in 1982 on which the trajectories reached central Alaska is shown by the numbers, and the tick-marks are 24 h intervals. Solid lines designate hazy conditions, and dashed lines clear periods. “M” indicates trajectories of marine aerosols. light levels encourage phytoplankton blooms; however, at certain Arctic locales, long-range transport of North Atlantic biogenic aerosol has also been documented (Xie et al., 1999). The biogenic aerosol present in largest concentration is dimethylsulfide (DMS), which is oxidized into a number of sulfur-based compounds that serve as cloud condensation nuclei. Other organic by-products, including NH3 and amino acids (L-methionine), and marine detritus,


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bacteria, diatom frustules (Leck et al., unpublished), make effective ice formation nuclei and are injected into the atmosphere through bubble bursting at the ocean surface and frost flower formation on frazil ice. These physical processes also introduce significant amounts of sea salt into the boundary layer especially over the freezing polynya surface. Through convective and updraft mechanisms, salt is lifted to height and may undergo deliquescence in clouds to form numerous ionic species. Suspended sea salt has been detected in remote sensing imagery to be a large component of aerosols in the so-called “roaring forties” latitudinal band of the Southern Ocean. Nearer Antarctica, however, katabatic winds and steeply sloping topography prevent much of this wind-generated sea salt aerosol from penetrating further southward over the continent. In fact, the aerosol concentrations over Antarctica are an order of magnitude less than those measured over the Arctic, even during the productive summer months (Bason, 2000). So, while sea salt concentrations may be used in the Arctic as a proxy for percent open water when studying ice cores, it is not an accurate representation in the Antarctic (Wagenbach et al., 1998). Those aerosols which do settle out of the atmosphere over Antarctica, usually descend from stratospheric levels to become part of the firn and may include black carbon particulates transported over long distances from tropical biomass burning areas (Wolff and Cachier, 1998) and nitrate compounds precipitated from polar stratospheric clouds (Kottmeier and Fay, 1998). In addition to these nucleation and accumulation mode particles are naturally-occurring atmospheric ice features which perturb the radiative profile. The formation of diamond dust and ice fog is contingent upon a number of factors, and is most likely to occur under mixed phase conditions, when large ice particles and abundant water droplets coexist. These prerequisites are often met in areas downwind of polynyas and leads (Rogers et al., 2001), where water vapor and biogenic ice nuclei from the open ocean surface are advected over the relatively cold, supersaturated sea ice periphery (Girard and Blanchet, 2001). In diamond dust events, the ice crystals that form are few but relatively large (30–60 μm; Witte, 1968; Hobbs and Rangno, 1998), may remain suspended for minutes to hours, have low emissivities (Shupe and Intrieri, 2004), and tend towards radiative cooling of the surrounding environment. Ice fog, on the other hand, is composed of numerous spherical ice crystals of 10–30 μm diameter, which do not aggregate efficiently and sediment slowly, allowing the fog to linger for hours to days and enhance surface warming through longwave emission (Girard and Blanchet, 2001). Ice fog is often found under surface inversions near highly sloping topography, accounting for high percentages of surface observations in the North Water Polynya (15%, September; Key et al., 2004), Northeast Water Polynya (40%, August; Minnett, 1995), and flaw leads near Alert (40%, Spring; Girard and Blanchet, 2001). While both diamond dust and ice fog occur year-round, their radiative impact is of greater importance in the winter, when longwave effects dominate; however, it has been suggested that radiative effects ascribed to wintertime diamond dust actually have their provenance in thin liquid layers (Shupe and Intrieri, 2004). The low effective emissivity and sparse nature of diamond dust, especially in times of low illumination, make it a difficult target for remote sensing or in situ observation, possibly leading to an underestimation of its occurrence and importance to radiative forcing. 2.7

Radiation

The electromagnetic radiation that propagates through the atmosphere is generally divided into two components—shortwave (SW) with wavelengths of about 0.35 to 3 μm, and longwave (LW) covering 5 to 50 μm. This distinction reflects not only their origins (SW from the

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sun; LW from the atmospheric constituents, including clouds, and the surface, as well as the sun), but also their interactions with the atmosphere and the surface, and the technologies used to measure their intensities. In propagating through the atmosphere, the SW radiation is primarily scattered whereas the LW is absorbed and re-emitted. While wavelengths shorter than 0.35 μm (ultraviolet) are strongly absorbed by ozone in the upper atmosphere, and some SW wavelengths are absorbed by water vapor, the clear-sky atmospheric transmissivity (measured incident SW, both direct and diffuse, divided by the insolation at the top of the atmosphere, and normalized by the secant of the solar zenith angle) shows very little variation (Minnett, 1999; Hanafin and Minnett, 2001). Scattering occurs across the whole spectrum, but has spectral dependences determined by the ratio of the wavelength of the light and the size of the scatterers. On the other hand, gaseous absorption and re-emission are very strongly wavelength dependent with distinct spectral lines. In some parts of the spectrum molecular absorption is sufficiently strong that the atmosphere is essentially opaque over spectral intervals several micrometers wide (e.g. absorption and emission by CO2 molecules around 15 μm wavelength). In spectral intervals where the atmospheric gases exhibit relatively few absorption lines, the propagation of the LW radiation can be quite efficient and heat can escape from the surface to space. These spectral intervals are referred to as “atmospheric windows” and their clear transmissivity is a strong function of the atmospheric water vapor distribution. Since the water vapor content of polar atmospheres is generally much less than at lower latitudes, these windows are particularly clear. Insertion of clouds and aerosols which absorb and re-emit in a spectral continuum given by Planck’s Function scaled by the spectral emissivity, has a much more pronounced effect on the radiative coupling between the surface and the atmosphere than at lower latitudes. Hence, cloud radiative forcing is an especially important characteristic of the polar regions.

3 NWP Models Most meteorological and radiative processes occurring within polynyas are of too fine a scale to be adequately represented in numerical weather prediction (NWP) models, which range in resolution between 12 km and 275 km and are output every 6 to 24 hours. Surface measurements are also too few and too unevenly distributed to provide a comparable validation dataset at model resolution. The compromise, which advances both the model-derived and in situ measurements, is an assessment of model physics using independent in situ observations (Curry et al., 2002) and a move towards regional or nested grids targeted towards certain areas or atmospheric features. Those surface and upper air observations that are available in polar regions are generally assimilated into large-scale analyses operated by NCEP (National Centers for Environmental Predictions) and ECMWF (European Centre for Medium-range Weather Forecasts), and later refined into re-analysis products—NCEP-R and ERA-40, respectively. While other models, such as the Polar MM5, ETA, NGM, and GEM also produce gridded meteorological features at various levels throughout the atmosphere at equal or higher spatial resolution, NCEP and ECMWF output is global, long-term, and widely used in process studies, validation exercises, and as initialization for climate models. Polynya-specific analyses of NCEP and ECMWF fields have identified regional and climatological characteristics of the open water region, while also pinpointing dynamical and scale deficiencies in the models. ECMWF and ERA-40, which have relatively high spatial resolutions, assimilate varied satellite products, and better represent ice feedbacks (Bromwich et al., 2001), are generally regarded as more


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accurate than NCEP and NCEP-R; although, the ECMWF topography appears to be insufficiently precise (Cullather et al., 1998) and may account for the offset of atmospheric features near Antarctica and the Cosmonaut Sea Polynya (Arbetter et al., 2004). Basic meteorological fields from models, such as surface air temperature and wind, which are used to estimate the air–sea fluxes and ice production within the polynyas, have provided insight in some cases as to the interaction between the polynya and the surrounding environment. Over the Weddell Sea, Moore et al. (2002) identified an extreme year in the surface meteorology of a local polynya, which NCEP-R portrayed as 20 K warmer and 50% cloudier than regional climatological values. Air–sea fluxes from NCEP-R in the Arctic suggested significant dense water production due to enhanced heat loss over the Northwestern Polynya in the Sea of Okhotsk (Martin et al., 1998). However, both NCEP and ECMWF display a cold bias in surface air temperature (Bromwich et al., 2001; Armstrong et al., 2003), which may have underestimated the climatological extreme in the Weddell Sea and overestimated the negative buoyancy flux in the Northwestern Polynya. Winds, too, are biased in the ECMWF and NCEP-R products. Overestimation of wind speeds, possibly through a northward displacement, intensification, or zonal distortion of the jet stream in the Arctic leads to exaggerated westerly and northerly components (Francis, 2002). In areas of katabatic influence, important to polynya formation and maintenance, NCEP is too coarse to resolve either the drainage current or the interaction of these winds with the synoptic flow (Samelson unpublished), while ECMWF surface winds resolve the katabatic flow but shifted from its true location (Arbetter et al., 2004). From concerns regarding the resolution of basic fields, new regional and nested models have been developed. The Polar MM5, which is utilized by the Antarctic Mesoscale Prediction System (AMPS), includes a 10-km domain in the western Ross Sea, where a recurring polynya forms under persistent southerly flow. While synoptic and boundary layer features are generally well-resolved by this model, increased cloudiness associated with the open water areas is underestimated (Monaghan et al., 2005). The ETA model, too, under-estimates cloud cover below 5 km height, due to its imposition of a strong inversion layer, which suppresses turbulent exchange to the upper boundary layer (Greenberg et al., unpublished). And while the NGM (Nested Grid Model), operated by the National Meteorological Center, captures the east–west gradient in mean surface air temperature over the North Water Polynya, for example, smoothing associated with its 190 km grid spacing likely dampens the magnitude of temperature difference between Ellesmere Island and Greenland (Barber et al., 2001).

4 Surface Interactions Interactions between the surface and overlying atmosphere are commonly divided into two groups—radiative and turbulent. Radiative fluxes are considered as having two components, one that originates at the sun (SW) and the other in the thermal infrared (LW), which comprises the emission from the surface and constituents of the atmosphere. The SW and LW interactions with the surface are markedly different. Whereas the absorptivity and emissivity in the LW is relatively consistent for snow, ice and open water, the surface reflectivity in the SW is highly responsive to surface composition. Dry snow on ice reflects more than 80% of the incident insolation in the visible (Hanesiak et al., 2001), whereas open water reflects less than 10% (Payne, 1972). This reflectivity, or albedo, effect is also sensitive to the solar zenith and azimuth angles. Taken together, the albedo and sun angle effects account for the pronounced

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difference in upwelling shortwave radiation over fresh snow and open water surfaces. Due to the rapidly varying albedo within polynyas during the daylit season, surface albedo effects significantly influence the local radiative profile and, thus, net cloud radiative forcing. Turbulent exchanges are so-called because of the efficiency with which properties can be transported through the atmospheric boundary layer by turbulent eddies. Heat, gas (including water vapor) and momentum exchange across the air–sea boundary is moderated by the stability of the lower boundary layer of the atmosphere, which in turn is influenced by the air– surface temperature difference. Atmospheric temperature inversions limit the efficiency of vertical transport, while unstable boundary layers associated with large air–sea temperature differences, such as those found in the vicinity of polynyas, promote turbulent transfer. The resulting influx of heat and moisture into the atmosphere above polynyas most visibly affects cloud and plume formation both over and downwind of the open water area. 4.1

Radiative Fluxes

Cloud radiative forcing (CRF) was defined in Ramanathan et al. (1989) as the sum of the short-wave (CSW ) and the long-wave (CLW ) contributions: CRF = CLW + CSW ,

(1)

where CLW = LW(c) − LW(c=0) ,

(2)

CSW = SW(c) − SW(c=0) .

(3)

LW and SW are, respectively, the longwave and shortwave fluxes incident at the surface in all (c) and clear sky (c = 0) conditions. When only the differences in downwelling, short- and longwave radiation incident at the surface under clear and all-sky conditions are considered, the measure is one of surface CRF—i.e., the influence of cloud cover on the underlying surface. However, in many analyses, the response of the surface to changing insolation is included in the forcing calculation, comprising a net cloud radiative forcing. This latter calculation can be performed at the surface or the top of the atmosphere, using in situ and remotely-sensed data, respectively. The sign and magnitude of cloud radiative forcing indicates whether a cloud preferentially scatters insolation (negative), slowing ice melt or promoting ice re-freeze, or absorbs and re-emits radiation in the longwave (positive), furthering ice melt or hindering re-freeze. During Arctic daylight conditions, in situ and satellite-based studies (Schweiger and Key, 1994) demonstrate a negative incident (Tsay et al., 1989) and net cloud radiative forcing (Walsh and Chapman, 1998; Intrieri et al., 2002) over the Arctic in general and over polynyas in particular (Minnett, 1999; Hanafin and Minnett, 2001). The main factors influencing the incident SW at the surface are the transmittance of the cloud-free atmosphere, the amount and types of clouds present (Figures 10 and 11), and whether the sun is obscured by clouds (Minnett, 1999; Hanafin and Minnett, 2001). The broad-band clear sky atmospheric transmittance has been found to be quite invariant in several data sets taken in different polynyas, having a value of 0.89 ± 0.02 (Minnett, 1999; Hanafin and Minnett, 2001; Key, 2004) whereas the cloud effects have been found to be more variable, reflecting the different local conditions that influence the types and properties of lower clouds in particular (Minnett, 1999; Hanafin and Minnett, 2001; Key, 2004; Key et al., 2004). The effects of the surface conditions have been found also to influence the incident SW CRF, in that contemporaneous measurements


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Figure 10: Time series from the Northeast Water Polynya in the summer of 1993 of measured surface insolation, SW (top), and observed cloud amount and type (center). The smooth line in the bottom panel represents the calculated top-of-atmosphere insolation. The vertical colored lines in the bottom panel indicate the energy removed from the insolation by the clouds and clear-sky effects—that is, the lower edges of the colors are the measured SW (as in the top panel). Zero values indicate missing data. After Minnett (1999). taken at a coastal station and from a research icebreaker in the nearby North Water Polynya show significant differences in clouds and CRF, that can be attributed to radiative interactions between the surface albedo and the clouds themselves (Key, 2004). Incorporating the surface albedo into the calculation to derive net SW CRF leads to a strong dependency on the surface conditions, in particular whether there is snow on the ice, whether the snow is dry or moist, whether the surface includes melt ponds, or whether the surface is ice-free water (Figure 12). The range of surface albedo for these conditions is very large, especially during the melt season going from greater than 80% to less than 10% (e.g. Hanesiak et al., 2001; Perovich et al., 2002a, 2002b; Eicken et al., 2004), with a consequent effect on the net CRF (Walsh and Chapman, 1998). Uncertainties in the correct specification of the surface albedo can potentially lead to a change in sign during many of the sunlit months (Intrieri et al., 2002). Additional radiative contributions from cloud–cloud interactions, both within layers of the same cloud and between proximate clouds, are unknown over polynyas and beyond the scope of most box or superparameterization models available for radiative calculation. Complex ray paths of insolation filtering through mixed phase cloud and laminar stratus decks may enhance or diminish forward scattering, affecting the amount of radiation measured at the surface, and thus, the surface cloud radiative forcing over a given area (Rouse, 1987; Jin and Barber, 2005). Arctic aerosols, especially those locally generated, such as sea salt and biogenic sulfates (DMS; Leck et al., 2004) may also influence the surface radiative forcing. Generally, the effects are more pronounced during the sunlit part of the year as the aerosols scatter more of the incident insolation back to space, leading to a negative radiative forcing. However,

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Figure 11: Incident cloud forcing plotted against cosine solar zenith angle (θ) for clouds of type: (a) stratiform, (b) cumuliform, (c) cirriform and (d) multi-level. Colors represent cloud fraction in octals as shown in the central panel. The black lines represent the average value of net cloud forcing as a function of cosine solar zenith angle calculated in bins of 0.05. From Hanafin and Minnett (2001).

because the polar atmospheres have low integrated water vapor amounts, the atmospheric infrared spectral windows are very clear, and so the incident LW is susceptible to enhancement by the presence of aerosols. Such effects may be even greater in the Arctic than at lower latitudes, where aerosol LW forcing can reach approximately 10 Wm−2 (Vogelmann et al., 2003). 4.2

Turbulent Fluxes

While radiative fluxes dominate the surface heat budget in polar regions, turbulent exchange of heat between the ocean and the atmosphere can be significant within polynyas. High wind speeds, large areas of open-water, and unstable lower atmospheric boundary layers all contribute to enhanced turbulent flux over polynyas, though the measurement of these fluxes and controlling parameters are difficult and infrequent.


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Figure 12: Time series of daily averaged net surface cloud radiative forcing for a range of surface albedos, given by the colors. The incident radiative forcing is derived from measurements taken at the Cape Hershel Ice Camp on the northern edge of the North Water Polynya during 1998. Air–sea temperature difference is an important parameter in determining the lower boundary layer stability which in turn has a large influence on the ocean-atmosphere fluxes. An unstable, or neutrally stable, boundary layer allows for efficient transport of heat, gases and moisture vertically through turbulent diffusion, whereas a stable boundary layer decouples the surface from the troposphere, inhibiting vertical transport. Over much of the world’s oceans the air–sea temperature difference is small, and negative (cool air over warmer water) leading to a neutrally stable or unstable boundary layer, but over polynyas and leads, in situations of off-ice air flow, very large temperature differences can occur (Walter, 1989; Massom et al., 1998), leading to a very unstable boundary layer and large sensible (400 Wm−2 ) and latent heat (130 Wm−2 ) fluxes (Andreas et al., 1979). More recently, Pinto et al. (2003) measured sensible and latent heat values of 100 and 30 Wm−2 from a lead near SHEBA in the Beaufort Sea. Similar values characterize air–sea fluxes over Antarctic polynyas (Massom et al., 1998; Roberts et al., 2001) where observations indicate that polynya sensible and latent heat transfer is two orders of magnitude greater than that over sea ice (Worby and Allison, 1991). Summing over the contribution of all polynyas and leads to air–sea heat exchange underscores the dominance of these open water areas to heat flux in polar regions despite the relatively small areal fraction they occupy (Maykut, 1978). The extreme conditions prevalent downwind of the ice edge and over open water render questionable the use of standard exchange coefficients in bulk aerodynamic formulae. As the internal atmospheric boundary develops over the first few hundred meters of open water, convection is both forced and free leading to a value of the sensible heat transfer coefficient of approximately 1.8×10−3 compared to approximately 1.0 ×10−3 determined in mid-latitude conditions that are not fetch-limited (Andreas and Cash, 1999). The situation becomes more complex within polynyas and leads during conditions when frazil ice is forming and being advected downstream, such that the heat fluxes develop a strong dependence on fetch (Alam and Curry, 1998). For most polynyas, however, the downwind dimension is greater than a few hundred meters, and so the open ocean coefficients can be used; however, it is noteworthy that the largest ocean to atmosphere fluxes occur close to the upwind ice edge. The injection of moisture into the atmospheric boundary layer leads to enhanced plume and cloud formation (Dethleff, 1994; Mailhot et al., 2002; Zulauf and Krueger, 2003; Arbetter et al., 2004), which is apparent in satellite imagery (Fett et al., 1997; Figure 7), and which

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can significantly modify the surface radiation budget, with increases in the LW component of ∼70 Wm−2 modeled downwind (Pinto and Curry, 1995). This increase both in cloudiness and longwave emission to the surface constitutes a positive radiative feedback towards enhanced ice melt in the vicinity of leads and polynyas.

5 Outlook for the Future The International Polar Year (2007–2009) refocuses international attention on changes occurring in the high latitudes. General data deficits and lack of long-term monitoring stations, especially within polynyas and the maritime Arctic, are expected to be addressed by multidisciplinary and multi-national research cruises, stations, buoys, and modeling efforts. New technology that can withstand the rigors of polar deployment have been developed and are being tested in projects leading up to the IPY. The autonomous profiling CTD, moored in locations along the Alaskan Chukchi shelf during the Shelf-Basin Interaction study (Grebmeier et al., 2005), has already demonstrated a unique sampling strategy which increased hydrographic resolution in this area over 1000-fold in a matter of months. The atmospheric analog of this instrument, the long-used radiosonde, has undergone a transformation in recent years to include GPS-derived wind profiles and better-calibrated relative humidity measurements. To extend these atmospheric measurements both in horizontal and vertical space over the Arctic ice pack, instrumentation has been integrated into a small, unmanned light craft—e.g. the aerosonde (Inoue and Curry, 2004)—which has a 500 mile and 24-hour duration. Typical flight patterns otherwise carried out by piloted aircraft can be programmed into the aerosonde flight plan and measurements can be transmitted back to a shore-based receiving station in real time. Since the payload on these aircraft is only in the range of 2.5 kg, sensors must be miniaturized for deployment. Similar AUV’s have been developed for oceanographic surveys (Erikson and Rhines, unpublished), though, as of writing, they are restricted to open water areas. Combined measurements from the aerosonde and AUV for integrated air–sea sampling over polynyas has not yet been attempted but represents one future of polynya science. Shipboard measurement of polynyas will continue, utilizing science-directed research icebreakers, e.g. the USCGC Healy and CCGS Amundsen. Support from coastal stations will be increased as new stations are founded in undersampled areas, such as the Eastern Siberian Seas and the Canadian Archipelago. Measurements at these sites will add to the scant microphysical literature, though they remain characterizations of isolated, land-based locations and generally are unrepresentative of polynya conditions. Merging these varied data types with recently-launched, high resolution remote sensing retrievals, such as those from space-borne lidar (e.g., CALIPSO, PARASOL, ICESat) and cloud radar (CloudSat) will better identify the effects of polynyas on surface meteorology, boundary layer dynamics and downwind cloud optical and radiative properties.

6 Summary The complex interplay between polynyas and the overlying polar atmosphere tests the boundaries of measurement and modeling efforts alike. Intense, localized meteorological forcing may melt or fracture and advect ice downwind, thereby allowing unhindered turbulent and radiative heat exchange across the air–sea boundary. Cloud and plume formation over and in the vicinity of the polynya is fostered by the exchange, which further alters the radiative


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profile and possibly nearby ice and snow cover. The ranges of temporal and spatial scales on which these feedbacks and interactions take place are regulated by atmospheric boundary layer dynamics, which are poorly sampled over polynyas. Proximate coastal measurements of meteorological and radiative fields, which are assimilated by models and gridded analyses, often represent a land-based microclimate influenced by topography, surface cover, and office flow. Remotely-sensed retrievals within these regions provide an alternative data source, though coarse spectral and spatial resolution of many spaceborne sensors limits their applicability to polynyas. Future satellite missions targeted towards active quantification of clouds and aerosols with lidar and radar will forward understanding of cloud formation processes in dry polar atmospheres while adding much-needed data to the polynya archive. Continued in situ sampling from research icebreakers, aircraft, ice camps, and novel autonomous technology will provide useful time series, process and validation data for improving models and satellite retrievals. These combined approaches to studying polynya meteorology will enhance our understanding of the role of polynyas in physical systems and influence on global climate.

Appendix A: Acronyms AERI-ER AMAP AMPS AMSR-E AMSU-B ARM BASE CALIPSO CASES CASPR CCN CloudSat CRF ECMWF ERA-40 ETA FIRE-ACE

Atmospheric Emitted Radiance Interferometer-Extended Range, http:// www.arm.gov/science/research/R00029.stm Arctic Monitoring and Assessment Programme, http://www.amap.no/ Antarctic Mesoscale Prediction System, http://www.mmm.ucar.edu/rt/ mm5/amps/ Advanced Microwave Scanning Radiometer—EOS, http://www.ghcc. msfc.nasa.gov/AMSR/ Advanced Microwave Sounding Unit-B, http://www2.ncdc.noaa.gov/docs/ klm/html/c3/sec3-4.htm Atmospheric Radiation Measurement Program, http://www.arm.gov/ Beaufort and Arctic Storms Experiment, http://gcss-dime.giss.nasa.gov/ base/base.html Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, http: //www-calipso.larc.nasa.gov/ Canadian Arctic Shelf Exchange Study, http://www.cases.quebec-ocean. ulaval.ca/ Cloud And Surface Parameter Retrieval system, http://stratus.ssec.wisc. edu/caspr/caspr.html Cloud Condensation Nuclei Cloud Satellite, http://cloudsat.atmos.colostate.edu/ Cloud Radiative Forcing European Centre for Medium-range Weather Forecasts, http://www. ecmwf.int/ ECMWF 40-year Re-Analysis, http://www.ecmwf.int/products/data/ archive/descriptions/e4/ ETA (η) coordinate system model, http://meted.ucar.edu/nwp/pcu2/etintro. htm First ISCCP Regional Experiment—Arctic Cloud Experiment, http:// eosweb.larc.nasa.gov/ACEDOCS/

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P.J. Minnett and E.L. Key GEM IAPP ICESat IPY ISCCP LW MM5 NCAR NCEP NCEP-R NEW NGM NMC NSA-AAO NWP PARASOL ROPEX SBI SHEBA SSM/I SSM/T2 SW TIROS TOVS

Global Environmental Multiscale model, http://weatheroffice.ec.gc.ca/ model_forecast/index_e.html International Arctic Polynya Programme, http://www.aosb.org/IAPP.html Ice, Cloud, and land Elevation Satellite, http://icesat.gsfc.nasa.gov/ International Polar Year, http://www.ipy.org/ International Satellite Cloud Climatology Project, http://isccp.giss.nasa. gov/ Longwave component of the electromagnetic spectrum (λ = ∼5 to ∼50 μm) PSU (Penn Sate University)/ NCAR mesoscale model, http://www.mmm. ucar.edu/mm5/ National Center for Atmospheric Research, http://www.ncar.ucar.edu/ National Centers for Environmental Predictions, http://www.ncep.noaa. gov/ NCEP Reanalysis, http://dss.ucar.edu/pub/reanalysis/ Northeast Water Polynya, http://www.emi.dtu.dk/research/DCRS/seaice/ new.html Nested Grid Model National Meteorological Center North Slope of Alaska—Adjacent Arctic Ocean, http://www.arm.gov/sites/ nsa.stm Numerical Weather Prediction Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar, http://smsc.cnes.fr/PARASOL/ Ronne Polynya Experiment, http://www.esr.org/ropex/ronice.html Shelf-Basin Interaction study, http://sbi.utk.edu/ Surface Heat Budget of the Arctic Ocean, http://sheba.apl.washington.edu/ Special Sensor Microwave Imager, http://dmsp.ngdc.noaa.gov/html/ sensors/doc_ssmi.html Special Sensor Microwave Water Vapor Profiler, http://www2.ncdc.noaa. gov/docs/klm/html/c3/sec3-4.htm Shortwave component of the electromagnetic spectrum (λ = ∼0.35 to ∼3 μm) Television Infrared Observation Satellite, http://www.earth.nasa.gov/ history/tiros/tiros.html TIROS Operational Vertical Sounder, http://www2.ncdc.noaa.gov/docs/ podug/html/c4/sec4-0.htm

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Chapter 5

Gas Fluxes and Dynamics in Polynyas L.A. Miller1 and G.R. DiTullio2 1 Institute of Ocean Sciences, Fisheries and Oceans Canada, 9860 West Saanich Road, Sidney, BC V8L 4B2,

Canada; e-mail: [email protected] 2 Department of Biology, College of Charleston, 205 Fort Johnson, Charleston, SC 29412, USA

Abstract High biological productivity and active ice formation make polynyas prime air–sea exchange sites for a number of radiatively- and biologically-active gases, such as carbon dioxide and dimethylsulfide (DMS). Limited observations, to date, have shown that polynyas are generally sources to the atmosphere for biogenic gases (e.g., DMS, oxygen, methylhalides) but sinks of CO2 , which is drawn down both by primary production coupled with organic carbon export and by high solubility in the cold, high-salinity waters typical of polynyas. However, this simple summary belies large data gaps, and our conceptual models of gas dynamics in polynyas are riddled with untested assumptions. The most important needs for additional research and information are in wintertime and transition-period processes, ice biogeochemistry and permeability, and climate change feedback processes.

1 Introduction Polynyas are clearly critical to ice formation and heat exchange in polar oceans (Barber and Massom, 2007; Minnett and Key, 2007; Williams et al., 2007), and a number of recent studies have indicated that polynyas are probably also important sites for air–sea gas transfer. However, most of these studies (Table 1) have been short, seasonal expeditions, and as a result, our ideas about gas dynamics in polynya environments are largely extrapolations and conjecture based on what is known of open water or fully ice-covered seas. Whether a polynya is a source or a sink of a gas depends not only on the biogeochemistry of the gas and the season (as in temperate waters) but also on ice cover timing and polynya formation dynamics. In turn, polynya dynamics are controlled by local climatological forcing and likely by global scale atmosphere-ocean coupling (e.g., El Niño-Southern Oscillation) via long-range teleconnections (Savage et al., 1988; Gloerson and Mernicky, 1998; Arrigo and van Dijken, 2004a). Because polynyas are often biologically rich areas (Tremblay and Smith, 2007), they are potentially effective sources of biogenic gases, such as dimethylsulfide (DMS; Section 4 below) and alkylhalides (e.g., methylbromide; Section 5). Conversely, highly productive polynyas may be net sinks of CO2 , depending on the balance of Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74005-3

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Table 1: Gas studies in polynyas Polynya

Gases

Years

Season

References

Weddell Ross Sea

CO2 CFCsa , O2 , CO2

1981 1984

Spring Summer

Cape Bathurst Ross Sea

CFCs, CO2 DMS

1989–1995 1992

Summer Summer

Northeast Water

CO2 , O2 , CFCs

1992

Summer

North Water

CO2 , DMS

1993–1994

Spring–fall

Ross Sea

CO2 , DMS

1994–1996

Summer

Ross Sea

CO2 , DMS

1996–1997

Spring–fall

Mertz Mertz

O2 CO2 , CFCs, CH3 Xb

1999 2000–2001

Winter Summer

Chen and Poisson (1984) Takahashi et al. (1985) Trumbore et al. (1991) McLaughlin et al. (2002) DiTullio et al. (2003) DiTullio and Smith (1995) Yager et al. (1995) Wallace et al. (1995) Top et al. (1997) Miller et al. (2002) Bouillon et al. (2002) Bates et al. (1998) DiTullio et al. (2003) Arrigo et al. (1999) Sweeney et al. (2000b) DiTullio et al. (2003) Williams and Bindoff (2003) Sutherland et al. (2002a, 2002b) Yvon-Lewis et al. (2004)

Cape Bathurst St. Lawrence Is.

CO2 , O2 , CH4 , CO DMS

2002–2004 2001

All year Winter

a Chlorofluorocarbons. b Methylhalides: CH Br and CH Cl. 3 3

primary production, remineralization, and vertical organic matter export in relation to the timing of ice cover and wind events (Section 3). Sustained ice formation also encourages formation and export of cold, dense shelf waters (Williams et al., 2007) in which gases readily dissolve (Section 2), and polynyas where these processes occur could provide another significant sink for atmospheric greenhouse gases, such as carbon dioxide and chlorofluorocarbons (see also Hoppema and Anderson, 2007). Finally, as extremely variable phenomena, polynyas are likely sensitive to climate variations, with potential for both positive and negative feedbacks. While our understanding of gas dynamics in polynyas may be limited, research in this field has become more intensive over recent years (Table 1), and ideas about the role polynyas play in air–sea gas exchange are changing rapidly. Therefore, we attempt here to review the data currently available, while also emphasizing that those data are still open to interpretation and highlighting what we think are some particularly promising directions for future research.

2 Gases in Cold, Salty Water Gas partitioning (KH in atm M−1 ) between water and air is defined by Henry’s Law KH =

PA , [A(aq) ]

(1)

Gas Fluxes and Dynamics in Polynyas

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Figure 1: Equilibrium oxygen (O2 ) concentration in seawater as a function of temperature and salinity, µmol kg−1 . Darkened region is below the freezing point. Table 2: Examples of equilibrium gas concentrations (mol kg−1 ) in fresh and seawater at 0 and 25◦ C under typical atmospheric partial pressures, PA Gas

O2 a N2 a Ara CO2 b CFC-11d DMSe

Fresh water (S = 0)

Seawater (S = 35)

0◦ C

25◦ C

0◦ C

25◦ C

0.46 × 10−3 0.82 × 10−3 22 × 10−6 1.9 × 10−3 0.10 × 10−6 3.2 × 10−12

0.26 × 10−3 0.49 × 10−3 13 × 10−6 1.9 × 10−3 0.026 × 10−6 1.1 × 10−12

0.35 × 10−3 0.62 × 10−3 17 × 10−6 2.2 × 10−3 0.068 × 10−6 2.8 × 10−12

0.21 × 10−3 0.38 × 10−3 10 × 10−6 2.0 × 10−3 0.018 × 10−6 0.95 × 10−12

PA (atm)

0.21 0.78 0.0093 0.38 × 10−3 c 0.26 × 10−9 c 2 × 10−12 c,f

a Weiss (1970). b Total inorganic carbon-carrying capacity, assuming that alkalinity is 1900 µeq kg−1 in freshwater and 2300 in seawater; Lewis and Wallace (1998). c Atmospheric concentrations of these gases are variable; the tabulated values are typical values used in calculating the equilibrium aqueous concentrations. d Trichlorofluoromethane, CCl F; Warner and Weiss (1985). 3 e Dacey et al. (1984). f Barnard et al. (1982).

which describes the equilibrium relationship between the concentration of a gas in solution ([A(aq) ] in mol l−1 ) and its partial pressure in the overlying atmosphere (PA in atm). The Henry’s Law constant, KH , is a function of temperature as well as salinity, and for most gases, solubility in seawater increases with both decreasing temperature and salinity (Figure 1, Table 2). The salinity dependence is due to the “salting out” effect, whereby the total load of solutes in the solution inhibits further dissolution. When describing the potential for air–sea exchange, the partial pressure of the gas in the water (pA ; defined as the partial pressure in a small volume of gas overlying and in equilibrium with the solution) is used more commonly than KH . By definition, if pA is the same as PA , there will be no net gas exchange, whereas if pA is less than PA , there will be a net flux into the water, and vice versa.

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The equilibrium state described by thermodynamics (Eq. (1)) is not the only factor affecting how much gas moves across the air–sea interface. The flux, F , of a gas into or out of the ocean is directly related to both the gradient in partial pressures across the air–sea interface (p, the thermodynamic control) and to the gas transfer velocity, k (the kinetic control), F = kp.

(2)

The gas transfer velocity is usually expressed as a function of the gas solubility and wind speed (e.g., Liss and Merlivat, 1986; Wanninkhof and McGillis, 1999) but is also dependent on other factors which are much more difficult to parameterize (Liss et al., 1997). Nonetheless, field studies most commonly estimate gas fluxes by determining p analytically and calculating k from either observed or modelled winds. This approach has an uncertainty of up to 100%, mainly due to poor parameterizations of k, but also because short time-scale variations in both winds and p (which are very difficult to identify with the snap-shot, station-based sampling typical of oceanographic field work) heavily influence the total flux (Boutin and Etcheto, 1991; Bates and Merlivat, 2001; Fransson et al., 2004). For some gases, air–sea fluxes can be measured directly using a number of eddy techniques, and the methods for shipboard application are now reaching maturity (McGillis et al., 2001; Huebert et al., 2004), showing great promise for reducing the uncertainties in air–sea gas exchange rates. In Table 2 we have given the carbon dioxide solubility as the carbon-carrying capacity or the total dissolved inorganic carbon (DIC) concentration in equilibrium with the atmosphere. When dissolved in water, CO2 hydrolyzes, forming carbonic acid (H2 CO3 ), bicarbonate 2− (HCO− 3 ), and carbonate (CO3 ), 2− + CO2 + H2 O H2 CO3 H+ + HCO− 3 2H + CO3

(3)

generating a large, rapidly-exchanging pool of dissolved inorganic carbon. Analogous reactions occur in aqueous solutions of SO2 and NO2 , which also have quite high solubilities. Although the equilibrium concentration of the dissolved CO2 gas molecule in seawater decreases with increasing salinity (Figure 2a) like other gases, the carbon-carrying capacity of seawater increases with salinity (Figure 2b). The very high alkalinity of seawater (an increase in salinity is almost always accompanied by an increase in alkalinity) is mainly set by the carbonate system and is closely related to the buffering capacity of seawater, i.e., the amount of acid (such as additional CO2 ) that can be added before the pH starts to drop, inhibiting further CO2 absorption. Thus, as salinity increases, Eq. (3) shifts further toward the right, increasing dissociation of the carbonic acid, dramatically increasing the carbon-carrying capacity, decreasing pCO2 , and thereby encouraging CO2 dissolution. A comprehensive discussion of the seawater carbonate system is presented in Zeebe and Wolf-Gladrow (2001).

3 Carbon The primary motivation for studying air–sea carbon dioxide exchange is to understand the role the ocean plays in the global carbon cycle and controlling atmospheric CO2 levels, which in turn impact global climate through the greenhouse effect (Arrhenius, 1896). These studies have particular urgency at this time, because anthropogenic CO2 emissions to the atmosphere appear to be significantly altering the natural global carbon cycle and warming the planet’s surface (Folland et al., 2001). Numerical models have indicated that this warming


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Figure 2: (a) Equilibrium concentration (µmol kg−1 ) of aqueous CO2 gas (H2 CO∗3 , the sum of CO2(aq) and H2 CO3 , which cannot be distinguished analytically) in seawater as a function of temperature and salinity. (b) Seawater carbon-carrying capacity (µmol kg−1 total DIC) as a function of temperature and salinity. For both figures pCO2 = 380 µatm, and alkalinity varies linearly from 1900–2300 eq kg−1 between salinities of 0 and 35 psu. Darkened regions are below the freezing point. Stippled area in (b) gives characteristics typically observed in polynya surface waters. is likely to appear soonest and most dramatically in the polar regions, particularly in the Arctic (Cubasch et al., 2001), and some data are consistent with that prediction, although regional trends, interannual and seasonal variability, and other factors complicate the analysis (Polyakov et al., 2002; Comiso et al., 2003; Curran et al., 2003). An accelerated rate of change in polar regions, coupled with the high variability in polynya opening, closing, and productivity, could mean that gas exchange in polynyas will produce disproportionately large climate change feedbacks, relative to the small total area covered by polynyas. Polynyas potentially contribute to global oceanic carbon export through both the solubility and biological pumps (e.g., Volk and Hoffert, 1985). The solubility pump generally refers to the process through which carbon is removed from surface waters with formation of deep and bottom waters by convection in the Greenland, Labrador, and Weddell Seas (Anderson et al., 2000; Tait et al., 2000; Hoppema, 2004). However, these are not the only places where cold, high-salinity waters with high carbon-carrying capacity (Figure 2b) sink away from the surface. The intermediate waters (often also called shelf waters) formed under coastal leads and polynyas, where ice is formed and removed by wind or currents (e.g., Foster, 1972; Cavalieri and Martin, 1985, 1994; Haarpaintner et al., 2001), may also draw down atmospheric CO2 (Figure 3). A recent study of the inorganic carbon system during sea-ice formation in the Storfjorden polynya (Svalbard, Figure 4; Anderson et al., 2004) saw high DIC concentrations below 50 metres (m) that were associated with brines, which presumably had been produced at the surface when sea ice was forming. However, intermediate water formation is difficult to study, and this phenomenon has not yet been directly observed in any other polynya. Polynya contributions to the biological pump, by which photosynthetic production and then vertical export of organic matter removes carbon from the surface into deep waters and sediments, are even less straight-forward. While polynyas often have very high rates of both primary and secondary biological production (Tremblay and Smith, 2007; Ducklow and

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Figure 3: CO2 drawdown in ice-manufacturing polynyas.

Figure 4: Locations of some Arctic polynyas discussed in the text.


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Figure 5: The seasonal rectification hypothesis. Modified from Yager et al. (1995). CT denotes total dissolved inorganic carbon (DIC), OC denotes total organic carbon. Yager, 2007; Deibel and Daly, 2007), a high photosynthetic rate does not necessarily imply net annual CO2 consumption, and it is not clear to what extent polynyas are net exporting versus recycling systems. Particularly in shallow coastal polynyas where surface mixing can extend to the bottom, even deposition to the sediments does not necessarily isolate carbon from the atmosphere for long time periods. The timing of polynya opening and closing, in relation to the seasonal primary production cycle, is probably critical to the net annual biogenic CO2 flux. This concept was first presented by Yager et al. (1995) as the seasonal rectification hypothesis (Figure 5), attempting to describe the annual CO2 cycle in a seasonal polynya that opens in early spring and closes again in late fall. In such a polynya (or in any seasonally ice-covered sea), respiratory carbon remineralization during winter would cause supersaturation in the surface waters under the ice. By the time the ice has melted sufficiently for patches of open water to appear in spring, ice algae would have already depleted surface DIC concentrations to undersaturated levels, and any air–sea CO2 exchange would be directed into the water. Throughout the open water season, primary production would keep the surface waters undersaturated, but low winds could limit atmospheric CO2 drawdown. Early autumn storms, however, could

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facilitate equilibration between the atmosphere and surface waters, but by the time the surface waters become supersaturated due to excess respiration over production, the surface would be covered with ice, again preventing outgassing. A cycle of this sort would make the polynya a significant net annual CO2 sink, but that sink would be dependent on the timing of polynya formation and closing. If the polynya opens during the winter, particularly during a storm, enough CO2 could outgas in a short period to completely counteract the summer and autumn drawdown. In addition, high winds during the open water season would not only encourage air–sea exchange, but would also mix carbon-rich subsurface waters into the surface, where they would inhibit atmospheric CO2 drawdown. The seasonal build-up and remineralization of labile dissolved organic carbon (DOC) will also contribute to the biogenic CO2 source/sink balance in polynyas. The DOC pool in seawater, including the refractory fraction, is also subject to photochemical oxidation, which can either degrade the organic matter (facilitating biological uptake or directly producing inorganic carbon) or polymerize it, creating larger, more refractory molecules (Carlson, 2002). In polar regions light in the surface waters is limited both seasonally and by ice cover. As a consequence, photochemical cycling of dissolved organic matter would be accelerated in polynyas, relative to the surrounding, ice-covered waters. Higher incidence of ultra-violet (UV) radiation due to stratospheric ozone depletion in polar regions could further promote photochemical DOC degradation in polynyas (e.g., Kieber and Mopper, 1994). Photochemical oxidation, as well as primary production, may also be significant within the thin ice that often covers polynyas (Belzile et al., 2000). A paucity of data limits our understanding of the role calcium carbonate plays in polynya CO2 cycling. Changes in alkalinity associated with CaCO3 precipitation and dissolution dramatically influence pCO2 through the bicarbonate buffering system: Ca2+ + 2HCO− 3 CaCO3 + CO2 + H2 O.

(4)

In the past calcite producing coccolithophorids (e.g., Emiliania huxleyi) were not considered to be ecologically or biogeochemically important in polynya dynamics, because of known temperature constraints on their growth rates. However, E. huxleyi blooms have been observed at high latitudes (albeit perhaps due to climate-induced sea surface warming; Sukhanova and Flint, 1998), and other organisms (e.g., pteropods) may also significantly influence alkalinity in polynyas. Rivers are important sources of alkalinity to the surface waters of the Arctic Ocean (Anderson et al., 1983), and therefore, alkalinity effects are probably particularly important for polynyas there. Methane (CH4 ) is a stronger greenhouse gas than CO2 and is generally thought to be released from sediments (Macdonald, 1976; Tilbrook and Karl, 1994) and may accumulate under ice in polar waters (Kvenvolden et al., 1993). Data on methane distributions or dynamics specifically in polynyas have not yet been reported, but the timing of ice cover in relation to rates of microbial methane cycling processes in polynyas could be relevant to the net oceanic source-versus-sink balance of atmospheric methane. 3.1

The Ross Sea

The Ross Sea, Antarctica, contains two important, recurring polynyas (Figure 6), one in Terra Nova Bay and another off the Ross Ice shelf (the Ross Sea polynya, the largest polynya in the Antarctic at 396,000 square kilometres (km2 )), which have been sites of numerous polynya studies, starting with Priestley’s descriptions of the Terra Nova Bay polynya during the winter of 1912 (Priestly, 1962; Bromwich and Kurtz, 1982). That polynya forms


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Figure 6: Ross Sea bathymetry. Dark grey lines are semi-permanent ice features (ice shelves and glacial tongues); light grey shaded regions give the approximate locations of the Terra Nova Bay polynya, to the west, and the larger Ross Sea polynya. Depth contours are every 200 m through 1000 m and every 1000 m thereafter. consistently during the austral winter, as katabatic winds descend off Victoria Land and push the ice offshore, while the Drygalski Ice Tongue prevents advection of more ice into the area (Bromwich and Kurtz, 1984; Van Woert, 1999). Katabatic winds off the Ross Ice Shelf also appear to play the major role in keeping the Ross Sea polynya open, although warm atmospheric temperatures, particularly in association with La Niña events in the Pacific (Bromwich et al., 1998; Van Woert et al., 2003) may contribute at times. The first surface pCO2 data from the Ross Sea were collected by Takahashi et al. (1985) for late January and early February of 1984. Although they did not report the ice conditions, Takahashi et al. found that pCO2 was much lower in areas where the two polynyas form than in the rest of the Ross Sea. These observations have been confirmed by later studies, including those extending into both the spring and fall (Figure 7; Bates et al., 1998; Sweeney et al., 2000a; see also the on-line surface water pCO2 data repository at the Lamont-Doherty Earth Observatory). The lowest surface pCO2 values observed in the Ross Sea to date have been about 130 µatm (Table 3). Bates et al. (1998) and Sweeney et al. (2000b) both calculated small (less than 10 mmol m−2 d−1 ) air-to-sea fluxes during spring and summer, but their use of long-term averaged wind speeds could have significantly underestimated the true fluxes, and Sweeney et al. saw evidence for larger fluxes (up to 70 mmol m−2 d−1 ) during an autumn cruise.

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Figure 7: Surface pCO2 observations in the Ross Sea. Grey scale ranges from 110 (light) to 370 (dark) µatm. Triangles: Takahashi et al. (1985), circles: Sweeney et al. (2000a). pCO2 was measured directly and corrected to in situ temperature. Table 3: Carbon dioxide partial pressures, pCO2 (µatm), in polynya surface waters Polynya

Minimuma

Month

Maximuma

Month

References

Ross Sea

130

January/ February

360

November

North Water Northeast Water

130 170

June July/August

500 280

April July/August

Sweeney (2003) Takahashi et al. (1985) Bates et al. (1998) Miller et al. (2002) Yager et al. (1995)

a Observed minima and maxima, not necessarily true annual extremes.

Since the mid-1990s, a number of interdisciplinary programs have targeted the carbon system of the Ross Sea, producing a substantial, albeit disjointed, data set. Although large seasonal biogenic carbon drawdowns are consistently observed in the Ross Sea (Bates et al., 1998; Arrigo et al., 2000; Gordon et al., 2000; Sweeney et al., 2000b), Carlson and Hansell (2003) reported that relatively small quantities of DOC are produced during the summer. What DOC does accumulate is labile on time scales of only two months (the role of photochemistry versus direct heterotrophy in that lability has not been investigated), suggesting


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that in the Ross Sea DOC export is relatively unimportant to carbon sequestration, although it may still be playing a role in the annual cycle of air–sea CO2 exchange. Similarly, Asper and Smith (2003) estimated that about 60% of the POC in the surface waters is respired directly back to CO2 during the summer, with only about 13% exported below 200 m. Sweeney (2003) synthesized data from a number of cruises to compile a model annual cycle which confirmed the hypothesis (Figure 5) that seasonal sea ice creates a net annual carbon dioxide sink by preventing winter outgassing. Arrigo et al. (2000) suggested that due to light effects, Phaeocystis antarctica usually dominated over diatoms (which were less effective at either consuming DIC or producing DOC) in the more deeply mixed Ross Sea polynya, while diatom blooms were relatively more important in the more stratified Terra Nova Bay polynya. Thus, a climatic warming that increases stratification could favor diatoms, thereby reducing carbon drawdown efficiency (Arrigo et al., 1999). In addition, large icebergs calving off ice sheets in Antarctica have recently disrupted circulation and increased ice cover sufficiently to reduce seasonal primary productivity by as much as 90% (Arrigo and van Dijken, 2003a). Finally, iron released by melting sea ice appears to contribute to the high spring productivity observed in the Ross Sea (Sedwick and DiTullio, 1997), and loss of the seasonal sea ice could lead to lower productivity rates sustained over longer periods, with accompanying implications for lower carbon export. Gordon et al. (2000) also showed that alkalinity does not vary much during the summer season, contributing evidence that calcifying organisms are unimportant in the Ross Sea carbon cycle under the current climate regime. Dense shelf water under the Ross Sea was first reported by Jacobs et al. (1970), although it is uncertain how much forms in the polynyas versus under the ice shelf (Williams et al., 2007). In 1984, Trumbore et al. (1991) saw elevated levels of CFCs at depth along the shelf slope, as well as in the High Salinity Shelf Water in the western region of the Ross Sea, and they suggested at least part of those high CFC concentrations must have entered the water through polynyas and leads during winter. Takahashi et al. (1985) report inorganic carbon (DIC and pCO2 ) and oxygen data from that same cruise, and the waters which were high in CFC-12 may also have been more saturated with CO2 when last at the surface (implying additional, wintertime equilibration before convection) than the low-CFC-12 waters. However, the difference was not significant, relative to the uncertainty in the ratio of dissolved oxygen consumption to organic carbon remineralization. Later, Sweeney (2003) confirmed that CO2 export with shelf water formation is probably insignificant, based on measured DIC concentrations in deep water masses and estimates of brine rejection in the Ross Sea. 3.2

The North Water

First described in 1616, the North Water, in northern Baffin Bay (Figure 8), is among the most extensively studied of all recurrent polynyas (Dunbar and Dunbar, 1972). The North Water is a coastal polynya with complex circulation (Melling et al., 2001) and forms when an ice bridge in the narrow straits to the north blocks ice flowing into the area (Ingram et al., 2002). Air-borne infrared radiometry studies during 1978/79 and 1980/81 showed that even during winter, the North Water has only a very thin ice cover (Steffen, 1986). Sampling completed during 1997–99 extended into early spring and late fall (Barber et al., 2001; Deming et al., 2002), providing a relatively comprehensive understanding of the annual carbon cycle in the North Water. A synthesis of inorganic carbon data from 1998 and 1999 (Miller et al., 2002) confirmed that although the surface waters were supersaturated in CO2 under the sea ice in early spring, by the time the ice cleared, pCO2 had dropped below atmospheric values, and the water remained undersaturated until the ice began to form

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Figure 8: The North Water. Shaded region gives the approximate location of the polynya. Depth contours are every 100 m through 1000 m. again in the autumn (Figure 9). Ice algal activity was extremely high in the spring (Michel et al., 2002), consistent with the observed pCO2 trend, but colored dissolved organic matter (CDOM) observations in the ice and in the surface waters indicated that photochemistry, as well as respiration, may have placed at least some limits on the photosynthetic CO2 sink (Belzile et al., 2000; Scully and Miller, 2000). Belzile et al. (2000) found that the ice not only contained high levels of photoreactive CDOM, but was also more transparent to ultraviolet than to photosynthetically active radiation, implying that within the ice, photochemical degradation may have been more efficient than photosynthesis. Unfortunately, Belzile et al. were unable to either confirm or refute that possibility with direct photooxidation measurements. Scully and Miller (2000) also saw high concentrations of CDOM in surface waters during the spring and early summer, with maxima at the surface, likely derived from ice melt. Because of photobleaching, CDOM maxima in other oceanic areas are more commonly observed below the surface (Miller, 1998), and therefore, photochemical degradation to inorganic carbon may have been sluggish in the North Water. Miller et al. (2002) found that total organic carbon varied dramatically in the surface waters throughout the study, at least partially attributable to rapid turnover of labile DOC, although contamination problems were also noted. The net result of the competing biological and photochemical processes occurring in the North Water during the summer was very low surface water pCO2 values (Figure 9; Table 3; Miller et al., 2002). The Miller et al. study did not directly measure air–sea CO2 fluxes but did generate a rough estimate of 0.3 Tmol for the net seasonal atmospheric CO2 drawdown from spring to fall. With the assumption that ice prevents outgassing during the winter, this


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Figure 9: Seasonal average pCO2 and ice cover observed in the North Water during 1998 and 1999 (Miller et al., 2002). pCO2 was calculated from measured DIC and total alkalinity; error bars are standard deviations of all measurements during each time period, indicating regional variability. Horizontal line gives the approximate atmospheric pCO2 during the study (365 µatm). value could be taken as an estimate of the annual CO2 drawdown by the North Water. For perspective, 35,000 km2 of open water (about half of the entire North Water region) could outgas that same amount of carbon during winter (assuming supersaturation levels observed in April) in about 20 days under wind speeds of 25 m s−1 (storm conditions). Bourke and Paquette (1991) saw some evidence of intermediate and deep water formation in North Water during the 1980s. However, Miller et al. (2002) were unable to conclusively identify an associated solubility-mediated CO2 drawdown, mainly because of no wintertime sampling and very little dissolved oxygen data. 3.3

The Northeast Water

The Northeast Water (NEW), off the northeastern coast of Greenland at about 78–80◦ N (Figure 10), was the subject of an interdisciplinary study during the summers of 1992 and 1993 (Hirche and Deming, 1997). The polynya formed because a land-fast ice barrier to the south, over Belgica Trough, prevented ice in the East Greenland Current from entering the area via an anticyclonic eddy around Belgica Bank (Schneider and Budéus, 1994). Although ice- and snowmelt runoff from Greenland appear to lower the salinity near the coast, the ice barrier that blocked ice from flowing into the Northeast Water also blocked melt waters, and therefore, the surface waters in the central NEW had relatively high salinities (Schneider and Budéus, 1994), as well as very low temperatures (Budéus et al., 1996), facilitating CO2 absorption (see Section 2, above). Schneider and Budéus speculated that the Northeast Water could have been an ice factory in winter, exporting shelf water, but no clear evidence for shelf or deep water formation was found (Budéus and Schneider, 1995;

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Figure 10: The Northeast Water. Shaded region gives the approximate location of the polynya. Depth contours are at 200 m, 500 m, 1000 m, and every 1000 m thereafter. Top et al., 1997). Consistent with the low temperatures, Yager et al. (1995) saw very low pCO2 values in the surface waters (Table 3) during the summer of 1992. Despite that undersaturation Yager et al. determined that calm weather limited atmospheric CO2 absorption, leading directly to development of the seasonal rectification hypothesis (Figure 5). In a later reanalysis that included data from 1993, Falck (1999) estimated somewhat higher shortterm air-to-sea gas exchange rates at some stations. Falck also estimated that overall, the net effects of calcium carbonate precipitation and dissolution were insignificant, although the alkalinity data presented in Yager et al. showed some rather dramatic localized variations indicating that either calcifying organisms or local or remote terrestrial runoff had some important (or at least interesting) effects. Surface DOC concentrations measured during the same study were already higher in the polynya than under the ice in early spring, although much of that material appeared to be terrestrial and refractory (Skoog et al., 2005). The DOC concentrations then decreased from spring to summer, concomitant with an increase in DON (Skoog et al., 2001). Among the possible explanations were microbial degradation of the organic matter within the polynya, which could have released CO2 , and export to the East Greenland Current. Wallace et al. (1995) and Falck (1999) derived very high rates of primary production from observed net


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Figure 11: Approximate locations of some Antarctic polynyas discussed in the text. enrichments in dissolved oxygen and concluded that the bulk of the resulting organic matter must have been exported from the polynya, either by vertical sinking or horizontal advection, rather than being remineralized within the polynya. 3.4

Other Polynyas

A significant component of Antarctic Bottom Water appears to be formed in a polynya off the Mertz Glacier Tongue (Figure 11) on the Adélie Coast, south of Australia and New Zealand (Williams and Bindoff, 2003; Marsland et al., 2004; Williams, 2004). Although a number of cruises have attempted to study the carbon dioxide system in the polynya, due to extremely difficult conditions, very few have been successful. A transect along 145◦ E during late summer of 1993 showed that pCO2 dropped dramatically near the coast (Metzl et al., 1999). Sutherland et al. (2002a, 2002b) also report surface pCO2 data from the area during the 2000–2001 summer, but they show no clear relationship with the polynya. A three-year study of the Mertz Polynya, including a detailed winter expedition, investigated deep water formation under the polynya and found that the oxygen levels in the High Salinity Shelf Water (HSSW) were higher than those seen within the (undersaturated) surface waters of the eastern part of the polynya (Williams and Bindoff, 2003). Williams and Bindoff concluded that the HSSW must have formed from surface waters on the western side of the polynya, after the westward current along the coast had carried the water through the polynya, providing more time for equilibration with the atmosphere. Additional analysis of the oxygen fluxes is underway to further test this hypothesis (G. Williams, personal communication).

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Polynyas in the Weddell Sea (Figure 11) have long been invoked as potential sources for Antarctic Bottom Water (Foster, 1972; Gordon, 1978; Comiso and Gordon, 1998), but early interpretations of inorganic carbon (Chen and Poisson, 1984; Anderson and Jones, 1991) and CFC (Mensch et al., 1996) data from the Weddell Sea were consistent with the idea that the deep waters in that region form mainly under ice shelves rather than in polynyas. However, total inorganic carbon in Weddell Sea Bottom Water increased significantly between 1993 and 1998 (Hoppema et al., 1998), implying a direct connection with increasing atmospheric CO2 concentrations, possibly through coastal polynyas. Holland (2001) showed that the Weddell Polynya, which forms intermittently at Maud Rise (Figure 11), could produce convection as deep as 1 km, and in 1998 Hoppema (2004) found a large, sub-surface water mass with high inorganic carbon concentrations, extending to depths of about 1 km in that same area. Hoppema explained his observations as resulting from sub-surface remineralization, without discussing the polynya, but the close to proximity to the polynya certainly warrants further investigation into how biogenic and physical processes may interact in this potentially deep-convecting polynya. Although the Cape Bathurst polynya (Figure 4) is one of the largest recurring polynyas in the Arctic, only sparse information is available on its physical forcing (Arrigo and van Dijken, 2004b; Barber and Hanesiak, 2004) or biogeochemistry. McLaughlin et al. (2002) measured CFC distributions in the area but related them to large-scale circulation of the central Arctic basin, rather than to local shelf and polynya processes. Data from recently completed studies that included carbon dioxide, methane, and carbon monoxide distributions and dynamics are expected to dramatically expand our understanding of carbon cycling in this polynya.

4 Sulfur Over the last three decades research into the marine sulfur cycle has become increasingly important, beginning with the seminal paper by Charlson et al. (1987), documenting the importance of dimethylsulfide [DMS; (CH3 )2 S] fluxes to the atmosphere, the role of DMS in forming cloud condensation nuclei, and its potential negative feedback on global climate. Although direct measurements of DMS fluxes are rare, with methods only recently becoming available (e.g., Huebert et al., 2004), at least 70% of the sulfur flux from the oceans to the atmosphere is thought to be in the form of DMS, accounting for as much as 50% of all natural (including terrestrial and volcanic) atmospheric sulfur sources (Schlesinger, 1991). Although the oceanic DMS cycle is complex (Malin et al., 1992; Andreae and Crutzen, 1997), the oceanic DMS flux to the atmosphere ultimately depends on the algal production rate of the DMS precursor [dimethylsulfoniopropionate, DMSP; (CH3 )2 SCH2 CH2 COOH], the DMSP degradation pathway (Kiene, 1996b; Kiene et al., 1999) and rates (Kiene and Bates, 1990; Kiene, 1996a), as well as various physicochemical factors (e.g., diffusivity). Phytoplankton species composition and physiological state are the two most important biological factors determining DMSP production rates; in general, dinoflagellates and haptophytes (e.g., Phaeocystis) have the highest DMSP cellular quotas, relative to other algal classes (Keller and Korjeff-Bellows, 1996). Algal DMSP lyase activity (DLA) moderates the conversion of DMSP to DMS (Cantoni and Anderson, 1956), but the presence of the enzyme is species-specific and not well documented (e.g., Stefels et al., 1995). Harada et al. (2004) recently showed that DLA is higher at low than at high nutrient concentrations (consistent with the hypothesis that DLA is involved with oxidative stress protection; Sunda et al., 2002).


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Significant DLA can also result in diel DMS levels varying by more than an order of magnitude in systems dominated by vertically migrating dinoflagellates (Merzouk et al., 2004). In addition, trophodynamic factors such as grazing and food-web structure also directly influence DMS production and release. For instance, krill grazing on pelagic or sea-ice algae and copepod grazing on diatoms have been shown to be significant mechanisms of DMS release to the water column in both Antarctic and Arctic polynya regions (Daly and DiTullio, 1993; Lee et al., 2003; Kasamatsu et al., 2004). Finally, DMSP can be used by bacterioplankton as an osmolyte (Kiene et al., 2000), possibly helping explain the high DMSP levels found in sea-ice algae (see below). The polar regions are a disproportionately large source of marine DMS (Kettle et al., 1999) due to haptophyte dominance in these areas. However, polynya regions are undersampled with respect to DMS and DMSP, and of 37 coastal polynya systems identified in the Antarctic region (Arrigo and van Dijken, 2003b), sulfur measurements have been made in only a few, mainly the Ross Sea (Figure 6) and Prydz Bay (Figure 11). Due to the massive blooms of the colonial haptophyte, Phaeocystis antarctica, DMS concentrations in the Ross Sea can exceed 300 nM in surface waters during austral summers (DiTullio et al., 2003). These values are 100 times higher than the average concentrations observed in the world’s oceans (Kettle et al., 1999). During the peak colonial P. antarctica bloom in the Ross Sea (November; DiTullio et al., 2000), integrated DMS values (reaching 25 mmol m−2 ) in the upper 50 m of the water column were also orders of magnitude higher than the average observed in Southern Ocean waters (Curran et al., 1998). During late summer (February) of 1992, DMS concentrations were lower (up to 2 mmol m−2 ) than during the colonial bloom period in spring (DiTullio and Smith, 1995), but still high compared with observations in temperate waters. At present, we do not clearly understand the important physiological and life-history factors affecting DMSP production in P. antarctica blooms, preventing reasonable estimations of the true DMS sea-to-air fluxes in the Ross Sea. Although DMSP production is high in the Ross Sea, both DMSP export to depth with sinking biodetritus (DiTullio et al., 2000) and DMS consumption rates (DiTullio et al., 2003) within the water column may also be high. Elevated DMS concentrations have also been observed during a colonial P. antarctica bloom in the Prydz Bay polynya region (Figure 11; Gibson et al., 1990). Similar to the Antarctic, measurements of biogenic sulfur production have been made in only a couple of the many polynyas in the Arctic (Winsor and Björk, 2000). Although the North Water (Figure 8) is one of the most productive polynyas in the Arctic, a recent study found relatively low DMS and DMSP concentrations in the region (Bouillon et al., 2002), especially in comparison to the Ross Sea polynya. This difference was attributed to the dominance of large, DMSP-poor centric diatoms (Thalassiosira spp. and Porosira glacialis) in the North Water and DMSP-rich Phaeocystis antarctica in the Ross Sea. Bouillon et al. suggested that the two polynyas are at opposite ends of a DMS production spectrum. As noted above, algal species composition and physiological state are undoubtedly the important parameters responsible for these differences. While surface water DMS concentrations may be low (less than 10 nM) during winter (DiTullio and Smith, 1992), deep mixing events in shallow coastal polynyas can directly link the benthic DMS and DMSP cycles with the atmosphere. Recent work in the St. Lawrence Island polynya (Figure 4) promises to provide insight into the importance of this phenomenon. Sea ice from both the Antarctic and Arctic often contains unusually high concentrations of DMSP (Kirst et al., 1991; Levasseur et al., 1994; DiTullio et al., 1998; Bouillon et al.,

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2002). For example, DMSP concentrations in ice algae from the Weddell Sea (Figure 11) were orders of magnitude higher than in phytoplankton from nearby open waters (Kirst et al., 1991). Not only haptophytes (Kirst et al., 1991), but also diatoms (Levasseur et al., 1994; DiTullio et al., 1998; Trevena et al., 2000, 2003) are important producers of DMSP in sea ice. Sea-ice algal DMSP production will strongly influence DMS release in a polynya region itself, as well as affecting nearby marginal ice zones through sea ice and algae transport out of the polynya. Quantifying the sea-to-air DMS flux associated with sea-ice algal DMSP production from polynya regions, however, will be a challenge for future research due to the spatial and temporal patchiness of biogenic sulfur production in both polynya waters and in the ice (DiTullio et al., 1998; Trevena et al., 2000). The majority of these studies reported DMS and DMSP standing stocks and did not measure cycling rates. Before accurate conceptual or numerical models of sulfur cycling and air–sea exchange in polynyas can be developed, many more comprehensive measurements of pelagic and sea-ice DMS cycling rates in polynya regions, as well as benthic remineralization rates, are needed.

5 Methylhalides Almost no data are available from polynyas on methyl halides, which may contribute to tropospheric ozone depletion (Barrie et al., 1988). However, Sturges et al. (1992, 1993) have found that ice-algal communities in both the Antarctic and the Arctic can produce bromoalkanes. Yvon-Lewis et al. (2004) determined CH3 Br and CH3 Cl along the transect between Hobart, Tasmania and the Mertz Glacier, and they found extremely low concentrations at the southern end of the transect, which they interpreted as indicating strong vertical mixing, although they did not make a direct connection with possible convection in the Mertz polynya. Incubations of various phytoplankton species have shown that Phaeocystis produce more CH3 Br than other phytoplanktonic species (Scarratt and Moore, 1996; Sæmundsdóttir and Matrai, 1998), and Baker et al. (1999) noted a positive correlation between CH3 Br and DMSP concentrations in North Atlantic waters dominated by Phaeocystis spp. Since Phaeocystis spp. are such an important component of phytoplankton communities in both Arctic (Smith et al., 1991) and Antarctic waters (El-Sayed et al., 1983; Gibson et al., 1990), it is logical to predict that production in polynyas is a significant source of atmospheric methylhalides.

6 The Future for Air–Sea Gas Exchange in Polynyas We are still learning what questions to ask about gas dynamics in polynyas; we have in hand only intriguing snippets of data showing that every polynya is different. Methods for determining air–sea gas exchange from satellites are only now being conceived, and therefore, a comprehensive understanding of the role polynyas play in global gas cycles is many months of ship time and many sample analyses away. The following gives our personal, and certainly biased, list of the outstanding questions that need to be addressed. • What happens in fall and winter? For obvious reasons, most of the chemical data from polynyas has been collected in summer or spring seasons representing less than half the year. We certainly cannot construct a complete picture of a polynya’s annual cycle from


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such limited data, and assumptions that gas fluxes are not significant in ice and that wintertime biological activity is limited to slow respiration are no longer acceptable. Short-term high wind events strongly dominate air–sea gas fluxes (Bates and Merlivat, 2001), and therefore open water during winter storms will be important to net annual exchanges in polynya areas, particularly for gases like carbon dioxide and methane that tend to accumulate under sea ice and for shallow, coastal polynyas where wind-driven mixing can directly connect the benthos with the surface. Leads within pack ice could also be significant sites of winter outgassing, especially considering the areal extent of these features. In addition, if we want to confirm whether intermediate and deep water formation in polynyas is an important mechanism for CO2 sequestration, we will need to sample when convection is occurring, not in the summer, when the surface waters are stratified by meltwater and warming. Of course, autumn and winter (and even early spring) field work in polar regions is extremely difficult and requires careful planning, as well as substantial financial and logistical resources. However, we have learned a lot over the last decade about how to conduct such work (Gibson and Trull, 1999; Perovich et al., 1999; Rosenberg et al., 2001; Barber et al., 2004), and we have now entered an era in which it is quite reasonable to attempt extensive winter sampling. • How much does gas move in ice? While it is probably true that sea ice inhibits direct air–sea gas exchange, recent studies on first-year ice have shown that it is not simply a passive barrier. Eddy correlation measurements have found very large vertical CO2 fluxes above first year sea ice, sometimes contrary to the air–sea gradient (Papakyriakou et al., 2004; Semiletov et al., 2004). In addition, both the inorganic and organic carbon contents of sea ice can be very high (Giannelli et al., 2001; Krembs et al., 2002; Delille et al., 2004). We have known since at least the mid-1970s that sea ice is permeable to gas transfer (Gosink et al., 1976), and these new studies imply that sea ice may be acting almost as a capacitor, storing carbon and controlling the air–sea exchange. The implications for gas fluxes in polynyas and leads, which are often covered by thin, new ice, could be significant. • What are the potential climate change feedbacks in polynyas? Perhaps the most critical question is how much gas fluxes really can be changed by temperature and seasonal ice cover. Based on past studies, polynyas appear to be net sinks for atmospheric carbon dioxide but sources of DMS, implying that polynyas could mitigate anthropogenic greenhouse warming. However, we do not know how robust those source-versus-sink balances are. Without comprehensive seasonal data and rate constants for the important elemental cycling processes, we do not even know whether the net sources and sinks we have hypothesized are true, much less whether they are as critically dependent on the timing of the ice cover and primary production cycles as they appear to be. Nonetheless, we can speculate about some examples of possible, although probably simplistic, feedbacks. If seasonal polynyas are indeed net annual CO2 sinks, warming could easily erode the sink by extending open water into fall, winter, or early spring periods when the surface waters are supersaturated. Although this positive feedback could be significant, it would probably be only short lived. Currently, carbon held under the ice during winter is still in the surface waters and therefore is protected from outgassing only until that water mass advects into more temperate areas where ice does not form in the winter; i.e., seasonal rectification can only provide carbon storage through the inter-annual to decadal time scale. Long-term carbon storage is effected through deep- and intermediate-water

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L.A. Miller and G.R. DiTullio formation, as well as through particulate organic carbon export, and a systematic global warming that simply melts ice, resulting in less ice cover at any given time of the year, would probably increase surface stratification and reduce any net carbon export through shelf water formation. On the other hand, if despite a decrease in spring and summer ice, substantial areas of ice still form in winter (Folland et al., 2001), simply increasing the areas of seasonal ice cover and polynyas, the carbon sink could be enhanced. While a decrease in ice cover might be expected to increase primary production, the accompanying increase in exposure to UV radiation would have the opposite effect, with possible decreases in emissions of biogenic gases, such as DMS. The net effect of UV on DMS fluxes in polynyas, however, will certainly hinge upon the differential UV effects on DMSP production in diatoms and Phaeocystis spp. versus the numerous factors affecting DMS cycling and photooxidation rates. Increasing UV exposure also increases DOC cycling, although whether that would be manifested as an increase in CO2 regeneration or as polymerization to more refractory forms of DOC is uncertain. In addition, concentrations of both DMSP and DOC are higher in ice than in seawater, and therefore, a loss of ice may further decrease biogenic gas releases, as well as organic matter recycling. Finally, species shifts induced by either UV or sea surface temperatures (i.e., Phaeocystis to diatoms, not to mention coccolithophorids) could have dramatic effects. To say the least, the balance between all these competing effects is difficult to predict or model at this time.

Throughout this chapter, we have presented many speculations and little concrete information, and the question of climate change feedbacks takes that discrepancy to the extreme. Yes, it is a scientific cliché to claim that too little is understood and that more research is required, but the cliché is applicable here. Nonetheless, we now have the capacity to adequately address the important questions concerning gas distributions and exchanges in polynyas and to estimate their role in regional and global elemental cycles.

Acknowledgements We thank S. Johannessen, K. Johnson, R. Macdonald, C. McNeil, and W. Williams for helpful conversations and T. Takahashi for providing the data from the 1984 Polar Sea expedition in the Ross Sea. P. Olsen was of critical assistance in digging up some rather obscure, as well as misquoted, references. The thorough comments of three anonymous reviewers were also very helpful.

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Chapter 6

Biogeochemistry of Polynyas and Their Role in Sequestration of Anthropogenic Constituents M. Hoppema1 and L.G. Anderson2 1 Alfred Wegener Institute for Polar and Marine Research, D-27515 Bremerhaven, Germany 2 Göteborg University, Department of Chemistry, S-421 96 Göteborg, Sweden

Abstract Polynyas are common occurrences all around the Arctic and Antarctic. Coastal polynyas are generally highly productive, which can lead to substantial CO2 drawdown. Consequently, they are important sink regions for atmospheric CO2 . Depending on the surface area, the timing, duration and other factors, large differences exist as to the importance of polynyas in a biogeochemical sense. In the Arctic, the North Water Polynya seems to be the most important one, while in the Antarctic the most important is the Ross Sea Polynya. Polynyas in the Arctic have been better investigated and therefore the important polynyas are described with some confidence as to accuracy and completeness. For the Antarctic, this only holds for the Ross Sea Polynya. For many other Antarctic polynyas, only incomplete information is available. This is true even for the large, well known Weddell Polynya of the 1970s, which represents one of the few open-ocean polynyas. Here its biogeochemical role is semiquantitatively assessed by combining the physical data from the 1970s with the known distributions of biogeochemical properties from recent years. It is deduced that the Weddell Polynya was a significant one-time sink for anthropogenic CO2 and CFCs, with ensuing deep-sea sequestration. Notably, some coastal polynyas are instrumental in transferring anthropogenic CO2 from the ice-free shelves to the abyssal oceans.

1 Introduction Polar regions are extreme regions, characterized by extreme conditions. They are cold, stormy, foggy, scantily-inhabited and poorly-investigated. One of the most characteristic properties of these regions is the perennial ice coverage, which necessarily has a large seasonal variation superimposed on it. Ice coverage has a large impact on the physical, chemical, geological and biological features. The ice itself is the matrix for organisms, particulate matter and chemical species, but it also influences the distributions of properties in the ocean underneath and the atmosphere above it. Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74006-5

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Under the pack ice of the Antarctic Ocean, significant undersaturations of oxygen and anthropogenic chlorofluorocarbons (CFCs) have been observed (e.g., Weiss et al., 1979; Klatt et al., 2002). These are gases with a relatively short equilibration time with the atmosphere, which implies that the ice cover exerts a strong influence. For species with considerably longer equilibration times, like CO2 , the influence of the ice cover is likely to be even greater, because they would need a longer time of ice-free water to achieve equilibrium. Several studies document the importance of ice cover with regard to the CO2 distribution (e.g., Bates et al., 1998; Gibson and Trull, 1999; Sweeney, 2003). Moreover, Stephens and Keeling (2000) contend that the Antarctic sea ice was instrumental in causing the large differences between atmospheric CO2 levels in the glacial and interglacial periods. The surface waters of the Arctic Ocean are supplied in a different way than in the Antarctic Ocean, namely, through advection from both the Pacific and Atlantic Oceans. These waters are cooled on their way to the Arctic and hence the solubility of gases increases, resulting in undersaturation, as time is too short to reach equilibrium before the water reaches the ice cover. For CO2 this is reinforced by substantial primary production over the shelves prior to entrance to the central ice-covered Arctic Ocean (e.g., Kaltin and Anderson, 2005). If the ice pack represents such a major factor, we expect that the absence of sea ice in regions which are usually ice covered will give rise to major changes and anomalies in the oceanic and atmospheric conditions. This holds for chemical species as well as for biogeochemical processes. Hence, we expect and use it as our working hypothesis that polynyas are characterized by anomalous spatial and temporal property distributions, which may exert a basin-wide influence. Our aim is to qualitatively and, if possible, quantitatively estimate the added value that polynyas possess for the cycling of anthropogenic and non-anthropogenic species in ice-covered regions. A scenario for anomalous conditions within a polynya according to Lizotte (2003) could be as follows. Many coastal, wind-driven polynyas are “ice factories”, implying that sea ice is produced while the brine is rejected and transported to depth. The ice is subsequently blown away from the shore. Thus dense shelf water is produced which will be detrained off the shelf. This draws water onto the shelf to replace it, which in the Antarctic is mostly Modified (warm, saline) Circumpolar Deep Water (MCDW)—this in turn contributes heat to keep the polynya open. Brine production tends to deepen the surface mixed layer on the shelf, which is accompanied of a redistribution of material, both in dissolved and particulate phase. Also, the MCDW that mixes on the shelf influences the property concentrations. This sets the initial conditions for the spring biogeochemical processes on the shelf. Subsequently, during spring and early summer the polynya changes into an ice-melting area. This tends to stabilize the upper surface mixed-layer, which is beneficial for phytoplankton growth, and thus CO2 and nutrient drawdown. In the Arctic Ocean the same type of “ice factories” are present in the wind-driven polynyas, but the produced brine-enriched water will be replaced by shelf water originating from adjacent oceans and from river runoff. Different formation mechanisms exist for different polynyas. In this regard they are commonly assigned either as sensible-heat or latent-heat polynyas. The former are generated by sensible heat from the ocean, i.e., mostly relatively warm water that upwells into the cold polynya area, thus inducing ice melt or impeding ice growth. These polynyas may be associated either with convective overturning of the water column or entrainment of deep water in the mixed layer. In latent-heat polynyas, sea ice is continually formed and removed by winds and/or currents. The cold polynya loses heat to the even colder atmosphere; this heat is delivered by the latent heat of fusion of the sea ice. Although this is an often used and convenient classification, in reality most polynyas are a mixture of these types. Another subdivision

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could be into coastal and open-ocean polynyas. The majority of polynyas occurs along the coasts of the continents or islands. However, open-ocean polynyas, especially when they develop to large-scale features like the Weddell Polynya of the 1970s, may instantly have impact on vast regions and even on the global overturning circulation. This warrants major consideration of such features. Due to the contrasting hydrographic backgrounds, differences exist between Arctic and Antarctic polynyas and their formation. In the Arctic the water column features a relatively high stability and thus vertical mixing across the stable pycnocline is hardly feasible. Hence, in Arctic polynyas the sensible-heat component of polynyas is not well developed—albeit some particular sensible-heat polynyas do occur (Morales Maqueda et al., 2004). In contrast, the Antarctic water column possesses a low stability, which allows irregular convective overturning. Besides that, upwelling of warm deep water is common in the Southern Ocean, which also plays a role in polynya formation. It is manifest that such differences in occurrence and formation history also exert impact on biogeochemical processes in Arctic and Antarctic polynyas.

2 2.1

Antarctic Polynyas Weddell Polynya in the 1970s

The Weddell Polynya can be definitely considered the most impressive polynya of the Antarctic Ocean. Its spatial extent was about 350 000 km2 (Carsey, 1980). Many theoretical studies were dedicated to this unique phenomenon (e.g., Beckmann et al., 2001; Holland, 2001), which occurred each winter during the three consecutive years 1974–1976. It has not occurred to this extent ever since. Gordon (1982) speculated that a polynya may have occurred at least once before the 1970s. The polynya as such has been observed only by microwave satellite imagery (Carsey, 1980), but Gordon (1978, 1982) and Foldvik et al. (1985) discovered features in the Weddell Sea that were thought to be oceanographic remnants of it. In particular, during the Islas Orcadas cruises in the austral summers of 1976 and 1977, Gordon (1978) found a chimney with a radius of 14 km extending to 4000 m depth west of Maud Rise. A similar feature was observed nearby by Foldvik et al. (1985). The chimney was characterized by extremely cold, low-salinity and high-oxygen water as to when compared to the surrounding deep water. Gordon (1978) suggested that, provided the chimney derived from the Weddell Polynya, there must have been many more of such features. The observations of Foldvik et al. (1985) seem to support this. In addition, Gordon (1982) described a “cold spot” in 1978 near 20◦ W—in its third year the position of the polynya, which had drifted with the general flow of the Weddell Gyre—with a temperature maximum between 0.2 and less than −0.2◦ C. The spot was characterized by cold water (relative to the surrounding water) down to 2700 metres (m). During the Weddell Polynya the cold, relatively low-saline winter surface waters were transferred directly to the deep ocean layers by local convective processes. Gordon (1982) assessed the impact of polynya convection on the ventilation of the deep Weddell Sea. He considered distributions of potential temperature and salinity of a cruise just before (1973) and a cruise right after (1977) the occurrence of the polynya—since the Weddell Sea supports a regime with low energy, the effects of convection are expected to persist for a relatively long time. The volume of deep water involved in polynya processes (with deep-water cooling) was estimated to be 1.5 × 1015 m3 . An analysis of the mixing diagram suggested that 10–20%

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of the modified deep-water must have been derived from winter surface water. Thus the total volume of surface water convected into the deep Weddell Sea amounted to 1.5–3 × 1014 m3 , which equals 1.6–3.2 Sv (1 Sv = 106 m3 s−1 ) during the entire period of three years (1974– 1976). This is a substantial contribution to the total ventilation by the Weddell Sea/Atlantic sector of the Southern Ocean (Orsi et al., 2002) and more than the recent ventilation through Weddell Sea Bottom Water production (Fahrbach et al., 2001). In situ observations and modeling have shown that the Weddell Polynya had a major impact on the oceanographic conditions of the Weddell region. But did the polynya also have impact on the ocean-atmosphere exchange of gases, such as oxygen, CO2 and CFCs, or other biogeochemical properties? Gordon (1978, 1982) measured dissolved oxygen during his polynya cruises, but no data on CO2 or CFCs are available. We assess the effects of the polynya on the oceanic CO2 , its anthropogenic component and the nutrients, using as a basis the analysis of Gordon (1982). In 1977 in the cold chimney, O2 concentrations greater than 5.6 ml/l were observed, where normally in the temperature maximum/O2 minimum layer less than 5.0 ml/l are found (Gordon, 1978; units as used by the author instead of the more usual µmol kg−1 ). Also in 1978 the O2 minimum (less than 4.8 ml l−1 ) was interrupted by O2 values greater than 5.2 ml/l (Gordon, 1982). This proves that surface water, which has a high O2 concentration, was transported down through the water column. Thus the convective processes associated with the polynya resulted in elevated ventilation of the deep Weddell Sea as compared to the same region without a polynya. Since surface water has a relatively low Dissolved Inorganic Carbon (DIC) concentration—DIC is the sum of all CO2 species— as compared to deeper water (Figure 1), we expect that the processes that caused a high-O2 state simultaneously caused a low-DIC state in the deep water. This also would be true for the major nutrients (nitrate and phosphate), which in their vertical distributions have many features in common with DIC. We estimate, using typical DIC concentrations in the surface layer and the deep water (Figure 1), that a cold spot as observed by Gordon (1978) would probably contain 5–10 µmol kg−1 less DIC than the surrounding water. The DIC difference caused by the polynya is much smaller than the corresponding O2 difference (greater than 25 µmol kg−1 ), because of the much stronger O2 gradients within the water column. Although convective processes associated with the polynya cause a low-DIC patch in the deep-water realm, simultaneously they transfer anthropogenic CO2 into the deep water. The surface water has been in contact with an atmosphere that also contains fossil-fuel CO2 and thus, depending on the degree of equilibration, it is “contaminated” with a certain portion of it. The degree of equilibration in the Weddell Sea should be relatively high (Anderson et al., 1991) because the elevated level of CO2 in the atmosphere merely inhibits outgassing of CO2 of upwelled deep-water with a high partial pressure of CO2 (pCO2 ). Hence, the elevated CO2 in the surface layer is of anthropogenic origin. Poisson and Chen (1987) estimated that in 1981 the Weddell Sea surface layer contained 28 µmol kg−1 of anthropogenic CO2 . If we take the value for the mid-1970s to be 25 µmol kg−1 , we calculate (using 1.5–3 × 1014 m3 of convected surface-water as estimated by Gordon, 1982) that the total uptake during the period of the three polynya years amounts to 4–8 × 1012 mol C. This equals 1.5–3×1013 g C yr−1 , which is significantly larger than the amount (8×1012 g C yr−1 ) estimated by Anderson et al. (1991) for the sequestration of anthropogenic CO2 in 1989 through dense bottom-water formation in the southern Weddell Sea. Clearly, an open-ocean polynya event contributes to enhanced ventilation and uptake of anthropogenic CO2 , but since the processes associated with polynya formation distort the extant dynamics and circulation of the region, these should also be taken into account. Enhanced ventilation and the corresponding oceanic uptake of anthropogenic species rely on


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Figure 1: Section cutting the Weddell Gyre (as displayed in the chart of the Atlantic sector of the Southern Ocean) contoured for DIC (µmol kg−1 ) in the upper 2 km of the water column. Data from FS Polarstern cruise ANT XV/4, April–May 1998. the fact that surface water is becoming isolated from the atmosphere. However, the surface water must be replaced by upwelled subsurface water. In the Weddell Sea the intermediateand deep-waters have low concentrations of O2 and CFCs and high concentrations of DIC and nutrients, which are derived from the Circumpolar Deep Water of the Antarctic Circumpolar Current to the north. As these upwelled O2 and CFC undersaturated waters reach the surface, they will in time equilibrate with the atmosphere, which implies that the polynya event will eventually have led to a net uptake of atmospheric O2 and CFCs. For CO2 it is more complicated. Contrary to O2 and CFCs, the upwelled subsurface water is oversaturated in CO2 , which after introduction in the surface layer, will tend to release CO2 to the atmosphere. The relevant question is whether the Weddell Polynya eventually has led to more CO2 uptake or not—starting from the fact that the contemporary Weddell Sea is a sink for atmospheric CO2 (Hoppema et al., 1999). The answer is probably yes. The Weddell circulation creates a highly efficient biogeochemical–physical coupling between the surface and subsurface layers. Organic material produced in the surface layer is almost completely degraded at shallow depths beneath the surface layer (Usbeck et al., 2002). The CO2 -charged intermediate water is partly upwelled into the surface layer, but more importantly, about 80% of it is isopycnally transferred to greater depths within the Weddell Gyre, and from there to the abyssal Antarctic Circumpolar Current north of it (Hoppema et al., 2002). This unique mechanism provides the sequestration of natural CO2 in the abyssal oceans, where the Weddell Sea contributes a significant part of the ocean-wide deep-sea sequestration (Hoppema, 2004). If, due to the polynya event, increased upwelling of CO2 -charged subsurface water occurs, then as a consequence less CO2 will be available for deep-sea sequestration. This would

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tend to increase the atmospheric CO2 and thus counteract the uptake of anthropogenic CO2 . It is not difficult to see that if surface water with DIC in the range of 2200–2215 µmol kg−1 is replaced by subsurface water containing 2265 µmol kg−1 (see Figure 1) and the same alkalinity, this would lead to a net release of CO2 to the atmosphere. However, this is not all. The potential CO2 release from the surface layer is to a certain extent counteracted by biological activity fixing CO2 in organic material, which in turn is remineralized below the surface layer. This does not necessarily have to take place during the occurrence of the polynya, which is during winter when biological activity is at its minimum. Also CO2 drawdown during the ensuing spring and summer tends to counteract the high CO2 concentration due to upwelling, because undersaturation due to biological drawdown of CO2 induces the uptake of the previously released CO2 . Compensation of the increasing trend, caused by the upwelling of CO2 -rich deep water, is only feasible if the extent of biological drawdown of CO2 is enhanced beyond the usual level in the Weddell Sea. For this it is important to know which factor is limiting for primary production, and whether the upwelled water might increase its level. Currently primary production in the Southern Ocean is thought to be limited by the micronutrient iron (Boyd et al., 2000; Tremblay and Smith, 2007). The upwelled water tends to increase the iron content of the surface layer (De Baar and De Jong, 2001), and thus the usual level of primary production in the Weddell Sea will be enhanced due to the additional iron-charged upwelled deep-water. The elevated production will lead to elevated downward transport of organic material, which will be remineralized and isopycnally transported into the abyssal oceans. In summary, there are two competing processes involved in CO2 uptake or release due to the occurrence of the Weddell Polynya. Convective transport of anthropogenic CO2 -enriched surface water into the deep Weddell Sea tends to increase the CO2 concentration of the Weddell Sea. Upwelling of natural-CO2 enriched subsurface water, which would otherwise be available for abyssal sequestration, tends to decrease it. We suspect that the latter effect will to a large extent be counteracted by elevated biological activity in the surface layer, which through the biological carbon pump transfers CO2 to the intermediate-water layers again. Thus, the Weddell Polynya was instrumental in the uptake of an additional amount of anthropogenic CO2 in the order of 1013 g C. Although this is not a large share of the total oceanic uptake of fossil-fuel CO2 of order 2×1015 g C, it is a substantial part of the deep-sea sequestration of anthropogenic CO2 , which is important on time scales of hundreds of years. For the major nutrients the net effect of the Weddell Polynya is relatively insignificant. Since the nutrients are largely independent on anthropogenic activity and they cannot be exchanged with the atmosphere, their distributions are subject only to the major redistribution process associated with the polynya (i.e., the replacement of high-nutrient deep-water by low-nutrient surface-water). By the ensuing biogeochemical processes, the nutrient concentrations in the surface and deep layers are largely returned to their pre-polynya levels. An intriguing hypothesis was presented by Gordon (1982). He contended that conditions favorable for open-ocean convection, and thus polynya formation, may coincide with conditions unfavorable for dense-water formation on the shelves. The factor that could link these regions is the variable divergence of the system. Stronger divergence transfers more ice from the shelves to the open ocean, inducing additional ice formation over the shelves, thus promoting dense water formation. In contrast, ice advected to the open ocean is melted there, thus inhibiting convection. With less divergence the opposite of these processes occurs. If this remote coupling exists, it would strongly diminish the relevance of open-ocean polynyas like the Weddell Polynya in effecting elevated ventilation and uptake of anthropogenic species. We have emphasized the surplus that the Weddell Polynya was able to deliver with regard to


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ventilation and uptake of CO2 . Of course, the shelves are vital regions for deep- and bottomwater formation as well, and thus also for the uptake of anthropogenic species. If the scenario proposed by Gordon (1982) were true, the Weddell Polynya was just a different vehicle for achieving a similar magnitude of ventilation and uptake as ever. However, we suspect that the factors determining the extent of bottom-water formation on the shelves and deep ventilation in open-ocean polynyas act on sufficiently different spatio-temporal scales to allow only a weak negative correlation between the two mechanisms. While the absolute concentration of anthropogenic CO2 in the surface water of the Weddell Sea has continuously increased because of the increasing atmospheric CO2 level, if in the future the Weddell Polynya were to form, its effect on the oceanic CO2 uptake will increase. On the other hand, the DIC anomaly that it will cause in the deep-water will become smaller and smaller because the DIC concentration difference between the surface-water and the deep-water would decrease. Since O2 and the major nutrients are in a quasi steady state and independent of anthropogenic changes, their distributions due to convective processes in a future polynya will be similar to those in the 1970s. However, the latter conclusion would not hold true if the magnitude of future biological activity in the Southern Ocean would be changed through anthropogenic causes. 2.2

Recurrent Offshore Polynyas

After more than four decades of remotely sensed ice observations, the Weddell Polynya has been shown to be virtually a one-time event. Other open-ocean polynyas have been observed, although with different characteristics. Well-known examples of recurring polynyas are the Cosmonaut and the Maud Rise polynyas (Comiso and Gordon 1987, 1996), the occurrence of which has been documented in most years. Their lifetimes are between days and weeks. In some years the polynyas reoccurred several times during one winter. Their size is highly variable, but generally they are much smaller than the Weddell Polynya. As its name suggests, the Maud Rise polynya is found near the sea mount with the same name in the southern Weddell Sea just east of the prime meridian. Note that also the Weddell Polynya of the 1970s is thought to be associated with circulation-topography interactions at Maud Rise (Holland, 2001; Muench et al., 2001). The Cosmonaut polynya takes its name from the Cosmonaut Sea near the eastern edge of the Weddell Gyre circulation. Comiso and Gordon (1996) argued that the Cosmonaut polynya may form through locally enhanced upwelling of warm subsurface water (i.e., without convective overturning). Ushio et al. (1999), using oxygen data, demonstrated that convection in a shelf-break polynya in the Cosmonaut Sea entrains significantly more deep-water into the surface layer than that which takes place under the Weddell Sea pack ice. Several hypotheses exist as to the formation of (transient) open-ocean polynyas (Morales Maqueda et al., 2004), where also convection may play a role. Convective-like features in the Weddell Sea can also originate from the deeply mixed coastal current (Bersch, 1988), and these may subsequently be advected into the open ocean. In the formation of such features, (coastal) polynyas are likely to be involved. Wakatsuchi et al. (1994) described the formation of polynyas within the Antarctic Divergence between 70–120◦ E which come into existence by the combined action of upwelling and advection of dense coastal waters. Despite uncertainty as to its formation, downward transport of cold surface water may well be associated with open-ocean polynya generation. Unfortunately, there are no in situ oceanographic measurements to test this. However, remnants of convective processes may be discerned in vertical property distributions using non-polynya open-ocean data. For this

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Figure 2: Scatter diagrams of salinity (A), oxygen (B), silicate (C), and DIC (D) versus potential temperature for the central Weddell Sea. Data from FS Polarstern cruise ANT XIII/4, April 1996. purpose, plots of potential temperature (θ) versus salinity (S), oxygen (O2 ), DIC and silicate are displayed for the Weddell Sea (Figure 2). Seizing a suggestion by Gordon (1991), nonlinear features in the mixing plots between the Circumpolar Deep Water (CDW; θ > 0◦ C) and the Weddell Sea Bottom Water (WSBW; θ < –0.7◦ C) could be due to a third source water mass in the deep Weddell Sea, namely, deep-water ventilated through open-ocean convection. As described above, during and immediately after the Weddell Polynya this water mass was strongly present as a low-salinity portion centered at θ = −0.2◦ C (Gordon, 1991). In the 1990s, the mixing lines for salinity and O2 appear to be slightly non-linear and suggest lower salinity and higher O2 (Figure 2). This in turn suggests a surface-water component in the deep-water. However, the deviations from linear two end-member mixing seem to envelope a larger portion of the deep-water column (i.e., −0.2 < θ < −0.6◦ C). The reason for this is that, apart from the WSBW and CDW and the ventilated deep water through convective processes, a fourth end-member of mixing exists in the deep Weddell Sea. This is deep-water that originates to the east of the Weddell Gyre, most likely in Prydz Bay (Nunes


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Vaz and Lennon, 1996; Orsi et al., 1999; see also Section 2.5). In the Weddell Sea it is centered at θ of about −0.5◦ C. This water mass is characterized by a distinct CFC maximum, high salinity, high O2 , and low DIC and nutrient concentrations (Hoppema et al., 2001a). Notwithstanding the multi end-member mixing within the deep Weddell Sea, the anomaly at θ = −0.2◦ C is estimated to be about 0.002 for S and 2 µmol kg−1 for O2 . In the θ –DIC plot no anomaly can be distinguished near this temperature. Using typical O2 concentrations and salinities of the deep-water and the surface-water in winter, we estimate the fraction of surface water transported down into the deep-water to be of the order of 1%. It is evident that with such a small fraction and the generally much smaller water column gradients in DIC, a DIC anomaly would not be discernable. Note that a DIC anomaly due to the fourth end-member is evident from a non-linearity near −0.5/−0.6◦ C (Figure 2D). The ventilation of the deep-water of the Weddell Sea by convective processes in transient polynyas seems to be minor as compared to the Weddell Polynya, where the fraction was 10–20%. Because we do not know the time scale over which this anomaly is generated, it is impossible to accurately assess its importance. The estimates of the deep water residence time range from 3 years for the upper layers (Hoppema et al., 2002) to 35 years for the entire water column (Rutgers van der Loeff and Berger, 1993). If we take the short residence time estimate, transient polynyas ventilate only 10% of the ventilation of the Weddell Polynya. But in contrast to the Weddell Polynya, the transient polynyas are annually recurring features, which regularly contribute to the ventilation of the deep-water of the Weddell Sea and the sequestration of anthropogenic species. The polynya formation mechanism proposed by Wakatsuchi et al. (1994)—involving eddies in the Antarctic Divergence with deep-water upwelling combined with dense shelf water advection/downwelling—is potentially quantitatively significant for the transfer of ventilated, anthropogenic CO2 enriched shelf water into the intermediate-water layers, because of the high frequency of eddies there. The depth of sequestration depends on the density of the shelf water and the degree of underway mixing with adjacent, less dense water. Intermediateand deep-waters are subject to ventilation by this process and not the bottom waters formed around the Antarctic. The mechanism for the Cosmonaut polynya as postulated by Comiso and Gordon (1996) may be a special case of this process associated with the Antarctic Divergence, and even the Maud Rise polynya may have something to do with it (Enomoto and Ohmura, 1990). 2.3

Coastal Polynyas in the Weddell Sea

There are several polynyas bordering the Weddell embayment (here defined from the Antarctic Peninsula to 20◦ E), but their characteristics are very different and thus it is not appropriate to treat the coastal Weddell polynyas as a coherent group. Most information about the physical and biological signature of the polynyas was collected from the comprehensive studies by Arrigo and Van Dijken (2003) and Barber and Massom (2007). In the southern Weddell Sea, one of the largest polynyas around the Antarctic occurs off the very wide Ronne Ice Shelf; that is, in summer, but in winter the polynya is only moderately large. On the eastern side of the Antarctic Peninsula with the Larsen Ice Shelf, a vast field of perennial ice is found (Comiso and Gordon, 1998). Because of the strong winds and the strong ice transport from the south, the Larsen Ice Shelf polynya is small and does not open every year. Often the winter area of the polynya is larger than the summer area, because of the abating winds, which blow the ice off the coast. Off the southeastern ice shelves, which border a relatively narrow continental shelf, some small to moderately large polynyas usually occur, while in the Lazarev Sea east of the prime meridian two small polynyas are found.

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According to Arrigo and Van Dijken (2003), the Ronne polynya is as productive as the mean Southern Ocean south of 50◦ S—indicating that per unit area it is much more productive, and definitely more productive than the sea-ice covered ocean. All other Weddell Sea polynyas are less productive. This obeys the correlation between the width of the continental shelf and the extent of primary production as found by the above authors. An indication for the primary production potential of the southern and southeastern polynyas is found in Smetacek et al. (1992). They observed a superbloom associated with ice platelets in late winter/early spring. Within this, admittedly thin, layer of platelet ice (of order 1 m), the nutrients were exhausted to undetectable levels. The superbloom was most likely driven by passive accumulation of cells via floating platelet ice. These results are highly relevant for the polynya region in the southern Weddell Sea for two reasons. First, the level of solar irradiance as early as October is sufficient to support intensive phytoplankton blooms. And secondly, primary production in this region is apparently not limited by iron like in most other Antarctic regions. These conditions enable a long period of active biological production in the coastal polynya, which is eventually only limited by the macronutrients (probably nitrate) like in other oceanic provinces. Smetacek et al. (1992) cite also other superblooms off the southern Weddell Sea coast. They also contend that these superblooms act to significantly enhance the productivity of the entire North West (NW) Weddell Sea. Off the Filchner-Ronne Ice Shelf, one of the most important Antarctic regions for bottom water formation is found (Foldvik et al., 2004). The wide shelf and polynya are pivotal factors for salinization of the shelf water, which together with modified Warm Deep Water comprises the main ingredient of the bottom-water. This region may not be the most efficient for the uptake of anthropogenic CO2 (see also Section 2.5), but merely due to the quantity of bottom water production it must be significant for deep-sea sequestration. Figure 3 illustrates that the bottom water on the continental slope originating from the Filchner-Ronne shelf contains significant anthropogenic CO2 . Because of the adverse ice conditions in this part of the Weddell Sea, Larsen Ice Shelf polynya is normally not accessible for ships. However, in 1993 the ice conditions were extremely different from other years (Comiso and Gordon, 1998) and a large polynya had opened; fortuitously the ice breaker FS Polarstern happened to be there. Over the Larsen shelf and the continental slope pCO2 and nutrients (Figure 4) were found to be much lower than in the offshore Weddell Sea. Depletions of DIC and nutrients in the surface layer, which were caused by very rapid biological uptake, appeared to be several times higher than in other Weddell Sea areas (Hoppema et al., 2000). Carbon consumptions were comparable to those in the hyperproductive Ross Sea Polynya (Smith and Gordon, 1997; Bates et al., 1998). This is surprising because most of this region hardly ever experiences open water conditions (apart from irregular leads). It is suspected that the local phytoplankton population is extremely well adapted to a low irradiance regime, which enables rapid growth during a short period of time (Hoppema et al., 2000). Additionally, the grazer population, which can be an important factor in keeping Antarctic phytoplankton stocks low (Smetacek et al., 1990), is expected not to be well developed in such a region without a large phytoplankton biomass. At the location closest to the Larsen Ice Shelf, which may usually be situated in the coastal polynya, the nutrient and DIC depletion was the highest (Figure 4C). Although the nutrient concentrations did not reach limiting levels, the large potential of the coastal polynya in CO2 drawdown has been demonstrated. Smetacek et al. (1992) speculated that superblooms may also occur off the Larsen Ice Shelf under the influence of platelet ice. We expect that analogous to the Ross Sea Polynya, the Larsen Ice Shelf polynya, when it forms, is a sink of CO2 due to the high biological carbon drawdown.


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Figure 3: Anthropogenic CO2 concentration in four sections over the continental slope of the Weddell Sea. Data from Anderson et al. (1991). There are indications that off Larsen Ice Shelf, deep- and bottom-water formation takes place (e.g., Fahrbach et al., 1995; Weppernig et al., 1996). The local polynya is likely to play a role in this. The intriguing observations of anthropogenic CO2 are highly relevant for occurrences near Larsen Ice Shelf (Anderson et al., 1991; see Figure 3). In the southern Weddell Sea, anthropogenic CO2 is confined to the surface and bottom layers only, whereas off the tip of the Antarctic Peninsula the large deep-water volume of Weddell Sea Deep Water contains significant quantities of it. This implies that there must be input of anthropogenic CO2 into the western Weddell Sea, most likely from the Larsen Ice Shelf polynya. Note that tracer studies happen to suggest that off Larsen Ice Shelf predominantly deep-water is produced, and not bottom-water (Weppernig et al., 1996). Thus although the Larsen Ice Shelf polynya is relatively small, transient and does not occur every year, it must be highly efficient in the transfer of anthropogenic CO2 into the ocean. In the coastal polynya in the Lazarev Sea, Naqvi (1986) observed relatively high O2 and low nitrate concentrations in the upper 100 m, but the nutrients certainly were not limiting phytoplankton growth. In addition, the author did not find indications for shelf water that may participate in dense bottom-water production. Although bottom-water cannot be produced there, Orsi et al. (1993) reported that ventilated deep-water, which is not dense enough to reach the bottom, may originate from the coast of the eastern Weddell Gyre. It should be appreciated that for the entire Antarctic, such deep-water ventilation is approximately as important as ventilation by bottom-water production (Orsi et al., 2002). Thus, the

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Figure 4: Distribution of pCO2 (A), nitrate (B), DIC depletion (C) and salinity (D) at the sea surface off the Larsen Ice Shelf in the polynya (except for the DIC depletion, which is for the entire surface layer of about 100 m depth). SB is shelf break. Data from FS Polarstern cruise ANT X/7 of January 1993; see also Hoppema et al. (2000). small polynyas of the Lazarev Sea that are situated over a narrow continental shelf may still be active sites of ventilation and biological activity. 2.4

Ross Sea and Terra Nova Bay Polynyas

The Ross Sea Polynya is the largest (both in winter and summer) and best investigated annually recurring polynya of the Antarctic. Both synoptic winds and katabatic surges contribute to its formation, but also intrusion of deep-water on the shelf has its share in opening the ice (Jacobs and Comiso, 1989). A comparably small but influential feature off the western coast of the Ross Sea is the Terra Nova Bay polynya. It is highly persistent and thought to be producing as much as 10% of the annual sea ice of the Ross Sea and about 1 Sv of High Salinity Shelf Water (Kurtz and Bromwich, 1985). Thanks to many predominantly U.S. and Italian expeditions to this region, a solid baseline of in situ biogeochemical data exists. In


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contrast to most other Antarctic coastal regions, the biogeochemistry of which is only known from “snapshots”, seasonal cycles of several properties are known for the Ross Sea Polynya. Here we focus on the budget and net fluxes of inorganic carbon and nutrients. Additional material can be found in Miller and DiTullio (2007). Hyperproductivity is the term that has been used to describe austral spring phytoplankton growth in the Ross Sea Polynya (Smith and Gordon, 1997)—this is the more surprising as this is the southernmost region of the Antarctic Ocean. There is an enormous consumption of nutrients and CO2 in the spring and summer (Bates et al., 1998; Gordon et al., 2000), which are primarily fixed in particulate organic matter (Smith and Asper, 2000). However, the nutrient concentrations are never fully depleted and thus plankton growth appears not to be limited by the major nutrients. In the Terra Nova Bay polynya nutrients occasionally approach detection limits (Saggiomo et al., 1998). In late winter, DIC concentration in the surface layer was about 2200–2230 µmol kg−1 (Bates et al., 1998). There exists uncertainty as to the pCO2 in this time of the year; while Bates et al. (1998) find values close to those in the atmosphere, Sweeney (2003) reports significantly higher ones around 425 μatm. The lower values may have been due to early phytoplankton growth (Smith and Gordon, 1997). By late January the pCO2 has fallen to only about 130 µatm, while the DIC reduction is as high as 150 µmol kg−1 (Sweeney, 2003). The corresponding nitrate reduction is about 25 µmol kg−1 . Spatial distributions of pCO2 clearly reveal the polynya region by much lower values than those in adjacent areas (Sweeney et al., 2000; Barbini et al., 2003). Also in Terra Nova Bay, pCO2 values under 200 µatm were measured in summer (Barbini et al., 2003). Since alkalinity appears to be conservative, the CO2 reductions are predominantly caused by photosynthesis of non-calcifying organisms (Bates et al., 1998), which is supported by plankton data indeed. Bates et al. (1998) assessed that the Ross Sea Polynya is a net CO2 sink, where the CO2 uptake from the atmosphere (1–3.8 Tg C until mid January) roughly matches the export production at 250 m (Nelson et al., 1996). Smith and Asper (2000) showed that the nitrogen budget is strongly dominated by particulate matter in spring and that later in the season also regeneration, export and dissolved organic nitrogen contribute. Sweeney (2003) deduced net O2 uptake from the atmosphere and transport off the Ross Sea shelf. For CO2 he calculates a biologically-mediated uptake amounting to 1.3–1.8 mol m−2 during the summer and a negligible release during the ice-covered winter. Note that the polynya does not seem to play a great role in his computations. In contrast to O2 , no CO2 enrichment in the off-shelf flowing water could be found. We expect the water flowing off the Ross Sea shelf to be enriched in CO2 , namely in anthropogenic CO2 , which accompanies Ross Sea Bottom Water formation. North of the Ross Sea, this bottom-water is found to be contaminated with anthropogenic CO2 (Sabine et al., 2002). In the Ross Sea Polynya and in the Terra Nova Bay polynya, High Salinity Shelf Water is produced by brine rejection, which is the basis for the densest bottom-water mass generated in the Antarctic (Jacobs et al., 1985; Gouretski, 1999). Especially the Terra Nova Bay polynya appears to contribute much (Budillon et al., 2003). Since this polynya is persistently open, the extent of re-equilibration of CO2 and other gases should be relatively high, implying that the uptake of anthropogenic CO2 must be high as well. In aggregate, the Ross Sea Polynya appears to be more productive than the abutting regions and drawdown of nutrients and CO2 is substantially enhanced. Furthermore, the Ross Sea and Terra Nova Bay polynyas are the sites of High Salinity Shelf Water formation, which is accompanied by uptake of anthropogenic CO2 and CFCs. These in turn are sequestered in the Ross Sea Bottom Water in the abyssal oceans north of the Ross Sea. However, compared

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to the Weddell Sea and Atlantic sector of the Southern Ocean, the rate of bottom-water formation in the Ross region is relatively small (Orsi et al., 1999), and hence the absolute contribution of the Ross Sea Polynya to the sequestration of anthropogenic species must be relatively small as well. 2.5

East Antarctic Coastal Polynyas

Studies by Massom et al. (1998), Arrigo and Van Dijken (2003) and Barber and Massom (2007) using SSM/I satellite data have highlighted East Antarctica (20–160◦ E) as a region with many coastal polynyas. Most of them are relatively small as compared to the Ross Sea and Ronne Ice Shelf polynyas. The local bathymetry appears to be the major factor for their occurrence (Massom et al., 1998). As the water flow and sea-ice drift along the coast is westward, any protruding features like north-south headlands, glacier tongues, floating ice shelves or grounded icebergs cause sea-ice reduction in the lee of it. Additionally, severe katabatic winds remove the pack ice from the coast. Some polynyas are semi-recurrent, i.e., they do not occur every year. These are generally the smallest polynyas. The largest polynyas in this region are the Cape Darnley (68–69◦ E), the Prydz Bay (78◦ E), the Davis Sea/Shackleton Ice Shelf (93–95◦ E) and the Mertz Glacier (144–145◦ E) polynyas. Knowledge about most of these polynyas has been acquired by remote sensing techniques, because wintertime access to the region is difficult due to hostile conditions (but see Williams and Bindoff, 2003). This means that our knowledge of the biogeochemical conditions in these polynyas is scanty. Arrigo and Van Dijken (2003), using SeaWiFS satellite data, report that the East Antarctic polynyas, with the exception of the Prydz Bay polynya, are less productive than the large polynyas in the Ross and Weddell Seas, and on an annual basis even less productive than average in the Southern Ocean south of 50◦ S—however, per unit area they are more productive. The reason for this is thought to be the (small) width of the continental shelves in East Antarctica—note that the Prydz Bay shelf is wider indeed. High productivity in the Prydz Bay polynya measured in situ was reported by Zilin et al. (2001). This was also found by Gibson and Trull (1999) who presented unique data covering a full annual cycle in the inner Prydz Bay and found the region to be a strong CO2 sink, the oceanic pCO2 never reaching supersaturation. Nutrient depletions were much higher than usually observed in Antarctic waters, and nitrate even reached values where it could be limiting to phytoplankton growth. In contrast, Sambrotto et al. (2003) reported nutrient consumptions in the Mertz polynya region which are of the same order of magnitude as those in other Antarctic regions. Generally pCO2 undersaturations were found in the Mertz area, the highest close to the coast, but definitely not to the same extent as in Prydz Bay. Ishii et al. (1998) presented DIC data along the East Antarctic coast for the austral summer, and observed distinct longitudinal DIC minima at the locations of wintertime polynyas, including the Lützow-Holm Bay, Casey Bay, Prydz Bay and West Ice Shelf polynyas. In keeping with the Gibson and Trull (1999) data, the largest CO2 drawdown was observed in Prydz Bay. The few in situ data do not appear contradictory to the satellite data (Arrigo and Van Dijken, 2003). Sambrotto et al. (2003) presented a good correlation between the SeaWiFS and in situ chlorophyll for the Mertz region indeed. The few CO2 data for the polynya regions point to pCO2 undersaturation and thus uptake of atmospheric CO2 , largely caused by CO2 drawdown by phytoplankton during the spring and summer period. The data of Gibson and Trull (1999), albeit only of regional nature, suggest that also in winter the polynyas be CO2 sinks, or only slight sources at most. Prydz Bay may be exceptional (see above), but we believe that similar processes occur in


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other polynyas but only at a somewhat lower level. We think that the coastal polynyas reinforce the CO2 uptake of the waters south of the Polar Front. We base this on the valuable data of Gibson and Trull (1999); polynyas offer longer ice-free conditions, which allow a longer equilibration of undersaturated waters with the atmosphere and thus elevated CO2 uptake. Moreover, they lengthen the productive season, which leads to elevated CO2 drawdown. Some East Antarctic polynyas play a role in the formation of dense bottom water. Therefore, they are instrumental in sequestering anthropogenic CO2 and CFCs in the deep ocean. Rintoul (1998) underscored the importance of this bottom-water, called Adélie Land Bottom Water, as it contributes about 25% to the total Antarctic Bottom Water volume. It is certain that the Mertz polynya and the nearby Adélie Depression are necessary for producing dense shelf water through brine rejection during sea-ice formation (Gordon and Tchernia, 1972; Fukamachi et al., 2000) which constitutes a vital ingredient of bottom-water. An additional factor are intrusions of saline deep-water onto the shelf (Rintoul, 1998), which by the way may also contribute to keeping the polynya open (sensible heat)—but latent heat is still more important for the Mertz polynya (Williams and Bindoff, 2003). Vaillancourt et al. (2003) observed (remnants of) mesoscale deep convective features reaching up to 1400 m depth near the boundary of the polynya. Also within other East Antarctic polynyas dense shelf water is formed and thus multiple sources of deep- and bottom-water off East Antarctica are possible (Gordon and Tchernia, 1972). In fact, Bindoff et al. (2000) document deep-water formation near 104◦ E. However, the Mertz region is likely to be the main source (Rintoul, 1998). In the Antarctic Bottom Water of the Indian sector of the Southern Ocean, which at least partly derives from the East Antarctic coast (Rintoul, 1998), significant storage of anthropogenic CO2 was found (Sabine et al. 1999, 2002; McNeil et al., 2001). Additionally, the CFC concentrations of Antarctic Bottom Water in this region are among the highest of the Antarctic (Orsi et al., 1999). In contrast, the Antarctic Bottom Water in the Weddell Sea region was found to be relatively poor in anthropogenic CO2 (Poisson and Chen, 1987; Hoppema et al., 2001b). This indicates that over the East Antarctic shelves, the equilibration of surface waters with the atmosphere must occur to a larger degree as compared to other regions, allowing more anthropogenic CO2 to enter the nascent bottom-water. This is in agreement with the significantly higher saturation of CFCs (but still below 100%) in the High Salinity Shelf Water of the Mertz region than that of the other bottom water formation regions (Orsi et al., 2002). This may well be related to the high persistence of open water in the Mertz polynya (Arrigo and Van Dijken, 2003) even in winter. More to the west, a significant singular source of dense bottom water is found in Prydz Bay (Nunes Vaz and Lennon, 1996; Wong et al., 1998; Orsi et al., 1999). This probably stems from saline shelf water of the Prydz Bay continental shelf (Wong et al., 1998). It is evident that the Prydz Bay polynya plays a prominent role in the salinization of this dense shelf water. After leaving the bay, the Prydz Bay Bottom Water flows westwards (Orsi et al., 1999) within the coastal current due to the Coriolis force, towards the Weddell Sea where it interleaves with the local Weddell Sea Deep Water at about 3500 m depth. Its ventilation strength is estimated to be of the same order of magnitude as that of the local Weddell Sea ventilation by means of bottom-water formation (Hoppema et al., 2001a), namely 2.7 ± 0.9 Sv (a recent analysis suggests that the more likely figure should be near the lower boundary of this estimate; O. Klatt, 2004, personal communication). This is a significant contribution to the total ventilation by the Southern Ocean, which amounts to 14 Sv (Orsi et al., 2002). Since we do not know the concentration of anthropogenic CO2 in the shelf water source of Prydz Bay Bottom Water, it is impossible to estimate its share to CO2 sequestration. However, we

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expect that this ventilated deep water with Prydz Bay origin is a more than proportional contributor to the sequestration of anthropogenic CO2 . This we base on the saturation of CFCs on the Prydz Bay shelf, which is relatively high as compared to that at other bottom water production sites (Orsi et al., 2002), thereby presuming some kind of positive correlation between the air–sea equilibration of CFCs and anthropogenic CO2 . In the end, this is likely to be caused by the relatively large winter surface area of the polynya (Arrigo and Van Dijken, 2003). 2.6

Summary and Concluding Remarks for Antarctic Polynyas

All around the Antarctic continent, coastal polynyas occur. Generally, these are characterized by high productivity with accordingly high drawdown of carbon and nutrients. The Ross Sea Polynya is even assigned hyperproductive (Smith and Gordon, 1997). Elevated levels of iron over the continental shelves (De Baar and De Jong, 2001) and prolonged ice-free conditions (i.e., more light availability for photosynthesis) are pivotal factors in this. They are considered to be sinks for CO2 , also for its anthropogenic portion, and CFCs. In some polynyas also processes take place which precondition the shelf waters for deep and bottom water formation. As the dense shelf water, produced through cooling and addition of brine from ice formation in the polynya, is incorporated in the nascent deep and bottom waters, it carries with it contaminations in the form of anthropogenic CO2 and CFCs. Obviously, since uptake of anthropogenic species is impeded by ice cover, polynyas tend to relieve this impediment. Another factor that may be important for the uptake of anthropogenic CO2 is, somewhat counter intuitively, upwelling and intrusions of CO2 -charged deep water onto the shelf. Because of the ever increasing level of CO2 in the atmosphere due to fossil-fuel burning, the outgassing of CO2 in the polynya is strongly diminished due to the reduced pCO2 difference between ocean and atmosphere. In fact, this is equivalent to uptake of anthropogenic CO2 . This may be a more efficient way for anthropogenic CO2 to enter the shelf water than through slow air–sea exchange after cooling of the shelf water. Thus, polynyas with upwelling of CO2 -rich warm deep water may be particularly efficient in the uptake of anthropogenic CO2 . Stated differently, the larger the sensible-heat component of the polynya, probably the more efficient it is for uptake of anthropogenic CO2 . Open-ocean polynyas are relatively uncommon. Oceanographic factors involved may be convective overturning or entrainment of warm subsurface water. Their biogeochemical role is largely unknown, because in the last 30 years only transient occurrences have been documented. If we assume that non-linear features in the θ-property relationships for the Weddell Gyre are a consequence of convective processes in open-ocean polynyas, we must conclude that their contribution to deep ventilation and uptake of anthropogenic species is minor. There is one exception to this, namely, the large Weddell Polynya in the 1970s. Although no contemporaneous CO2 measurements exist, we estimated, based on an analysis by Gordon (1982), the uptake of anthropogenic CO2 through convective processes in the polynya to be in the order of 1013 g C. This is significant as compared to the CO2 sequestration in the abyssal oceans. The Weddell Polynya caused larger changes in the spatial distributions of biologically-mediated properties and biogeochemical processes. In particular, subsurface water enriched in biologically-mediated CO2 was transferred into the surface layer. However, enhanced biological activity is likely to have reduced this higher level of CO2 in the surface layer. In the end this resulted only in a relocation of properties within the Weddell Gyre.


3

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Polynyas in the Arctic Ocean

Polynyas are found more or less all around the Arctic Ocean (see Barber and Massom, 2007, for locations), at least during some time of the year. In winter many polynyas are formed by the wind, which blows the sea ice away, while land or land-fast ice prevents new sea ice to drift into the area. Examples of the former type are the Storfjorden and St. Lawrence Island polynyas (SLIP), and of the latter type the Laptev Sea polynya. Often polynyas open up during winter along the Alaskan North coast (e.g., Winsor and Chapman, 2002) and also over the Siberian shelf (e.g., Dokken et al., 2002), when strong wind blows off the coast. Most of these polynyas disappear in the summer, simply for the reason that the area is ice-free during this season. Except for the SLIP, these polynyas are not present during the biologically productive times, even if biogeochemical processes during the spring–summer season condition the waters that are present during the polynya period. These polynyas are efficient sea-ice factories and thus also produce high-salinity, brine-enriched waters, which are important in ventilating the deeper layers of the surrounding seas. In the coupled system of summer productivity and winter remineralization, these high-salinity waters are also a route for transporting chemical constituents. In the Arctic Ocean shelf-slope plumes are an intimate part of the processes that determine the properties of the mid-depth and deep waters of the Arctic Ocean, including their chemical constituents (e.g., Rudels et al., 1994; Jones et al., 1995; Schauer et al., 1997; Anderson et al., 1999). Polynyas that open in the spring and that are present at least partly into the summer season are the Northeast Water polynya (NEW) (northeast coast of Greenland), the North Water polynya (NOW) (between Ellesmere Island and Greenland) and the Cape Bathurst polynya (Mackenzie Shelf). All of these polynyas are biologically productive regions, with the NOW being exceptionally productive. Normally these polynyas are not present during winter. They open up before the surrounding waters early in the spring through the specific flow of waters that take away the sea ice, while ice barriers prevent new ice from upstream to enter the area. Consequently, these polynyas are not such efficient sea-ice factories, but through biological processes they are important in transforming the chemical signature of the waters present in them. Some biogeochemical aspects of these polynyas can also be found in Miller and DiTullio (2007). 3.1

Storfjorden Polynya

Storfjorden is situated in the eastern Barents Sea, between the islands of Spitsbergen, Barentsøya and Edgeøya in the Svalbard Archipelago. It is about 160 km long, with a sill depth of 120 m and a maximum depth of about 180 m. Water of Atlantic origin enters the fjord from the Norwegian Sea, but the characteristics of the water in Storfjorden are significantly modified by sea-ice formation in winter. Over the shallow areas along the eastern part of the fjord, offshore winds transport the ice out of the fjord, thereby forming a polynya that remains ice-free until late winter, thus enhancing ice production (Haarpaintner et al., 2001a). Most of the ice formed in the winter season is exported, but some remains, melts in the summer and (together with meltwater from the Barents Sea) contributes to a lower salinity of the surface water in Storfjorden (e.g., Haarpaintner et al., 2001b). The Storfjorden polynya is not of any quantitative importance with regard to biogeochemical transformation or transport of chemical constituents, but it is a very suitable laboratory where especially the winter processes can be studied under relatively accessible conditions.

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Ice formation in winter produces brine, resulting in dense water that flows off the fjord shelves to fill the deeper regions of the fjord (Haarpaintner et al., 2001a). This flow carries with it near-surface constituents, including gases that enter the surface water from the atmosphere. Furthermore, the high-salinity bottom water produced in winter is enriched in chemical constituents from organic matter decaying at the sediment surface. Anderson et al. (1988) evaluated the chemical modification by the decay of organic matter in the highsalinity bottom water of Storfjorden from data collected in summer 1986. The regeneration rates of phosphate, O2 and total dissolved inorganic carbon (DIC) followed the classical P : O2 : C ratios 1 : −135 : 106 of Redfield et al. (1963), with the rate during the winter of 1986/87 corresponding to 3.7 mmol C m−2 d−1 over a six-month period, or about 8 g C m−2 yr−1 . Provided no build-up of organic matter occurs in the fjord, this would also represent the export production. The dense water formed in the deeper layers of the fjord will eventually overflow the sill and subsequently sink down into the deeper regions of Fram Strait (Quadfasel et al., 1988; Schauer and Fahrbach, 1999) and transport with it the decay products. The volume of cold, brine-enriched water found in 1987 (produced during the winter season of 1986/87) was 385 × 109 m3 , while its mean salinity was 35.25. For producing this excess salt, a mean sea-ice production of 1.5 m over the whole fjord is needed (Anderson et al., 1988). The mean DIC excess in the cold, brine-enriched water was 12 µmol kg−1 (Anderson et al., 1988), resulting in a total transport of 55 × 109 g C into the deeper waters of Fram Strait (if all bottom water would exit Storfjorden). In the spring of 2002 exceptionally high salinity waters were found in Storfjorden (Rudels et al., 2005), with elevated DIC concentration but without corresponding signals in the nutrient and O2 concentrations (Anderson et al., 2004). As the surface water pCO2 was well below atmospheric levels (286 µatm relative to 376 µatm), the excess DIC was attributed to uptake from the atmosphere. Anderson et al. (2004) suggested that sea-ice formation enhances the air–sea exchange of CO2 , and hypothesizes it to be a result of an efficient exchange across the surface film during the ice-crystal formation, increased solubility in the low-temperature brine, and the rapid transport of the brine-enriched, high-salinity water to deeper waters. 3.2

Northeast Water Polynya

The Northeast Water polynya (NEW) is formed from the combined effects of a fast-ice barrier (the Norske Øer Ice Shelf), that extends from the Greenland coast to bridge a trough system, and a northward flowing coastal current (e.g., Böhm et al., 1997). The NEW opens up in April/May and increases in size throughout the summer, is reduced in size at the end of summer and is normally closed by the end of September. It was extensively studied in the early 1990s in a multidisciplinary international program. The nutrient and O2 distributions reveal that primary productivity had already started before the polynya opens in spring and it continues all through the summer. New nutrients are continuously supplied by the northward flowing coastal current underneath the ice barrier south of the polynya (e.g., Wallace et al., 1995), which supports primary production throughout the productive season. The nutrient concentrations decrease along the flow path within the polynya, and nitrate concentrations often drop below the detection limit in the downstream area of the polynya, while phosphate still is found at about 0.6 µM (Wallace et al., 1995). This is caused by the low nitrate concentration (approximately 4 µM) relative to that of phosphate (approximately 1.1 µM) in the surface mixed-layer of the East Greenland Shelf


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Water (Kattner and Budéus, 1997), which is the source of the surface layer of the NEW. The low nitrate concentration relative to that of phosphate points to a Pacific origin (Wallace et al., 1995; Jones et al., 1998). Primary production also depletes DIC in the summer surface waters by up to 2 mol C m−2 (for 1992; Yager et al., 1995). The associated decrease in pCO2 (average: 218 ± 34 µatm) can be compensated for by a flux of CO2 from the atmosphere if a stormy period of a few weeks occurs before the freeze-up in September. The authors hypothesize a rectification scenario: The decrease in pCO2 caused by primary production induces a CO2 flux from the atmosphere before the polynya is covered by sea ice in the fall, while the subsequent sea-ice cover prevents outgassing of the CO2 that is produced by organic matter decay in the fall and the winter. Extensive blooms of ice algae will decrease pCO2 before the sea-ice cover melts in spring and the resulting melt water creates a thin, highly stratified surface water that initially hampers air–sea exchange (Yager et al., 1995). The rectification hypothesis may be amplified by off-shelf flow of high-pCO2 water. Dissolved organic carbon concentrations observed within the NEW surface water exceed those of the surrounding surface waters (110 µM versus 96 µM), which is attributed to melting sea ice and ice algae (Skoog et al., 2001). These authors also found that the dissolved organic carbon concentrations in polynya surface water decreased from 110 to 105 µM as the productive season progressed, while dissolved organic nitrogen concentrations increased from 5.6 to 6.1 µM. This resulted in a decrease in the C : N ratios of dissolved organic matter from spring to summer from 20 to 17. Furthermore, they found a significant decrease in the dissolved organic matter C : N ratio in all water masses within the polynya area as the productive season progressed. Observations of non-Redfield behavior have also been reported by Daly et al. (1999) in the dissolved and particulate pools as well as in the rates of transformation among them. 3.3

North Water Polynya

The North Water polynya (NOW) is located between Ellesmere Island and Greenland and opens up in spring by dominating southward wind and currents. Sea ice is prevented from drifting into the polynya region from the north by an ‘ice bridge’ of conglomerate thick sea ice formed in Nares Strait (e.g., Ingram et al., 2002). NOW is one of the largest polynyas in the Arctic and also one of the most biologically productive ones. It was extensively studied from 1997 to 1999 within the International North Water Polynya Study. Water from the Arctic enters the NOW region from the north through Nares Strait, and from the south a branch of the West Greenland Current supplies water (e.g., Bâcle et al., 2002). These two water sources with different chemical and physical characteristics affect the biogeochemical conditions of the polynya (e.g., Mei et al., 2002). The water from the Arctic has high silicate (and phosphate) concentrations and is referred to as silicate-rich Arctic Water (SRAW) relative to that entering from Baffin Bay (referred to as Baffin Bay Water; BBW) (Tremblay et al., 2002a). In the surface, SRAW dominates in the northwest, while BBW dominates in the southeast, consistent with the current regime (Ingram et al., 2002). The surface mixed-layer (SML) depth starts to get shallow (less than 20 m) in April in the BBW, but in the SRAW not until June (Tremblay et al., 2002a). Hence, depletion of nutrients begins in late April and late May in the BBW and SRAW regimes, respectively (Mei et al., 2002; Tremblay et al., 2002a). The nitrate concentration in April is about the same (about 10 µM) all over the polynya and is more or less depleted due to primary production by May in the BBW and in July

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Figure 5: Mean concentrations of DIC, normalized to S = 33 (filled symbols) and nitrate (open symbols) in the surface mixed-layer of SRAW (stars) and BBW (circles) in 1998. Note that the SRAW nitrate values have been offset by +15 µM and the two scales are set to a C : N ratio of 106 : 16. DIC data from Miller et al. (2002) and nitrate from Tremblay et al. (2002b). in the SRAW (Tremblay et al., 2002b). Nutrients are added to the surface water during this period by horizontal advection and vertical diffusion. Primary production consumes CO2 and it has been shown that the surface water pCO2 in the NOW decreases from oversaturation (approximately 450 µatm) in April to below 200 µatm in June (Miller et al., 2002). The observed DIC concentration (normalized to a salinity of 33) decreases from April to July (in 1998), but not as expected from the nitrate change considering the canonical C : N ratio of 106 : 16—this holds especially for the BBW (Figure 5). There are several possible reasons for the non-Redfield C : N change in the BBW. One likely reason is wind-driven vertical replenishment of nitrate over half of the new production during the first 7 weeks of production (Tremblay et al., 2002b), which has a larger relative impact on the nitrate supply than on that of DIC. Advective supply from the south can have a similar effect. Another cause that could explain the rapid decrease of DIC in May–June is that plankton utilizes remineralized nitrogen when the nitrate concentration gets low, while still taking up new CO2 if the carbon still remains in the organic form. The increase in DIC from June to July is likely a result of a flux of CO2 from the atmosphere into the surface water driven by the strong undersaturation during this time (Miller et al., 2002). The total decrease in DIC and nitrate in the SRAW is close to the canonical C : N ratio, but the May and June data deviate somewhat (Figure 5). The latter can be due to the method how the mean concentration was computed and/or to advective input from the Arctic coupled to biochemical processes upstream. Within the upper 150 m of the BBW, the new production was computed from the disappearance rates of nitrate and DIC during the bloom period (24 April to 18 June) as 15.9 ± 2.1 mmol N m−2 d−1 (equivalent to 114 ± 16 mmol C m−2 d−1 when applying the observed Particulate Organic Carbon (POC) : Particulate Organic Nitrogen (PON) ratio of 7.17 ± 0.15) and 105 ± 16 mmol C m−2 d−1 , respectively (Tremblay et al., 2002b). About 80% of this consumed nitrate and DIC was found in the particulate matter in the top 150 m during the bloom, while the rest supposedly had been exported to deeper layers. The total


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consumption of DIC during the bloom period (55 days) equals 6 mol C m−2 or 72 g C m−2 of which at least 14 g C m−2 is lost to the sediments during this time. Computations of Miller et al. (2002) for the BBW (eastern part of polynya area) during the same time period show that 2.35 mol C m−2 or 28 g C m−2 is taken up from the atmosphere. It is likely that a large fraction of the POC that is found in the top 150 m during the bloom period also is lost from the surface water, either by sedimentation or by advection, before it decays, making this region a very efficient carbon sink. The high productivity of the NOW results in a marked increase of nutrient concentrations towards the bottom (below 450 m), most pronounced for silicate and in the southern part of the polynya (Michel et al., 2002; Tremblay et al., 2002b). This silicate enrichment is driven by dissolution of biogenic silica as a result of diatom production in the surface and subsequent sedimentation. The high productivity in the NOW causes a transport of nutrients from surface layer to depth, where it influences the local and downstream nutrient signatures, very significantly for silicate (Tremblay et al., 2002b). Highly productive regions also produce sulfur gases, the most important being dimethylsulfide (DMS) which is a component of a biogeochemical cycle including dimethylsulfoniopropionate (DMSP) and dimethylsulfoxide (DMSO) (e.g., Bouillon et al., 2002). The volatile DMS, while highly supersaturated, is lost to the atmosphere where it may form cloud condensation nuclei, which are important in regulating the radiative balance of the atmosphere. Surprisingly low concentrations of DMS were detected in the upper water column (0–25 m) of the NOW in the spring of 1998, with mean values starting at 0.17 nmol l−1 in April, increasing to 0.65 and 1.08 nmol l−1 in May and June, respectively (Bouillon et al., 2002). This is significantly lower than the 12 nmol l−1 which was found in August 1991 in the open water west of Svalbard and lower than the average 3.0 ± 0.6 nmol l−1 observed in the iceedge zone (Leck and Persson, 1996). Also significantly higher DMS concentrations (mean 11.1 nmol l−1 ) have been reported from the Barents Sea (Matrai and Vernet, 1997). It is not possible from this single investigation to reject polynyas as important DMS production regions (see also Miller and DiTullio, 2007), and certainly more work is needed to determine the dynamics of DMS production in these areas. 3.4

Cape Bathurst Polynya

The Cape Bathurst polynya is formed in the outer Amundsen Gulf when easterly winds dominate the area at a time when open water or reduced ice-cover are present, mainly in the form of large fractures or leads along the coast. The pack ice of the central Arctic Ocean is the offshore boundary of the polynya. Observations made using satellite imagery have shown that the Cape Bathurst polynya varies considerably on an interannual basis with regard to seaice retreat and formation (Arrigo and Van Dijken, 2004). During the five years (1998–2002) these authors studied the polynya, it typically opened rapidly in June and began to freeze in October, except for 1998 when the opening started two months earlier and the freezing one month later, making this a year with an exceptionally long period of open water. The variable ice conditions also result in variable primary production, both in its timing and strength as evaluated from SeaWiFS satellite data (Arrigo and Van Dijken, 2004). It varied by a factor two over the five years of study, from 90 g C m−2 yr−1 in 2000 to 175 g C m−2 yr−1 in 1998. This makes the Cape Bathurst polynya about as productive as the NOW. The Cape Bathurst polynya was not much studied before the year 2000, when the Canadian Arctic Shelf Exchange Study (CASES) started. When the results of this study become available, a greater understanding of the biogeochemical processes in the Cape Bathurst polynya will be possible.

214 3.5

M. Hoppema and L.G. Anderson St. Lawrence Island Polynya

The St. Lawrence Island polynya (SLIP) is one of several polynyas on the shallow continental shelf of the Bering Sea, which are all formed in the winter south of all major islands and peninsulas, as prevailing northerly winds force sea ice away from the sheltering landmasses. SLIP is a winter polynya, as open water normally exists in the Bering Sea from May to November. However, because light conditions are advantageous before May within the polynya, the pelagic production is favored here. Furthermore, the very shallow bottom depth (30–70 m) results in an extensive supply of organic matter to the sediment surface, which supports a rich macrobenthic community (e.g., Grebmeier and Cooper, 1995). This rich benthic community is the basis for a large population of marine mammals and birds. The production of sea ice in the polynya results in brine-enriched bottom water that is enriched in nutrients due to mineralization of organic matter at the sediment surface, as was observed in April 1999 and March 2001 (Clement et al., 2004). Nevertheless, the SLIP does not have any essential impact on the biogeochemistry of the Bering Sea water masses, as it only constitutes a small part of a high-productive region, even though it plays an important role for the ecosystem of the region. 3.6

Laptev Sea Polynya

Within the Laptev Sea a flaw lead exists in winter along the fast-ice boundary being up to more than 2000 kilometres (km) long and more than 10 km wide (Zakharov, 1966). The flaw lead is maintained and controlled by the wind regime and in early winter it lies close to the coast at 5–10 m water depth, but during the progression of winter it shifts over 500 km offshore to a bottom depth of 20–30 m (Dethleff, 1995). Within this polynya, extensive sea ice is produced which entrains sediments by convectively induced resuspension, because of the shallow bottom depth. The majority of the suspended matter is of terrigenous origin but also marine organic matter is produced within the Laptev Sea. Most of the sea ice produced within the Laptev Sea is exported to the deep Arctic Ocean within the Eurasian branch of the transpolar drift. In fact, about 60% of the sea ice exported from Arctic shelf seas originates in the Laptev Sea, bringing with it in the order of 10 Tg of suspended particulate matter (SPM) per year, which is more than 70% of the SPM exported with sea ice from Arctic shelf seas (Eiken, 2004). Consequently, processes in these flaw leads are important in transporting SPM from the shelves (much of terrigenous origin) out into the deep central Arctic Ocean. The flaw lead region is also reflected in the composition of benthic species, such as Ostracods (Stepanova et al., 2003). It likely also has an effect on the biogeochemical signature of the bottom waters of the region, even if a distinct signal has not been reported yet. Furthermore, the brine released during sea-ice production could be a conduit for transport of chemical constituents from the shallow shelves to intermediate waters of the Arctic Ocean. 3.7

Comparison of the Different Arctic Polynyas

Flaw leads are present in the lee of land around most of the Arctic Ocean during the freezing season, being important sea-ice factories. However, as these “polynyas” disappear into open water in the spring, they mainly have an indirect affect on the biogeochemistry of Arctic water masses. From a biogeochemical viewpoint, the North Water polynya seems to be the most important, followed by the Cape Bathurst polynya and the Northeast Water polynya. New findings in future investigations, however, might change this view.


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There is no question that several Arctic polynyas are important sink regions for atmospheric CO2 , but their relation to the anthropogenic issue is more difficult to accurately assess. The increase of atmospheric pCO2 will strengthen the oceanic uptake by increasing the difference in partial pressure between the atmosphere and sea surface. This is valid for all oceanic regions, but in polynyas additional aspects have to be considered. These are mainly coupled to the magnitude of sea-ice production, which likely will change (and already might have changed) in a climate warming scenario. Possible feedbacks to this are: • More polynya area results in more sea-ice production with resulting brine volume and an increase in transport into deeper water masses; • A larger polynya area in the spring could enhance primary production; • Changes in the supply of nutrients, either vertically or horizontally, affect primary production; and • The ice barriers that maintain some polynyas, e.g. the NEW and NOW, might “collapse”, thus decreasing their importance. There are several other possible feedback processes of variable importance, but the above are likely the most relevant. However, without better knowledge of the sensitivity of the individual processes to the forcing, it is not possible to make any quantitative assessments. Polynyas are regions of tight coupling between physical and biogeochemical processes that have important impact on several levels. Sea-ice production is the starting link of ventilation of subsurface waters with transport of chemical constituents by them. The highproductivity polynyas are the basis of a rich ecosystem supporting most trophic levels up to birds, seals and polar bears (e.g., Stirling, 1997). This rich ecosystem results in a transformation of chemical constituents and flux of particulate matter to subsurface layers. Consequently, polynyas effect the biogeochemical environment in several different ways.

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Chapter 7

Physical Control of Primary Productivity in Arctic and Antarctic Polynyas K.R. Arrigo1 1 Department of Geophysics, Stanford University, Stanford, California, 94305-2215, USA

Abstract The unique physical characteristics of Arctic and Antarctic polynyas often make them highly productive marine environments. Here I focus on four major polynyas (the NEW and NOW polynyas in the Arctic and the Ross Sea and Mertz Glacier polynyas in the Antarctic) and compare and contrast the major physicochemical features that control rates of phytoplankton growth and primary production in each. Included in this analysis are the effects of temperature, solar radiation (including ultraviolet), and nutrient delivery. Also discussed is the positive feedback that exists between cloud cover and polynya size as well as the importance of the timing of polynya formation on ecosystem structure.

1 Introduction Rates of phytoplankton growth and primary production in the upper ocean are controlled by ambient temperature and the availability of light and nutrients. These physicochemical aspects of the marine environment are determined by both physical and biological processes. Physical processes controlling the temperature of surface waters include transmission of incident shortwave radiation, emitted long wave radiation, the flux of latent heat due to freezing or melting of sea ice, and the horizontal and vertical movement of sensible heat. However, the amount of shortwave radiation (e.g. light) absorbed by the water column and the ice can be modified by the presence of high concentrations of biogenic material, including algal cells and their constituent pigments. Similarly, nutrient concentrations in surface waters are replenished by the physical processes of convective- and wind-driven mixing with nutrientrich waters from below or by exchange with the atmosphere, via aeolian deposition. Nutrient concentrations are modified, however by the biological processes of nutrient assimilation, nitrogen-fixation, denitrification, and organic matter remineralization. Nowhere is the importance of these physical and biological processes more obvious, or their interactions more complex, than in the polynyas that form within the seasonal ice pack in the polar regions of both the Arctic and the Antarctic. Because of the unique physical attributes of polynyas, they represent some of the most biologically productive marine Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74007-7

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ecosystems on the planet (Smith, 1995; Smith and Gordon, 1997; Tremblay et al., 2002a; Arrigo and van Dijken, 2004). While the dynamics of the sea ice play a critical role in structuring the polynya ecosystem and controlling its rate of biological productivity, other factors, such as ocean circulation patterns and meteorological conditions, play important roles as well. The objective here is to evaluate how physical forcing mechanisms that impact the dynamics of polynya formation and expansion also help determine the degree of biological productivity within waters associated with the polynya. This will be achieved by focusing on four of the most well-studied and biologically significant polynyas from both the Arctic and the Antarctic regions. The major Arctic polynyas to be focused on here include the Northeast Water polynya (NEW) and the North Water polynya (NOW). Analysis of Antarctic polynyas will be based on recent research on the Ross Sea polynya (RSP) and the Mertz Glacier polynya (MGP).

2 Formation of the Four Major Polynyas As described in previous chapters, polynyas can be described as being either latent heat polynyas, sensible heat polynyas, or some combination of both. Because the mode of formation determines the physicochemical characteristics of the polynya, which in turn will impact rates of biological activity, it is important to understand how each polynya forms. One generalization that can be made concerning the four polynyas that will be discussed here is that they are all considered to be either latent-heat polynyas or have characteristics of both latent heat and sensible-heat polynyas. None of the four is strictly a sensible heat polynya, which are actually quite rare and have not been that well studied. 2.1

The NEW polynya (Arctic)

The NEW polynya, located along the northeastern coast of Greenland (Figure 1A), remains open from approximately the beginning of May to the end of September (Bohm et al., 1997). Its formation is due to the presence of several ice shelf barriers that extend perpendicular

Figure 1: Location and approximate size of the major (A) Arctic and (B) Antarctic polynyas discussed in the text. NOW = North Water polynya, NEW = Northeast Water polynya, RSP = Ross Sea polynya, and MGP = Mertz Glacier polynya.

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to the prevailing currents and winds and cause a divergence of the sea ice field in their lee. In summer, winds are weak and the Norske Oer Shelf Ice restricts the motion of sea ice associated with the northward flowing coastal current, representing the dominant forcing of the southern part of the NEW polynya. During this season, the polynya gradually increases in size towards the north since the air–sea heat budget is positive and no new ice-formation takes place (Schneider and Budeus, 1995, 1997). Melting of glacial ice and sea ice induces vertical stability in certain parts of the polynya, giving rise to enhanced primary production (Schneider and Budeus, 1995). 2.2

The NOW Polynya (Arctic)

The NOW polynya, located along the northwest corner of Greenland (Figure 1A), is formed between November and March as a result of the annual appearance of an ice bridge in Smith Sound, along with a southward flowing surface current (Barber et al., 2001). When ice jams stop the inflow of ice from the north, the continued drift of ice southward below the blockage creates a large polynya without melting ice by sensible-heat input (Ingram et al., 2002). However, upwelling near the Greenland coast can bring relatively warm water to the base of the turbulent surface layer where it is entrained via convection driven by brine drainage from the ice. The resulting flux of sensible heat supplies about one-third of the heat loss at the surface of the NOW polynya and slows further sea ice growth (Melling et al., 2001; Mundy and Barber, 2001). The formation and duration of the initial ice bridge is highly variable and appears to have formed later and broke up earlier in the 1990s than in the 1980s. The average sea-ice formation and decay dates closely follow the mean temperature spatial pattern, illustrating a strong atmosphere-surface coupling (Barber et al., 2001). 2.3

The RSP Polynya (Antarctic)

Despite the presence of strong winds throughout the winter months, sea ice concentrations in the RSP (located north of the Ross Ice Shelf in the southwestern Ross Sea, Figure 1B) remain generally high, although small areas of open water occasionally form near the ice shelf (Gloersen et al., 1992). This is because cold winter sea ice has a low brine volume and sufficiently high tensile strength to resist break-up by offshore katabatic winds. Only when temperatures rise and the brine volume of the sea ice increases and the sea ice begins to melt, can ice breakup and polynya expansion begin. The precise timing of breakup in the spring apparently is controlled in part by the thickness of the sea ice, which is a function of cumulative degree days (Maykut, 1986), with colder years exhibiting a delay in polynya formation. Arrigo et al. (1998) found that the timing of RSP formation was more strongly correlated with air temperatures in fall and winter than with the magnitude of the wind stress in spring, reflecting the importance of sea ice integrity in determining the timing of initial sea ice breakup. Upwelling of relatively warm upper circumpolar deep water (UCDW) has been proposed as a mechanism for weakening the integrity of the ice pack, facilitating early polynya expansion (Jacobs and Comiso, 1989; Dinniman et al., 2003; Arrigo and van Dijken, 2004). Soon after expansion, however, shortwave radiation increases the temperature of surface waters by as much as 3–4◦ C, accelerating the increase in the size of the RSP (Arrigo et al., 2000) and enhancing biological activity (Arrigo and McClain, 1994). 2.4

The MGP Polynya (Antarctic)

As described by Maslanik et al. (2001), the MGP, located near the coast of Adelie Land almost directly south of Tasmania (Figure 1B), is formed in the lee of the Mertz Glacier

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tongue, which extends northeastward from the Antarctic continent, and whose impact is enhanced by the close proximity of numerous small grounded icebergs and the large iceberg B-9B. As the coastal East Wind Drift current drives the flow of sea ice to the west, the Mertz Glacier tongue and nearby iceberg field forces the bulk of the ice pack to remain offshore, creating an inshore divergence in the moving sea ice field (Massom et al., 2001). This results in the formation of a semi-constant compact barrier of thick broken-out fast-ice and other large floes from the east that extends westwards from north of the Mertz Glacier terminus. This barrier reduces the flow of sea ice from within the polynya that would normally result from the intense northward flowing katabatic winds. An annual fast-ice promontory, which forms to the west of the Mertz Glacier, further narrows the outlet path for sea ice. As a result of this and high sea ice-production rates, the MGP is somewhat unusual in that it periodically fills in with ice, significantly reducing the open-water area until a synoptic storm event can clear the polynya region of its newly-formed ice.

3

Role of Physicochemical Properties of Polynyas

Because of their complex and varying modes of formation, polynyas can differ greatly with respect to their physicochemical characteristics, which can influence both the taxonomic composition of the phytoplankton population and their rates of growth and primary production. The relationship between phytoplankton taxonomic composition and aspects of the physical and chemical environment is not well understood in either Arctic or Antarctic polynyas. For example, although the NEW polynya exhibits distinct regional differences in the physical and chemical characteristics of the water column, few corresponding biological differences are discerned (Booth and Smith, 1997). In general, the relationship between phytoplankton community structure and physicochemical properties of polynyas are restricted to relatively gross taxonomic divisions. For instance, in the RSP, the largest phytoplankton blooms are dominated by the prymnesiophyte Phaeocystis antarctica and are located in the weakly stratified waters north of the Ross Ice Shelf (Arrigo et al., 1998; Goffart et al., 2000). This bloom develops early, probably beginning in late October or early November, at a time when sea ice is still abundant in the region. A second type of bloom is dominated by a number of different diatom species and develops later in the year, beginning in December and January after the sea ice has largely disappeared. Diatom blooms are much smaller but more numerous than the P. antarctica bloom, and are associated with more strongly stratified surface waters near the marginal ice zone (MIZ). There appears to be little spatial or temporal overlap between these two bloom types, suggesting that the difference in community composition does not represent ecological succession (Arrigo et al., 1998; Goffart et al., 2000). Similarly, data from the NOW polynya in the Arctic showed that two major ecological regions existed, each dominated by a different phytoplankton community. The eastern NOW polynya region is characterized by warm saline surface waters and is dominated by picophytoplankton. The northwestern region is colder and less saline and is dominated by nanophytoplanktonic diatoms (Mostajir et al., 2001). Although more detailed taxonomic information are not yet available, work is continuing in order to better understand the relationship between phytoplankton community structure and characteristics of the physicochemical environment of polynyas. This is particularly important in the face of the ongoing environmental changes that have been documented in both polar regions, as well as changes that are predicted for the near future. Anticipated changes in temperature, precipitation, nutrient inventories, and circulation patterns are expected to


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impact phytoplankton community structure to varying degrees (Nehring, 1998; Arrigo et al., 1999; Peperzak, 2003). How these changes will alter ocean biogeochemistry and pelagic food webs needs to be further addressed. Fortunately, physicochemical characteristics of the water column are much more easily related to patterns of phytoplankton growth and primary production than they are to phytoplankton community structure. This is because these relationships are more easily measured and quantified, and hence, much more data are currently available. In particular, sea surface temperature, the ambient light field (including both visible and UV wavelength ranges, as well as its modulation by ice cover, wind-mixing, and stratification via melting of sea ice and solar insolation), and nutrient concentrations (including macronutrients and trace metals) have received considerable attention in recent years, particularly with respect to their importance in controlling phytoplankton dynamics within Arctic and Antarctic polynyas. 3.1

Effect of Temperature

Temperature has been shown to modulate rates of growth and production in polar waters via its regulation of metabolic activity (Eppley, 1972). Many metabolic activities of polar microalgae, such as carbon fixation and nutrient assimilation, exhibit Q10 values in excess of two (Priscu et al., 1989), meaning that for every 10◦ C increase in temperature, metabolic activity increases by a factor of two (or more for higher values of Q10). This increase with temperature is most apparent when resources such as light and nutrients are in ample supply. In general, rates of production by psychrophilic (cold-loving) algae will increase with increasing temperature, until a threshold is reached, beyond which rates will level off or even decline (Figure 2). Almost all polar algae grow at ambient temperatures that are a few degrees below their optimum (Li, 1980; Palmisano et al., 1987; Arrigo and Sullivan, 1992) so that seasonal warming in the summer generally results in enhanced growth and production rates. This is not always the case, however, as microbial activity and substrate utilization in the NEW polynya exhibited various responses to short-term warming, responding to temperature significantly at just 50% of the stations sampled (Yager and Deming, 1999). Nevertheless,

Figure 2: Changes in growth rate with temperature measured for psychrophilic marine phytoplankton acclimated to a temperature of −1.1◦ C. Redrawn from data in Li (1985).

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processes that increase the temperature of surface waters in polynyas will generally result in higher rates of growth and primary production, at least until resource limitation sets in. There are two primary sources of heat to polynya surface waters, solar radiation and upwelling of warm, saline deeper waters. Solar radiation is probably the most reliable and effective mechanism for heating surface waters as the reduced sea ice cover and increasing solar elevation during spring and summer result in a rapid increase in shortwave radiant flux to surface waters of polynyas in both polar regions. The importance of surface temperature in controlling phytoplankton bloom dynamics was particularly well demonstrated during a study of the NOW polynya. Mei et al. (2002) showed that during April and May, high phytoplankton biomass was consistently greatest in the warm waters along the Greenland side of the NOW polynya. There, the phytoplankton bloom began two months earlier than in the cooler waters found along the Canadian side (Odate et al., 2002). Similarly, high phytoplankton biomass was observed again in September, associated with the warmer and saline water in the southeastern part of the study area (Odate et al., 2002). Both the sensible heat due to deep warm water entrainment into the mixed layer and the biological heating effect via phytoplankton light absorption appeared to contribute to the pattern of phytoplankton distribution in the NOW polynya (Mei et al., 2002). However, given the generally small change in sea surface temperature measured over a typical seasonal cycle, temperature is not likely to be as important as other factors, such as resource limitation, in controlling rates of primary production within polynyas. For example, a 5◦ C increase in sea surface temperature, a reasonable upper limit to the seasonal temperature change experienced by polynyas, would increase metabolic rate only by about 40%, assuming a Q10 of 2.0. Because light availability and nutrient concentration both have much wider dynamic range than temperature (both can vary by orders of magnitude), these resources have a much greater potential to influence rates of primary production in polar waters, particularly in highly productive polynyas. 3.2

Effect of Light and Nutrients

The productivity of polynyas in both Arctic and Antarctic regions is determined most strongly by a balance between the availability of light and nutrients. In general, light availability is a function of season, cloudiness, sea ice cover, and the intensity of surface stratification, often described in terms of the mixed layer depth. Stratification can result from a freshwater flux from melting sea ice and from solar heating of surface waters. Nutrients are supplied to the ice-free surface waters of polynyas either via advection of high nutrient water from less productive regions adjacent to the polynya, from upwelling of nutrient rich waters from below the pycnocline, or via aeolian deposition. The importance of these various processes in supplying vital resources to the phytoplankton community differs with the specific physical characteristics of a particular polynya. In the NEW polynya, surface waters are characterized by their low nitrate and high silicate concentrations, being derived from East Greenland Shelf Water (Kattner and Budeus, 1997). The anti-cyclonic surface circulation in the NEW polynya follows the topography of the trough system and continuously supplies nutrients to the surface throughout the year. The northern boundary of this tongue of relatively nutrient-rich water is controlled by the uptake of nutrients by phytoplankton during the summer months when light levels are high (Kattner and Budeus, 1997). Ultimately, nutrients are exhausted, and the phytoplankton bloom begins to decline in intensity. However, a second, albeit short-lived, phytoplankton bloom has been observed to form during autumn in the NEW polynya, likely the result of vertical diffusion of


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nitrate associated with increased wind mixing (Touratier et al., 2000). Therefore, processes that can replenish nutrients to surface waters while light is still in ample supply can result in enhanced rates of annual production. That is not to say that light is not an important factor governing primary production in the NEW polynya. A simple non-linear relationship was found to exist among the attenuation of solar radiation by sea ice, the percentage of open water, sea ice thickness, and phytoplankton primary production (Smith, 1995), suggesting that light indeed plays an important role there. In fact, the NEW polynya is characterized by the presence of multiple ecological regimes, defined primarily by the degree of stratification, which controls the ambient radiation field. A heavily ice-covered regime (more than 50% ice cover) is characterized by low levels of phytoplankton biomass and primary production that is dominated by small size classes. In the open water regime, surface stratification is strong, nutrients are abundant, and biomass and production are high and dominated by large phytoplankton. In the mixed ice regime, where the surface layer is strongly influenced by melt water and the pycnocline is relatively deep, production is intermediate and partitioned between small and large phytoplankton size classes (Pesant et al., 1996). Clearly, both irradiance and nutrient fields exert strong influences on phytoplankton productivity and ultimately result in a mosaic of biomass within the polynya (Smith, 1995). Nutrients may play a larger role within the NOW polynya, particularly during late spring when low-salinity Arctic water enters northern Smith Sound and mixes with Baffin Bay water. The Arctic water originates from the Bering Sea and contains high concentrations of phosphate and silicate. Baffin Bay water dominates in the southeast NOW region and is associated with relatively shallow upper mixed layers and weak horizontal advection. Phytoplankton depletion of macronutrients in Baffin Bay water begins in April and continues until nitrate is exhausted from the upper mixed layer in early June (Tremblay et al., 2002b). Primary production then shifts to recycled nitrogen sources as the community composition moves to one dominated by dinoflagellates and ciliates (Lovejoy et al., 2002). Over half of the new production during this period can be attributed to wind-driven replenishment of nitrate in the euphotic zone (Tremblay et al., 2002a). The NOW polynya appears to act as a silicate trap in which the biota differentially transports surface silicate to depth, as well as playing a key role in reducing biogenic silica dissolution, thereby influencing local and downstream nutrient signatures (Michel et al., 2002). Collectively, the results from NOW imply that the timing and magnitude of blooms are controlled by a succession of oceanic and meteorological forcing, including early advection of nutrient-rich surface waters into the polynya and later wind-driven upwelling of high nitrate waters from below the thermocline (Tremblay et al., 2002a). In Antarctic waters, macronutrients like nitrate and phosphate are generally in ample supply, so that nutrient limitation is more likely to be the result of an inadequate abundance of trace metals, such as iron. This limitation has been demonstrated most convincingly in offshore waters north of the continental shelf (Boyd et al., 2000; Gervais et al., 2002; Coale et al., 2004), but evidence suggests that iron limitation is important in highly productive Antarctic polynyas as well (Arrigo et al., 2003a, Coale et al., 2004). Strong convection replenishes iron in surface waters where it reaches concentrations of about 0.3 nM prior to the initiation of the phytoplankton bloom (Sedwick and DiTullio, 1997). However, by late December, iron in surface waters of the RSP are nearly undetectable, leading to the decline of the phytoplankton bloom at a time when one third of the macronutrients have not yet been exhausted (Fiztwater et al., 2000; Arrigo et al., 2003a). Macronutrients are also still well above growth-limiting concentrations when diatoms begin to decline in the western

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MIZ of the expanding RSP (Arrigo et al., 2000), with the exception of those regions where iron released from melting sea ice raise iron concentrations in the upper 30 m of the water column above 2 nM (Sedwick and DiTullio, 1997). Therefore, even in the RSP, which is located on the productive Antarctic continental shelf, experimental results have shown that iron availability controls the rate of annual primary production. In the MGP the combination of light and nutrient availability is important in defining the strength of the phytoplankton bloom and the intensity of primary production. Two distinct deep mixing features were observed in the MGP, associated to varying degrees with high phytoplankton abundance and high photosynthetic competency (Vaillancourt et al., 2003). One was observed along the Adelie Land coast and was characterized by a deep mixed layer with chlorophyll a concentrations of 386 mg m−2 and elevated efficiency of photosystem II (Fv/Fm > 0.5). A second feature was located at the eastern end of the study area where a bloom of Phaeocystis antarctica formed within a shallow (24 m) mixed layer, with surface chlorophyll a concentrations of 8 mg m−3 and elevated Fv/Fm. Within this feature, the silicic acid to nitrogen utilization ratio explained over 30% of the variability in the distribution of surface water pCO2 , suggesting that a strong link exists in these waters between iron availability and phytoplankton primary production. Possible iron sources include aeolian deposition, release from melting sea ice, coastal sediments, and the transport of UCDW (Sambrotto et al., 2003). The spatial coherence of phytoplankton biomass, photocompetency, high salinity, and deep-mixing suggests that blooms of Phaeocystis and diatoms form in this region after physical disturbances result in mixing of nutrient-rich subsurface waters into the euphotic zone (Vaillancourt et al., 2003). 3.3

Effect of UV Radiation

Because polynya surface waters are exposed to solar radiation much earlier than adjacent ice-covered waters, they can receive relatively high doses of ultraviolet radiation (UVR), particularly in the Antarctic where reduced stratospheric ozone concentrations (the ozone hole) can persist into the spring (Frederick and Snell, 1988). Recently, the Arctic has also experienced a recurrent springtime thinning of the stratospheric ozone layer (Manney et al., 2003), resulting in increased exposure of surface waters to UVB radiation (UVB, 280 to 320 nm). However, little is known about the biological effect of such UVB enhancement on the Arctic marine ecosystem. Understanding the impact of enhanced UVB in Arctic waters is complicated by the fact, that unlike the Antarctic, a large amount of dissolved organic carbon (DOC) is deposited near the coast from the many rivers that flow into it. A significant fraction of this DOC consists of colored dissolved organic matter (CDOM) that absorbs very strongly in the UVB range and may help screen out the additional UVB radiation resulting from diminishing ozone concentrations (Vasseur et al., 2003). Arrigo and Brown (1996) showed primary production was enhanced in the upper approximately 30 m of the water column by the presence of even low concentrations of CDOM, where increases in production due to the removal of damaging ultraviolet radiation more than offset any reduction resulting from a decrease in water clarity. Unfortunately, measurements of the effects of UVB radiation on primary production in polynyas of either polar region are relatively rare. In the NOW polynya, Belzile et al. (2000) measured vertical profiles of UVR and photosynthetically available radiation (PAR) beneath five large sea ice floes. They found that 2–13% of the incident UVB radiation was transmitted through the snow (0.01–0.09 m thick) and ice (0.5–1.3 m thick) to the waters below. The relatively high UVR transparency found in their study coincided with the seasonal maximum of incident UV irradiance. Hence, they concluded that the resulting high UVR : PAR


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ratio may have a negative effect on phytoplankton communities in surface waters, as well as those within the sea ice (Belzile et al., 2000), although these effects were not actually measured. However, in the St. Lawrence Island polynya (Arctic), measurements of underwater PAR and UVR, together with an analysis of DNA damage using dosimeters, indicated that phytoplankton probably were not being negatively influenced by ambient levels of UVR, at least in late winter and early spring (Cooper et al., 2002). Even in Antarctic waters below the ozone hole, the impact of enhanced UVB radiation on rates of primary production is predicted to be very small (Arrigo, 1994; Arrigo et al., 2003b). When integrated to the 0.1% light depth, model results show that the loss of primary production resulting from enhanced fluxes of UVB due to severe ozone depletion are less than 0.25%. The loss of primary production is minimized by the strong attenuation of UVR within the water column and by sea ice which is at its peak extent at the time of the most severe ozone depletion (Arrigo et al., 2003b). Coastal polynyas in the Antarctic are most greatly affected by decreased ozone concentrations during the month of November (a 1– 3% loss of primary production), when ozone levels are still low and UVR fluxes are high. However, the phytoplankton blooms in most coastal polynyas, including the RSP and the MGP, have not yet reached their peak biomass before ozone levels increase to normal levels (Arrigo and van Dijken, 2003), further minimizing the impact of enhanced UVB on annual rates of production (Arrigo et al., 2003b).

4

Cloud Reduction over Polynyas with High Latent Heat Flux

Because rates of primary production early in the phytoplankton bloom are strongly controlled by light availability, Arrigo and van Dijken (2004) conducted a satellite-based study of the RSP to determine whether there were significant inter-annual differences in cloud cover (which can reduce incident irradiance by more than 50%) and whether differences in the degree of cloud cover are translated into changes in rates of primary production within the polynya. They found that the fraction of RSP surface waters covered by clouds exhibited a distinct seasonal pattern. During the month of October, polynya surface waters were rarely exposed to sunny skies, a reflection of the greater cloudiness during this time of year and the extremely small size of the RSP during October. Cloud-free open water area increased in November, the same time that the RSP started to increase in size, with a maximum of about 20% of the ice-free ocean surface experiencing sunny skies for some portion of the day. The amount of RSP surface waters exposed to sunny skies increased dramatically in December, reaching as high as 80% on some days. Cloudiness generally increased again in January, when the fraction of RSP surface waters covered by clouds always exceeded 0.8. The mean fraction of open water area covered by clouds during spring and summer varied relatively little from year to year, ranging from 0.90 to 0.94. Although these interannual differences in mean cloud-free open water area appear to be small, to the extent that sunny days are responsible for a disproportionate fraction of primary production, years when the cloud-free open water area is more variable might be expected to be more productive. Surprisingly, the interannual difference in the fraction of RSP surface waters covered by clouds was able to explain 98% of the variability in annual primary production in the RSP (Figure 3C), with cloudy years exhibiting the lowest annual production rates. It is important to note, however, that cloud cover in the RSP is highly negatively correlated with polynya size (Figure 3A), making it impossible to statistically separate the effects

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Figure 3: Relationship between (A) the annual mean open water area and the annual mean fraction of open water covered by clouds, (B) the annual mean open water area and annual pelagic primary production, and (C) the annual mean fraction of open water covered by clouds and annual pelagic primary production for the RSP.

Figure 4: Possible causes for the high correlation between polynya size and cloud cover. Additional solar insolation associated with light cloud cover (A) will melt more ice, increasing polynya size. Alternatively, enhanced heat flux from the open water within the polynya (B) may increase cloud dissipation. Evidence from the RSP suggests that (B) is more likely. of sea ice cover (Figure 3B) from effects of clouds in controlling annual production (Figure 3C). This high correlation implies that a physical connection exists between the amount of open water and the degree of cloudiness in the southwestern Ross Sea (Maslanik et al., 2001). This connection could be the result of enhanced solar insolation during the spring and summer of years when cloud abundance is low (Figure 4A), causing increased radiative heating of the sea surface and enhanced sea ice melting (Minnett, 1999; Wendler et al., 2000; Hanafin and Minnett, 2001). Alternatively, an oceanic heat source could accelerate sea ice melt (Jacobs and Comiso, 1989), and the resulting increase in open water could facilitate a higher oceanic heat flux to the atmosphere (Figure 4B), resulting in the dissipation of clouds (Wang and Wang, 2001). The second possibility is the more plausible for the RSP because in 2000–01, the unusually heavy sea ice cover, and hence the reduced area of open water, were clearly controlled by the presence of the B-15 iceberg fragments (Arrigo et al., 2002) and not by decreased solar insolation. This indicates that in 2000–01, the amount of open water was controlling


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the cloud cover, and not vice versa, a relationship that the high correlation between these two variables suggests applies to other years as well (Arrigo and van Dijken, 2004). The oceanic heat flux required both to melt the sea ice and for cloud dissipation in the RSP is probably derived from the movement of warm UCDW onto the continental shelf, the heat content of which represents a potential flux of greater than 200 W m−2 to the atmosphere (Jacobs and Comiso, 1989). This value is consistent with the estimated oceanic heat flux of 125 W m−2 that would be required for cloud dissipation (Sass, 2001). Jacobs and Comiso (1989) also noted that the breakout of sea ice in the Ross Sea is too rapid to be explained by winds or by solar radiative heating and must instead be the result of a deep water heat source. Considering the strong relationship that exists between the Multivariate ENSO Index (Wolter, 1987; Wolter and Timlin, 1993) and sea ice cover in the RSP (Arrigo and van Dijken, 2004), if in fact the movement of warm UCDW onto the continental shelf is an important factor controlling distributions of sea ice (and clouds) in the RSP, then there might be a connection between ENSO, the volume of UCDW that moves onto the shelf each year, and annual primary production (Arrigo and van Dijken, 2004).

5 Timing of Polynya Expansion There is evidence to suggest that the timing of polynya formation in both polar regions can impact not only the initiation of the phytoplankton bloom (often in non-intuitive ways), but the nature of the associated food web as well. Arrigo et al. (1998) reported that early expansion of the RSP could actually lead to a later development of the phytoplankton bloom. This is because the start of the phytoplankton blooms in the RSP are controlled by the interaction between seasonal changes in the frequency and intensity of katabatic winds and yearly differences in the timing of polynya formation. For example, higher than average winter temperatures over the Ross Sea result in a earlier polynya formation, presumably due to a thinner and weaker sea ice cover. Because the wind strength and the frequency of katabatic surges declines substantially between October and December, the earlier the polynya forms, the more likely that newly exposed surface waters will experience high winds associated with katabatic surges that can extend approximately 250 km beyond the coastal slope break (Bromwich and Kurtz, 1984; Bromwich, 1989; Bromwich et al., 1992). These strong katabatic winds may mix the phytoplankton out of the surface layer and increase the advective component of surface waters (with their associated biota) northward, forcing phytoplankton beneath the consolidated ice pack. The continuous removal of phytoplankton into regions unfavorable for growth would delay bloom development, particularly if the initiation of the bloom were dependent upon seeding by algal assemblages released from melting sea ice. Conversely, in years when the Ross Sea polynya forms later, katabatic events at the time of polynya formation will be less frequent and winds will be relatively weaker. The prolonged presence of sea ice in this case would buffer surface waters within the polynya from wind stress, which would decrease both vertical mixing and the advection of surface waters, allowing more rapid phytoplankton growth and earlier bloom formation once the polynya became ice-free. Timing of sea ice retreat may be an important mechanism controlling primary production and food web interactions in Arctic polynyas as well. Based on data collected from the Arctic, Hunt et al. (2002) proposed the Oscillating Control Hypothesis, which predicts that ecosystems experiencing seasonal ice retreat will alternate between bottom-up control in cold regimes and top-down control in warm regimes. When sea ice persists into the

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spring, phytoplankton bloom in cold, partially ice-covered water, whereas when ice retreats early (before mid-March), the increased solar radiation incident in the sea surface allows phytoplankton to bloom in warm water. In years when sea ice is persistent, low temperatures reduce both zooplankton production and juvenile fish survival, resulting in bottom-up control, a simple food web, and decreased populations of piscivorous (fish-eating) fish. Alternatively, when sea ice retreats early, phytoplankton bloom in warm water and zooplankton populations grow rapidly, providing plentiful prey for juvenile forage fish, and ultimately piscivorous fish. Marine birds and pinnipeds that feed on these forage fish also may fare better in cold regimes due to reduced competition from large piscivorous fish. Concepts such as the Oscillating Control Hypothesis exemplify the importance of interactions between physical aspects of the polynya system and the productivity of the biological populations that reside there. This is because at their most basic level, polynyas expose surface waters of polar oceans to atmospheric influences. The nature and intensity of these influences change over time so that the timing of polynya formation and its subsequent expansion is an important consideration when evaluating the physical influences acting on polynya dynamics. Once the polynya has formed, physical and chemical factors continue to influence biology through advection of nutrients into the polynya, mixing of nutrients to the sea surface, and radiative and meltwater stratification of the mixed layer. The production of a particular polynya will reflect the interplay between all of these physical factors, which ultimately, help structure food webs in these biologically rich polar waters.

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Odate, T., Hirawake, T., Kudoh, S., Klein, B., LeBlanc, B., Fukuchi, M., 2002. Temporal and spatial patterns in the surface-water biomass of phytoplankton in the North Water. Deep-Sea Research II 49, 4947–4958. Palmisano, A.C., SooHoo, J.B., Sullivan, C.W., 1987. Effects of four environmental variables on photosynthesis-irradiance relationships in Antarctic sea-ice microalgae. Marine Biology 94, 299–306. Peperzak, L., 2003. Climate change and harmful algal blooms in the North Sea. Acta Oecologica—International Journal of Ecology 24, S139–S144. Pesant, S., Legendre, L., Gosselin, M., Smith, R.E.H., Kattner, G., Ramseier, R.O., 1996. Size-differential regimes of phytoplankton production in the northeast water Polynya (77◦ –81◦ N). Marine Ecology Progress Series 142, 75–86. Priscu, J.C., Palmisano, A.C., Priscu, L.R., Sullivan, C.W., 1989. Temperature dependence of inorganic nitrogen uptake and assimilation in Antarctic sea-ice microalgae. Polar Biology 9, 443–446. Sambrotto, R.N., Matsuda, A., Vaillancourt, R., Brown, M., Langdon, C., Jacobs, S.S., Measures, C., 2003. Summer plankton production and nutrient consumption patterns in the Mertz Glacier Region of East Antarctica. Deep-Sea Research II 50, 1393–1414. Sass, B.H., 2001. Modelling of the time evolution of low tropospheric clouds capped by a stable layer. HIRLAM Tech. Rep. Norrköping (http://www.knmi.nl/hirlam/TechReports/). Schneider, W., Budeus, G., 1995. On the generation of the Northeast Water Polynya. Journal of Geophysical Research 100, 4269–4286. Schneider, W., Budeus, G., 1997. Summary of the Northeast Water Polynya formation and development (Greenland Sea). Journal of Marine Systems 10, 107–122. Sedwick, P.N., DiTullio, G.R., 1997. Regulation of algal blooms in Antarctic shelf waters by the release of iron from melting sea ice. Geophysical Research Letters 24, 2515–2518. Smith, W.O. Jr., 1995. Primary productivity and new production in the Northeast Water (Greenland) polynya during summer-1992. Journal of Geophysical Research 100, 4357– 4370. Smith, W.O. Jr., Gordon, L.I., 1997. Hyperproductivity of the Ross Sea (Antarctica) polynya during austral spring. Geophysical Research Letters 24, 233–236. Touratier, F., Legendre, L., Vezina, A., 2000. Northeast Water Polynya 1993: construction and modelling of a time series representative of the summer anticyclonic gyre pelagic ecosystem. Journal of Marine Systems 27, 53–93. Tremblay, J.-E., Gratton, Y., Fauchot, J., Price, N.M., 2002a. Climatic and oceanic forcing of new, net, and diatom production in the North Water. Deep-Sea Research II 49, 4927–4946. Tremblay, J.-E., Gratton, Y., Carmack, E.C., Payne, C.D., Price, N.M., 2002b. Impact of the large-scale Arctic circulation and the North Water Polynya on nutrient inventories in Baffin Bay. Journal of Geophysical Research 107, 3112. Vaillancourt, R.D., Sambrotto, R.N., Green, S., Matsuda, A., 2003. Phytoplankton biomass and photosynthetic competency in the summertime Mertz Glacier Region of East Antarctica. Deep-Sea Research II 50, 1415–1440. Vasseur, C., et al., 2003. Effects of bio-optical factors on the attenuation of ultraviolet and photosynthetically available radiation in the North Water Polynya, northern Baffin Bay: ecological implications. Marine Ecology Progress Series 252, 1–13. Wang, S., Wang, Q., 2001. Surface fluxes and stratocumulus clouds in DECS: a modeling study. In: Fourth Conference on Coastal Atmospheric and Oceanic Prediction, St. Petersburg, FL.

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Wendler, G., Moore, B., Dissing, D., Kelley, J., 2000. On the radiation characteristics of Antarctic sea ice. Atmosphere-Ocean 38, 349–366. Wolter, K., 1987. The Southern Oscillation in surface circulation and climate over the tropical Atlantic, Eastern Pacific, and Indian Oceans as captured by cluster analysis. Journal of Climate and Applied Meteorology 26, 540–558. Wolter, K., Timlin, M.S., 1993. Monitoring ENSO in COADS with a seasonally adjusted principal component index. In: Proc. 17th Climate Diagnostics Workshop. Norman, OK, NOAA/N MC/CAC, NSSL, Univ. Oklahoma, pp. 52–57. Yager, P.L., Deming, J.W., 1999. Pelagic microbial activity in an arctic polynya: testing for temperature and substrate interactions using a kinetic approach. Limnology and Oceanography 44, 1882–1893.

Chapter 8

Primary Production and Nutrient Dynamics in Polynyas J.-E. Tremblay1 and W.O. Smith Jr.2 1 Département de Biologie, Université Laval, Québec, QC G1K 7P4, Canada 2 Virginia Institute of Marine Sciences, College of William and Mary, Gloucester Pt., VA 23062, USA

Abstract Phytoplankton assemblages in polynyas are strongly impacted by the unique environment of those systems, and their growth and accumulation is always greater within a polynya than under heavy ice. The extent of this enhancement is dependent on the physical conditions of the polynya—the duration of the polynya’s existence, the distribution of ice and snow, and the physical circulation within it. We review the polynyas in both Arctic and Antarctic waters that have been intensively studied and compare them with respect to biomass, daily productivity, chemical and physical constraints, annual productivity, export, and effects on food webs and the local biogeochemical cycles. We conclude that the most productive polynyas (the North Water polynya and the Ross Sea polynya) have remarkably similar short-term productivity, and the annual productivity and seasonal timing of both are also similar. However, the two have strong dissimilarities in modes of control and export. The ecological consequences of enhanced production within a polynya are also investigated, and appear to vary among polynyas. We suggest that the differences among polynyas within polar systems reflect the differences in large-scale physical forcing that exist across the Arctic and Antarctic, and that generalizations among polynyas need to encompass this variability.

1 Introduction Polynyas are areas of reduced ice cover within regions of extensive consolidated ice, and are physically generated features. Regardless of whether they are latent-heat polynyas, sensibleheat polynyas, or mixed-mode polynyas (Muench, 1990), these singularities have reduced concentrations of ice and snow, which in turn implies that more solar radiation penetrates into the water column. Because irradiance in polar regions is the primary control of phytoplankton growth (Smith and Sakshaug, 1990), the growth of phytoplankton in polynyas is enhanced relative to the surrounding regions. And while this has been repeatedly shown to be true, there has been no detailed, bi-polar comparison of field-based phytoplankton investigations across polynyas. Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74008-9

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Polynyas occur throughout both the Arctic and Antarctic (Figure 1). In the Arctic they occur in the lee of islands (e.g., the St. Lawrence Island polynya) and downstream of ice shelves (e.g., the Northeast Water polynya off the coast of northeastern Greenland) or ice bridges (e.g., the North Water polynya between Greenland and Ellesmere Island). In the Antarctic they largely occur off ice shelves (e.g., the Ross Sea polynya off the Ross Ice Shelf) and the coast (e.g., the East Lazarov Sea polynya off the coast of Queen Maude Land; Arrigo and van Dijken, 2003). Some polynyas have been observed in deep water (such as the Maud Rise polynya in the Weddell Sea), but nearly all polynyas occur on the continental shelves of both poles. This is because many polynyas are generated by the action of wind; furthermore, in those systems where heat input from below is important, this heat input is often driven by the interaction of currents with the continental shelf itself. As a result, these regions are those that are most impacted by polynyas (Arrigo and van Dijken, 2003). Given that the shelves are already much more productive than the offshore waters, the question then becomes “What is the biological significance of polynyas?” Is annual productivity increased by their presence, or is the timing of the productivity pulse simply earlier, with yearly production being equal? A remote-sensing investigation of the Antarctic suggests that mean, annual primary production in several polynyas is possibly lower than the mean for the Southern Ocean (Arrigo and van Dijken, 2003). A definite answer is difficult to obtain, however, as it is very difficult to find a region of similar size and bathymetry that is not a polynya with which to compare it. Only by in-depth comparison to broad patterns of growth and accumulation can the significance of polynyas be understood. Arctic and Antarctic polynyas are in many ways very different (Table 1). For example, in the Arctic macronutrients are initially modest and usually depleted to the detection limit during phytoplankton growth. Nitrate initially ranges from less than 5 (e.g., Northeast Water) to 12 μM (North Water) in the upper mixed layer, and is dependent on mixing regime and the location of polynyas relative to the incursions of Pacific- and Atlantic-derived waters. In the Antarctic macronutrients are always elevated (NO3 initially about 30 μM; Jones et al., 1990; Smith et al., 2003), and are seldom depleted during the short, polar growing season. Recent results on investigations of the productivity of Antarctic continental shelf systems have shown the importance of iron in limiting growth, biomass and phytoplankton assemblage composition (Martin et al., 1990; Sedwick and DiTullio, 1997; Olson et al., 2000; Coale et al., 2003). Similarities also exist between Arctic and Antarctic polynyas, with many of the physical characteristics and temporal patterns of phytoplankton growth and accumulation being alike (Table 1). Given the variety of chemical and physical features, a continuum of productivity within polynyas would be expected. As polynyas are expected to have altered regimes of productivity (either in magnitude or in the timing of the productivity pulse), it is expected that they also would be the sites of extensive benthic and higher trophic level (e.g., birds, whales, seals) productivity (see Arrigo and van Dijken, 2003; Karnovsky et al., 2007). Certainly many polynyas are the locations of extensive accumulations of birds and marine mammals, but that enhancement may also be driven by factors independent of productivity (such as access to nesting and breeding sites, refugia from predation or breathing for marine mammals). In polynyas the cryobenthic coupling is altered by the removal or thinning of ice, and the benthic response to this change may be different than the increased pulse of the enhanced water column production. Finally, because many of the higher trophic levels integrate different spatial scales than do phytoplankton (by foraging over different scales of time and space), it may be difficult to ascribe a food web enhancement to increased polynya productivity alone. Indeed, it is expected that

Primary Production and Nutrient Dynamics in Polynyas

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Figure 1: Distribution of polynyas in May 1998 as derived from SeaWiFS satellite imagery. (A) Northern hemisphere (1: Cape Bathurst; 2: North Water; 3: Northeast Water, 4: Whaler’s Bay; 5: Laptev polynya and flaw lead; 6: Wrangel Island), and (B) Southern hemisphere (1: Ross Sea; 2: Terra-Nova Bay; 3: Mertz Glacier). The estimated pigment concentration is derived from the NASA OC4 (version 4) algorithm.

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Table 1: Comparison of the physical, chemical and physiographic properties of five different polynyas. ND = no data Variable

Polynya Northeast Water

Ross Sea

Mertz Glacier

Terra Nova Bay

Location

7◦ –79◦ 30 N, 71◦ –79◦ W

77◦ –81◦ N, 5–15◦ W

76◦ 30 –77◦ 30 S, 170–178◦ W

66◦ 30 S, 145◦ E

75◦ S, 165◦ E

Mean depth and range (m) Winter water temperature (◦ C) Winter salinity (psu) Summer water temperature (◦ C) Summer salinity (psu) Winter [NO3 ] (μM) Minimum [NO3 ] (μM) Winter [Si(OH)4 ] (μM) Summer [Si(OH)4 ] (μM) Max. chlorophyll concentration (μg l−1 ) Max. daily productivity (mg C m−2 d−1 ) Annual productivity (g C m−2 y−1 )

300 (170–600) −1.86 33.6 ∼1.0 (max 2) ∼31.0 ∼11.014 5 μm (mostly diatoms) in surface waters suggests that during the productive period (July– August), 32 to 67% of the production either sank or was advected east in the anticyclonic gyre (Pesant et al., 1997). Given the low sinking flux within the polynya, the main export pathway was presumably lateral transport and subsequent sinking east of the polynya. The remaining production was consumed in situ by copepods, appendicularians and heterotrophic dinoflagellates. Grazing by appendicularians seems to have led to significant vertical export, whereas that by dinoflagellates and copepods promoted recycling and remineralization within the surface layer. 2.3

Cape Bathurst Polynya

The Cape Bathurst Polynya is relatively small, with a maximum extent of open waters of ca. 24,000 km2 (Barber and Massom, 2007). Remotely-sensed ice cover and chlorophyll a for the period 1998–2002 shows substantial interannual variability (Arrigo and van Dijken, 2004a). The open water period and maximum chlorophyll a concentration (derived from SeaWiFs imagery) generally ranges from 3 to 5 months and ca. 3–5 μg l−1 , respectively, and blooms occur in June and in September (Arrigo and van Dijken, 2004a). The latter is generally the strongest, possibly due to enhanced mixing supply of nutrients combined with reduced grazing. Estimated annual phytoplankton production ranges from 90 to 175 g C m−2 y−1 . In 1998 an unusually early forming polynya was associated with a warmweather anomaly that presumably induced early ice melt and vertical stratification of the water column, and kept the waters ice-free for seven months (Arrigo and van Dijken, 2004a). This anomaly resulted in the generation of high chlorophyll a concentrations (8 μg l−1 ) in early May. 2.4

Other Arctic Polynyas

The Arctic Ocean is home to several other polynyas that are maintained by sensible heat (Whaler’s Bay Polynya off Svalbard, several small polynyas in the Canadian Archipelago) and latent heat (Laptev Sea polynya and flaw lead system; Wrangel Island polynya). Published information on phytoplankton is too scant to establish clear patterns, but satellite imagery suggests that some of those are sites of enhanced phytoplankton production (e.g. Laptev Sea; Figure 1). In the Bering Sea wind-driven polynyas commonly form in the lee of coastlines. Limited information on phytoplankton is available for the shallow (60 m) St.-Lawrence Island Polynya (Stringer and Groves, 1991). The polynya is dynamic and


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ephemeral; its size, shape and location vary as a function of wind, and hence it can be located to the north of the island initially, but switch to the south of the island in a matter of days. The extent of the polynya is typically small from January to April (2000–4000 km2 ) and can reach a maximum of ca. 16,000 km2 in May. Integrated chlorophyll a concentrations in open waters range from 10–50 mg m−2 in April to 100–1000 mg m−2 in May–June (Cooper et al., 2002). Considering all sampling periods and stations, the mean concentration of chlorophyll a is only 0.60 ± 0.34 μg l−1 (max of 2.47 μg l−1 ), despite the high nutrient concentrations supplied by the Anadyr Current (Grebmeier and Cooper, 1995). It is likely that the local pelagic production is transported laterally before substantial biomass can accumulate within the polynya. Proxy indicators of sediment deposition and the stable isotopic signature of fresh organic deposits suggest that ice algae, rather than phytoplankton, sustain the local benthic production (Cooper et al., 2002). 2.5 2.5.1

Ross Sea Polynya Temporal Patterns and Assemblage Composition

The Ross Sea polynya, centered at ca. 77◦ 30 S, 174◦ E, is the Antarctic’s most thoroughly studied polynya, and the phytoplankton dynamics of the polynya are well characterized. It has characteristics of both latent and sensible heat polynyas, in that winds advect ice away from the ice shelf, but heat is also added through the movement of Modified Circumpolar Deep Water onto the shelf (Jacobs and Comiso, 1989). The growing season begins early, in late October or early November (Smith and Gordon, 1997), approximately six weeks earlier than the annual bloom in the Polar Front region some 2000 km to the north. During this time a thin layer of ice and relatively little snow, allowing sufficient irradiance to penetrate and drive photosynthesis, often covers the region. Initial phytoplankton biomass is low (less than 0.05 μg l−1 ; Smith et al., 2000b). The polynya is often initially dominated by the haptophyte Phaeocystis antarctica, which appears to be able to utilize low irradiances for its growth (Moisan and Mitchell, 1999), although some diatoms and autotrophic dinoflagellates grow as well. Few losses to the assemblage apparently occur during spring, as the chlorophyll concentrations continue to increase through mid to late December (DiTullio et al. (2000) found evidence of deep transport of active populations to depth, but the mechanism and quantitative significance of these events was not shown). Chlorophyll concentrations reach up to 15 μg l−1 , but more typically reach ca. 8 μg l−1 (Figure 4). During this period nitrate is reduced from 30 to around 15 μM, and silicic acid from 80 to 72 μM. Growth normally proceeds much more slowly thereafter, and it appears that the reason for this decrease is the onset of iron limitation (Sedwick and DiTullio, 1997; Olson et al., 2000; Sedwick et al., 2000; Arrigo et al., 2003). At the same time losses of P. antarctica from the euphotic zone become more rapid and are likely due to the aggregation of colonies (enhanced by the high biomass conditions) and more rapid sinking of these larger particles (Asper and Smith, 2003). As a result, the chlorophyll decreases rapidly in January (Arrigo and McClain, 1994; Smith and Gordon, 1997; Smith and Asper, 2001; Figure 4), so that by the end of the month chlorophyll concentrations are 1.0 μg l−1 or less (Smith et al., 2000a). Little accumulation normally occurs in February, and by the end of the month ice forms, vertical mixing increases and the water column returns to winter conditions. This transition likely takes a number of months, but phytoplankton standing stocks are less than 0.1 μg l−1 by the beginning of May (Smith et al., 2000b). Smith et al. (2000b) generated a seasonal composite by merging data from two field seasons, and the patterns found appeared to explain the temporal progression well. However,

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Figure 4: The temporal patterns of clear-sky incident irradiance (E0 ), nitrate (NO3 ), silicic acid (Si(OH)4 ), and chlorophyll (chl) within the Ross Sea polynya. Based on the climatology of Smith et al. (2003) 77◦ S, 173◦ E. estimates of inter-annual variability have since been made by remote-sensing (Arrigo and van Dijken, 2004b) and by collecting data on net seasonal production and the temporal patterns of phytoplankton biomass and composition. Variations among years are substantial and were partly related to the impact of El Niño and an exceptionally large iceberg on ice dynamics (Arrigo and van Dijken, 2004b). Variations in the composition of the phytoplankton were also observed, as well as the temporal patterns of assemblage change (Smith et al., 2006). For example, in 2001–2 the production was less than the climatological mean (Smith et al., 2003), but in 2003–4 it was greater. In the former year the initial growth was dominated by Phaeocystis antarctica, as it was during the studies of Arrigo et al. (1999) and Smith et al. (2000a). However, in 2003–4 diatoms dominated the assemblage throughout the year. Thus the assemblage composition is not as predictable as often believed. Furthermore, Smith et al. (2006) suggest that the factors limiting growth also vary among years, and that no iron limitation occurred during 2003–4. The release from iron limitation during this year was, however, unexplained, although it may be related to the extent (both in space and time) of inputs from modified Circumpolar Deep Water (Hiscock et al., in press). 2.5.2

The Role of Iron

In January after the “normal” bloom of P. antarctica, diatom growth continues, with the rate likely controlled by iron availability. Diatoms are often found in mixed layers that are shallower than those in which P. antarctica assemblages are found (Arrigo et al., 1999; Smith


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Figure 5: The temporal patterns of in vivo fluorescence (an index of chlorophyll) from one location in the Ross Sea polynya (77◦ S, 173◦ E). Lines represent a 50-point running average for three depths (19, 25 and 41 m). The initial maximum is caused by the early growth of Phaeocystis antarctica within the polynya, and the second maxima is caused by an intense diatom bloom (Smith et al., 2006). and Asper, 2001), although there is no significant difference between the photosynthetic capacity of the two groups (van Hilst and Smith, 2002). They are thus exposed to higher daily irradiance and have greatly restricted iron inputs from below. Olson et al. (2000) found that photosynthesis in all species tested (a number of diatoms and P. antarctica) were equally limited by iron during late January. In other years a bloom of diatoms followed that of P. antarctica; furthermore, this secondary bloom can be nearly of the same magnitude as the P. antarctica bloom (Figure 5), which suggests that iron limitation was not strong during that particular year. Coale et al. (2003) found that colonial P. antarctica had higher iron demands than did diatoms and all other species tested, which is consistent with the early bloom of P. antarctica relative to diatoms. In addition, in January the mixed layer continues to become shallower, and when P. antarctica biomass declines, more radiation penetrates and is available to diatoms. It should be noted that the only report of macronutrient depletion in the Ross Sea was from a region near the Victoria Land coast (Smith and Nelson, 1985) in waters that had extremely strong stratification and were potentially influenced by glacial run-off. Thus the waters potentially were enriched with iron, and the strong stratification and high irradiances allowed the diatoms to accumulate and deplete both nitrate and phosphate, and reduced silicic acid to ca. 4 μM. The variations in iron inputs (as a function of melt water input either from land or ice) may play a role in the ultimate contribution of diatoms to the Ross Sea phytoplankton. Iron

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can be supplied from aerosols, ice, deeper water and sediments, but to date no clear understanding of the relative role of each is available. Atmospheric deposition is known to be extremely low in the Antarctic, particularly farther south (Fung et al., 2000), and so aerosols as a source of iron are likely insignificant relative to inputs from the water column. However, the degree of deposition on ice at low rates throughout winter and its subsequent release into the water upon melting is uncertain, and may depend on latitude. Sedimentary sources of iron would be dependent on depth and vertical circulation, because many polynyas have extensive stratification induced by melt-water, which is not easily broken down by winds, and in a similar manner, deep-water supply of iron to the surface may be restricted in summer months. Hence, given the restricted inputs to the surface, iron can and does become limiting to phytoplankton growth in the summer in some Antarctic polynyas. In the Southern Ocean iron input from the atmosphere is extremely low (Fung et al., 2000), as most of the continent is ice-covered. Iron concentrations in the Ross Sea polynya are initially elevated and above saturation concentrations (Fitzwater et al., 2000; Coale et al., 2003), but fall to levels low enough to limit phytoplankton growth. Hiscock et al. (in press) suggest that deep-water intrusions of modified Antarctic Deep Water onto the shelf result in increased levels of iron at the surface, and therefore stimulate both the magnitude and duration of phytoplankton growth in the region. Data from other cruises support this hypothesis (Smith et al., unpublished). It is interesting that off-shelf intrusions of modified ACC water stimulate diatom growth on the continental shelf of the west Antarctic Peninsula as well (although P. antarctica exists, it is largely confined to regions close to the coast; Prézelin et al., 2000), and we suggest that this is due to iron stimulation of diatom production. A novel mechanism for the control of phytoplankton assemblage composition has been proposed by Tortell et al. (2002), and this mechanism may be operative in the Ross Sea. They found in shipboard enrichments in tropical waters that under high iron, high pCO2 conditions, Phaeocystis spp. dominated the growth of the phytoplankton, whereas under reduced iron and pCO2 conditions, diatoms grew and dominated. In the Ross Sea Fe and pCO2 covary; that is, early in the spring, the water column is characterized by high iron and pCO2 , where the opposite conditions occur in summer (when diatoms are more commonly encountered). It is possible that this type of Fe–CO2 interaction occurs in the Ross Sea and influences assemblage composition, but experimental proof is unavailable. 2.5.3

Magnitude and Fate of Primary Production

The annual productivity of the Ross Sea polynya has been estimated using a number of techniques. Arrigo and van Dijken (2003) used pigment data derived from satellite imagery along with a simple productivity model and estimated that the annual productivity of the polynya (1997–2002) was 151 ± 21 g C m−2 y−1 . Nelson et al. (1996), based on temporal patterns of phytoplankton accumulation, suggested that productivity was ca. 112 g C m−2 y−1 , and Smith and Gordon (1997) used data collected in the austral spring to update the estimate of Nelson et al. to about 190 g C m−2 y−1 . Arrigo and van Dijken (2003) used a bio-optical model and concluded that the polynya’s annual productivity was 149 g C m−2 y−1 . Regardless of the estimate, the Ross Sea polynya is the most productive location within the entire Antarctic. Using nutrient deficits derived from the nitrate climatology of Smith et al. (2003), net community production for the polynya was estimated to be 1.21 mmol N m−2 d−1 over the entire growing season (Table 2), which is equivalent to 1.50 g C m−2 d−1 . Nitrate is the dominant nitrogen source during the hyperproductive periods, and ammonium is utilized more

Month

NO3 (mol m−2 )

October November December January February

6.23 6.04 5.38 4.90 5.02

Si(OH)4 (mol m−2 )

15.8 15.7 15.4 14.5 14.4

NO3 a (mol m−2 )

Si(OH)4 a (mol m−2 )

– 0.19 ± 0.01 0.85 ± 0.11 1.33 ± 0.13 1.21 ± 0.08

– 0.16 ± 0.07 0.46 ± 0.03 1.32 ± 0.68 1.43 ± 0.27

N-based primary productivityb (g C m−2 d−1 ) – 0.51 ± 0.02 2.20 ± 0.40 2.38 ± 0.35 0.02 ± 0.04c

Diatom primary productivityb (g C m−2 d−1 ) – 0.10 ± 0.04 0.19 ± 0.03 0.56 ± 0.43 0.16 ± 0.18c

Percentage of productivity by diatoms (%) – 20.1 8.8 23.4 1068


Table 2: Climatological estimates of nutrient deficits from the Ross Sea polynya. To estimate primary production from nitrate disappearance, a C/N molar ratio of 6.2 was used (Shields et al., unpublished), and an f -ratio of 0.9, 0.75, 0.5 and 0.5 for October, November, December and January, respectively. To estimate diatom production, a BioSi/C ratio of 0.62 was used (Nelson and Smith, 1986)

a Cumulative removal calculated from October. b Monthly means calculated from nutrient removal over 30 days. c Negative values excluded from means.

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during January and February. Using those climatological estimates, primary productivity equals 153 g C m−2 y−1 (Table 2). These estimates are similar to those of Arrigo and van Dijken (2003), who estimated January and annual productivity to be 1.46 g C m−2 d−1 and 151 g C m−2 y−1 (in contrast to our estimates of 2.20 g C m−2 d−1 and 153 g C m−2 y−1 ). The slightly lower short-term estimates of Arrigo and van Dijken (2003) likely result from the inability of the remote-sensing approach to account for biological losses of pigments within the water column, but the annual estimates are nearly equal due to the longer growing season observed during the year analyzed by Arrigo and van Dijken (2003). In a similar fashion, the productivity of diatoms can be estimated from silicic acid uptake (Table 2). Diatom carbon productivity (converted by the ratio measured by Nelson and Smith (1986) during a nearly monospecific diatom bloom off the coast of Victoria Land) was a relatively small fraction of the total productivity (19.8% on an annual basis). This is an underestimate, as the nitrate concentrations increased slightly during February (likely due to the increased frequency of storms and vertical-mixing), although silicic acid concentrations decreased. This suggests that Si uptake was in fact greater than the nutrient budgets would indicate, but regardless, diatom contribution to the annual production is relatively minor. It also should be noted that in February the relative productivity of diatoms to the total is extreme (more than 1000%), but this is likely a result of the modification of the Si : N uptake ratio under iron limitation as well as the small and variable magnitude of nitrogen uptake. The temporal nature of nutrient removal shows that in the Ross Sea polynya normally there is a temporal separation of the growth of diatoms and P. antarctica (Figure 4). Phaeocystis growth begins in spring, and its biomass reaches a maximum in December; indeed, a majority of the productivity during this time can be attributed to P. antarctica (up to 94% in December). However, its biomass and productivity declines rapidly thereafter (most likely due to iron limitation), and by mid-February it has little net effect on nitrate removal. In contrast, diatoms continue to grow and remove silica (and nitrogen), and in February nearly all of the productivity can be attributed to diatoms (Table 2). During 2003–4 a detailed analysis of fluorescence at a single location within the polynya confirms this pattern of spring P. antarctica growth—a primary biomass maximum in December, a decline in pigments, and a secondary maximum that results from diatomaceous growth (Figure 5; Smith et al., 2006). The patterns of vertical export from the surface layer of the Ross Sea have been investigated through the use of time-series sediment traps. Because the number of traps that can be deployed during any one season is limited, flux data are often limited in space. However, the flux from the Ross Sea polynya can be characterized from the data of Dunbar et al. (1998), Collier et al. (2000), Accornero and Gowing (2003), and Langone et al. (2003). Silica fluxes are maximal in late summer, which appear related to the initiation of ice production and increased sinking rates of metabolically stressed diatoms, as well as the grazing on diatoms by mesozooplankton (Figure 6). Carbon fluxes, however, are greatly delayed in time, with the maximum occurring in April–May, well into the austral winter. This peak is due to organic flux mediated by pteropods. While the biology of these groups (Limacina sp. and Cliona sp.) has not been well studied in the Antarctic, pteropods contribute to the fluxes in nearly all sediment trap measurements. It is controversial as to whether these should be included as a “real” flux or a migratory contribution, but in the Ross Sea Collier et al. (2000) argued that the pteropods were largely undergoing disintegration, suggesting that their collection was a result of death within the water column and subsequent sinking. Pteropods are also frequently found in other traps in the Ross Sea, so the event reported by Collier et al. (2000)


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Figure 6: The temporal patterns of vertical flux of biogenic silica (BSi) and particulate organic carbon (POC) from within the Ross Sea polynya. Data taken from Collier et al. (2000) and Langone et al. (2003) and a simple arithmetic mean generated for this one season (1996–7). Also plotted is the climatological chlorophyll a (chl) concentration from Smith et al. (2003). is not simply an isolated occurrence, but likely is a feature of the biological dynamics of the polynya. Removal of the pteropod fraction results in a four-fold reduction in flux (Collier et al., 2000). Both biogenic fluxes are temporally decoupled from surface production. The annual POC flux at 200 m in the central Ross Sea, including the contribution of pteropod remains in the traps, is in the order of 6 g C m−2 y−1 (Collier et al., 2000), which is ca. 4% of the annual surface production. 2.6

Mertz Glacier Polynya

The Mertz Glacier polynya is not as well characterized as the Ross Sea Polynya, but is apparently quite different with regard to many of its biological features. It is also characteristic of a large number of polynyas in this area of the Antarctic; it has been estimated that there are 28 recurrent polynyas along the east Antarctic coast, covering an area of 169,000 km2 during winter (Massom and Comiso, 1994). The region apparently is opened largely by the strong katabatic winds flowing off the coast of East Antarctica and funneled by the shore’s topography, but also may have some heat brought into the region by the upwelling of Modified Circumpolar Deep Water from off the shelf (Sambrotto et al., 2003). Macronutrients were always elevated ([NO3 ] > 22 μM, [Si(OH)4 ] > 58 μM) and above saturation levels, and iron concentrations ranged from 0.1–0.2 nM (Sambrotto et al., 2003), with the lowest levels coinciding with regions of the greatest nitrate removal. Chlorophyll concentrations in the polynya generally were 2.5 μg l−1 or below, and diatoms dominated these areas. Studies on the physiological state of phytoplankton have also been conducted in this polynya (Vaillancourt et al., 2003). Observed Fv /Fm ratios (proxy for the maximum quantum yield of photosystem II) of ca. 0.45 indicated that the polynya’s phytoplankton were not limited by nutrients and were

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physiologically robust, although the surface assemblage did exhibit strong photoinhibition that could be relieved by short (hours) periods of reduced light. Unfortunately, the taxonomic dominance of the assemblage was not determined at this time. 2.7

Terra Nova Bay Polynya

The Terra Nova Bay polynya is a rather small polynya, but its significance lies in the fact that it exhibits distinct responses that are different from those of the Ross Sea. Furthermore, it has a deep-water connection (ca. 1000 m) that apparently can act as a conduit for Antarctic Deep Water release from the continental shelf into the deeper waters of the Pacific Sector. It is forced by strong katabatic winds that blow from the west, and maintain a polynya of variable size through much of the year (van Woert, 1999). Its nutrient structure is similar to that of the Ross Sea, with summer nitrate concentrations around 15 μM (Catalano et al., 1999). Arrigo and McClain (1994) used surface chlorophyll a concentrations estimated from the Coastal Zone Color Scanner (CZCS) to show the marked difference in temporal bloom dynamics between the Ross Sea and Terra Nova Bay polynya (TNB). They found that the phytoplankton bloom in the TNB was at least a month later than that in the RS polynya, and suggested that this was due to deeper vertical mixing driven by strong katabatic winds. However, Nuccio et al. (1999) and Fonda Umani et al. (2002) compiled cell count data from TNB and showed that phytoplankton abundance was maximal in late December (dominated by P. antarctica), declined thereafter, but exhibited a secondary peak in mid-February. The latter feature may have been similar to that observed by Arrigo and McClain (1994), but no satellite images of the region earlier in the year were available to observe the primary cell abundance maximum. Based on a single survey, Arrigo et al. (1999) suggested that TNB was also the site of large diatom blooms. While it no doubt is the site of frequent diatom accumulations (the sediments there are diatomaceous oozes), other species have been observed, including Phaeocystis antarctica, dinoflagellates, and other phytoflagellates (Innamorati et al., 1992; Nuccio et al., 1999; DiTullio et al., 2000; Fonda Umani et al., 2002).

3

Ecological Consequences of Polynya Production

In the far northern and southern polynyas, it might be expected that the enhanced production would generate a cascade of increased production throughout the entire food web, terminating with increased biomass of higher trophic levels, as has been proposed for large oceanic regions (e.g., Ryther, 1969; Nixon, 1982; Pauly and Christensen, 1995). In the Antarctic Arrigo and van Dijken (2003) found a positive relationship between penguin colony size and the productivity of polynyas. For other animals, however, the relationship may not always be positive, as some large bodies of water fail to show an increase in higher trophic level productivity with increased productivity as a result of changing composition of various trophic levels. In addition, polynyas may have a higher trophic level fauna that is restricted in time and space due to their life histories, and many species of zooplankton may be unable to exploit very early and fast-growing blooms. Hence any increased productivity may not be reflected in the yield of higher trophic levels. In this case, a large fraction of the primary production is expected to sink to deep waters, which would result in a relatively efficient biological CO2 pump and benthic–pelagic coupling. Given the well-defined spatial relationships of productivity within some polynyas, the available data provide some insights into this fundamental oceanographic relationship.

Primary Production and Nutrient Dynamics in Polynyas 3.1

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North Water Polynya

The congruence of high primary production and abundant planktivorous birds and marine mammals in the North Water Polynya has long been recognized by explorers and, more recently, confirmed by the scientific community (Brown and Nettleship, 1981; Stirling et al., 1981; Stirling, 1997). While this observation led to the hypothesis of a causal link between high primary production and the apparent success of the herbivorous food web (Dunbar, 1981), the possibility exists that mammals and birds utilize the polynya also to gain access to open waters. The high incidence of accessible water facilitates breathing and foraging and the greater productivity perhaps is only a fringe benefit. Ice entrapment is a significant cause of mortality for cetaceans in Baffin Bay (Heide-Joergensen et al., 2002) and they may use the North Water as a refuge. Satellite-tracking investigations show that many belugas overwinter in the polynya, but move south and west during summer to feed in seasonally ice-free bays (Richard et al., 2001), as presumably do walruses, narwals and bowhead whales (Richard et al., 1998). Although this pattern questions the causal link between enhanced productivity and the aggregation of marine mammals in the North Water, the ability to forage early in the year within the polynya possibly has a positive impact on fitness and survival. Moreover, the linear enrichment of 15 N across trophic levels is consistent with a full-fledged herbivorous food web based on local phytoplankton production (Hobson et al., 2002). In contrast, key herbivorous copepods and planktivorous birds are clearly synchronized with the early phytoplankton outburst. The recruitment of the herbivore Calanus hyperboreus tracks phytoplankton blooms in the Arctic and occurs two month earlier in the North Water than in adjacent seasonally ice-covered regions (Ringuette et al., 2002). Such flexibility is possible because the females spawn at depth during winter and the new cohort can react opportunistically to a bloom. In other smaller species (e.g. Oithona similis), egg development strongly depends on temperature and is delayed so that first-feeding nauplii may not take advantage of the main bloom (Ringuette et al., 2002; Deibel and Daly, 2007). The relatively low biomass of zooplankton relative to maximum chlorophyll a concentrations in the North Water (Saunders et al., 2003) suggests that planktonic carnivores and birds regulate their biomass (Tremblay et al., 2006b). The most abundant planktivorous bird (dovekie; Alle alle) breeds on the steep slopes of the adjacent Greenland Coast (Boertman and Mosbech, 1998) and migrates south for the winter (Stenhouse and Montevecchi, 1996). During May 1998, the vast majority of foraging dovekies (>30 million birds) were observed in the eastern sector when the diatom bloom was well underway (Karnovsky and Hunt, 2002). At this time very few birds were seen in the west over waters with low chlorophyll a. The highest numbers of foraging birds in the east coincided with the maximum abundance of their primary prey, the copepods C. hyperboreus and C. glacialis. Feeding forays eventually encompass the northern and western North Water, tracking productive waters after the demise of the bloom in the east. 3.2

St. Lawrence Island Polynya

It appears that much of the production in the St. Lawrence Island polynya is partitioned to the benthos, although substantial bird populations do exist in the region. Maps of the distributions of benthic organisms and biomass, however, do not show a clear relationship to the average position (and hence production) of the polynya (Grebmeier and Cooper, 1995), despite the fact that benthic biota often show a positive relationship to primary productivity (Grebmeier and Barry, 1991). From this discrepancy we suggest that the production pulse is too short and not spatially constrained to produce a significant impact within the polynya’s

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benthos and/or that the pulse is not great enough (relative to the large seasonal production of the Bering shelf) to be of ecological impact. Additionally, the birds may forage over distances that are greater than those of the polynya’s extent, thereby obscuring a relationship of polynya production and higher trophic level yield. 3.3

Ross Sea Polynya

As previously noted, the Ross Sea polynya is the locus for the Antarctic’s largest primary productivity in the Southern Ocean, with an annual production of 150 to 200 g C m−2 . Much of this production occurs in November and December and is concentrated in the south-central region (Arrigo et al., 1999). The distribution of planktivorous birds (Ainley and Jacobs, 1981) was shown to be a maximum at the continental shelf break and north of Ross Island. Interestingly, the minimum bird biomass (≤0.004 g wet weight m−2 ) was observed north of the Ross Ice Shelf and roughly parallel to the spatial dimension of the Ross Sea polynya in early December. The relationship between production (as determined by the climatological net community production averaged over the same spatial scales of the higher trophic level biomass—a 2◦ grid resolution) and bird biomass was negative, and the relationship with whale biomass was insignificant (the trend was also negative). When a similar analysis was completed for whale biomass and penguin biomass, a similar negative relationship was observed. While we do not suggest that this is a causal relationship, it does seem to indicate that the enhanced production is not transferred into the food web. There might be multiple reasons for this negative relationship. The first might be that the polynya’s large production is dominated by the colonial form of Phaeocystis antarctica, and mesozooplankton may not be capable of directly ingesting particles of this size and quality. Hence the biomass of P. antarctica may be partitioned between the water column (i.e., in situ microbial regeneration) and the benthos, where it is utilized after microbial colonization and degradation. The ecosystem model of Tagliabue and Arrigo (2003), which explicitly models interactions between phytoplankton and zooplankton, indicates that zooplankton may be unable to track the high growth rates of P. antarctica. Kemp et al. (2001) used a generic model of a plankton system and found that trophic efficiency (a measure that is related to yield at the highest trophic level) decreased with increasing nutrient loading, and suggested this was due to a saturation of the ability of zooplankton to utilize high standing stocks of phytoplankton. A further effect of this inability was to shunt much of the phytoplankton into the microbial food web where the material was oxidized (Kemp et al., 2001). A second potential explanation for the lack of a positive relationship between production and bird biomass is that the birds depend more of ice-related production than on that of the water column. Ice-algal production supports a separate and largely independent faunal community that is available to pelagic birds, and the regions with high bird biomass experience greater periods of ice cover than in the polynya. A third explanation might be that the continental shelf break is the sight of a major oceanographic front whose circulation influences the mesoscale distribution of prey for birds and whales. Additionally, whales forage over long distances, and so the “instantaneous” biomass distribution of such large animals may not reflect seasonal or shorter-term variations in phytoplankton. Finally, it should be noted that the bird distributions were assessed in January–February, and the distributions may reflect recent oceanographic and ice conditions rather than the influence of seasonal production. Whether a similar relationship would be found earlier in the season remains uncertain.


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4 Comparison of Arctic and Antarctic Polynyas As with many comparisons of biological processes between the Arctic and Antarctic, the observed differences often appear to be the result of large-scale differences in the physical forcing, and in the case of polynyas, this again seems to be the case. Antarctic polynyas are always characterized by elevated macronutrient concentrations (generated by the large-scale Antarctic divergence around the continent) that are rarely depleted to levels that might limit phytoplankton growth. In contrast, Arctic polynyas are much more heterogeneous with regard to nutrient levels, which can range from high in those influenced by Atlantic and Bering Sea waters to low levels in those influenced by Polar Surface Water. Seasonal productivity in these latter polynyas must remain low in the absence of wind-driven upwelling or erosion of the upper halocline. Antarctic polynyas also may exhibit signs of iron limitation (e.g., Ross Sea polynya; Sedwick and DiTullio, 1997; Olson et al., 2000), but Arctic polynyas would not be expected to do so, largely based on the shallow nature of the continental shelf (and hence to a source of iron from sediments) and the rates of atmospheric inputs (Duce and Tindale, 1991; Fung et al., 2000). The depth of mixing is also different between the two systems, as mixed layer depths (and nutrient supply) during winter are much deeper in the Antarctic than in the Arctic, where mixing is usually restricted by the presence of a permanent halocline (Muench, 1990). Riverine inflow in the Arctic is far greater than in the Antarctic, where the amount of glacial iron input is low, except in localized situations. Primary productivity, both on a daily and seasonal basis, appears to be greatest in the North Water and Ross Sea polynyas. However, the factors that are responsible for the large productivity are quite different. The North Water polynya is highly productive due to the influence of nutrient-replete waters, episodic vertical mixing and the restriction of ice cover by the ice barrier at the inflow of Smith Sound. In contrast, the productivity in the Ross Sea polynya is greater than in other regions largely because of the duration of the growing season, which allows biomass to accumulate. Few losses due to herbivorous ingestion occur, and growth proceeds until limitation by iron takes place. Net seasonal production in both is similar. The temporal pattern of the North Water polynya is more similar to the temperate North Atlantic, with spring and autumn blooms, whereas a unimodal pulse of production normally characterizes that of the Ross Sea (Figure 4), although some years exhibit biomodal peaks (Figure 5). Our analysis also suggests that the supply of sensible heat leads to an exceptionally early growth season. The phytoplankton bloom in the eastern North Water peaked two months after the spring equinox, whereas the delay was three months in the Ross Sea and four months in the western North Water and Northeast Water polynyas. The Ross Sea and the North Water are also quite different with respect to temporal patterns of vertical flux (Figure 7) and food web structure. In the Ross Sea polynya vertical flux is a function of two independent processes. The first is the sinking of biogenic matter (diatoms and/or organic matter associated with P. antarctica) that had aggregated into larger, more rapidly sinking particles (Dunbar et al., 1998; Asper and Smith, 2003), which occurs at the end of the growing season (Figure 6). This signal is reflected in a maximum flux of biogenic silica. The second process is the large carbon flux associated with the demise of the pteropod populations, an event which occurs much later in the year and quantitatively dominates the annual flux. In the North Water, peak fluxes of carbon and biogenic silica occur simultaneously soon after the main diatom outburst and the secondary fall bloom (Figure 3). The contribution of zooplankton detritus to the carbon flux increases during summer, but its importance is much less than in the Ross Sea and a long-delayed flux maximum is not

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Figure 7: Comparison of vertical particulate organic carbon (POC) fluxes between the Ross Sea and the North Water. The time series begins on the 10th day of summer, following the demise of the phytoplankton bloom. observed. Nevertheless, the two polynyas share a similar flux pattern for biogenic silica, which is driven by the passive sinking of diatoms and aggregates. Food web utilization of biogenic matter in the two polynyas seems to vary markedly. In the North Water zooplankton feed on the diatom-based phytoplankton, and in turn are fed upon by upper trophic levels. The end result appears to be a rather linear food chain, with the enhanced productivity in the polynya being efficiently transferred and resulting in enhanced trophic level biomass. In contrast, the substantial standing stocks of pelagic birds, penguins, whales and seals found in the Ross Sea may not be directly utilizing the polynya-derived primary productivity, as the correlation between upper trophic level biomass and polynya production is extremely poor. Unfortunately, few polynyas other than the North Water and Ross Sea polynyas have been studied in enough detail to make meaningful comparisons among their food webs, temporal patterns, and controls of export. Due to increased availability of light, phytoplankton are clearly more productive in polynyas than adjacent waters as long as the seasonal ice cover persists. But do annual productivity and the pathways of organic carbon flow differ in polynyas? Once seasonally ice-covered water open up, they become potentially as productive as nearby polynyas have been earlier in the year, provided they share similar loadings of macronutrients and iron. Although the definitive test is not available, the key elements of physical forcing and timing discussed in this book and elsewhere imply that polynyas confer high productivity to a region. Firstly, it has been shown that a longer open water period leads to greater productivity in the Arctic Ocean (Rysgaard et al., 1999) because phytoplankton deplete the initial stock of nutrients and then make prolonged use of those supplied by recycling and added by episodic


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inputs (e.g., diffusion, tidal mixing). A similar case can be made for the accumulation of biomass in the Ross Sea and its dependence on iron supply. Secondly, early-opening waters are generally subjected to relatively strong wind-driven mixing during late-winter and early spring, which can favor nutrient renewal without bloom suppression when buoyancyrestoring forces (e.g. sensible heat) keep phytoplankton above the critical depth. This process is especially important in the Arctic where nutrients otherwise are largely restricted to depths below the permanent halocline. At the other extreme, summer blooms associated with seasonal ice melt typically experience limited nutrient renewal due to strong stratification. A late opening date also limits the time during which phytoplankton benefit from nutrient renewal via diffusion and tidal mixing. One exception to this pattern occurs along productive, retreating ice edges, of which polynyas are considered a special case. The amount of primary production transferred toward upper trophic levels depends on the ability of pelagic herbivores to graze the phytoplankton. This ability depends on the palatability of the dominant species and/or the temporal match between primary and secondary producers. In the Ross Sea the strong contribution of large Phaeocystis colonies to the bloom may impede grazing; conversely, an uncoupling between phytoplankton and grazers may be caused by the fast growth rates of Phaeocystis (Tagliabue and Arrigo, 2003) or the low abundance of poised grazers. By virtue of their weak stratification and deep mixed layers, early-opening latent-heat polynyas should favor dominance by Phaeocystis since it may photosynthesize more efficiently than diatoms under low light. In this view, Phaeocystis should bloom first in all polynyas. Observations in the North Water do not support this notion because substantial departures from background biomass are mediated by diatoms despite large east-west differences in the initial depth of the mixed layer. These diatoms sustain the local herbivorous food web owing to the presence of winter-spawning copepods that are able to exploit the bloom despite its precocious development.

5 Summary and Conclusions Despite the large data suite gathered from a number of polynyas, much remains to be understood. It is clear that Arctic and Antarctic polynyas are different, but it is far from clear why these differences exist. Examples include food web structure, patterns of vertical flux, and the timing and variations of the pulses of production. Polynyas are convenient regions to study, as they are by definition physically bounded systems, but in situ studies are often difficult due to the difficulty of gaining access to these ice-surrounded regions. Hence, further studies of polynyas are warranted in order to build a better understanding of their biological dynamics, their variability over longer time scales (years to decades) and the food webs within them. These efforts will allow to further refine proficient ecosystem models (e.g. Arrigo et al., 2003; Worthen and Arrigo, 2003). Polynyas, by virtue of their physical attributes, may be sensitive indicators of regional and/or global climate change. In a warming scenario, established recurring polynyas may open earlier or expand, while new ones may appear elsewhere as the heat balance shifts or the ice fragments. In view of our analysis it is likely that this would lead to greater primary production, especially if late-winter storms erode the Arctic halocline and inject nutrients into surface waters. At the same time, however, the peripheral ice margins retreat and polynyas may eventually loose their integrity earlier in the year. Other polynyas whose existence is contingent on the formation of an ice bridge (e.g. the North Water) may no longer exist. Rapid changes in sea ice have been observed in some regions of the Arctic during the past 25

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years (Johannessen et al., 1999; ACIA, 2005) as well as in the Antarctica peninsula region (Larson ice shelf; Kwok and Comiso, 2002). Indeed, changes in ice concentration appear to be extensive over the entire Arctic Ocean, and hence it can reasonably be assumed that individual polynyas will exist strictu senso for shorter periods despite a longer overall period of open water. The net result of these processes is difficult to predict, but possible consequences include shifting productivity, assemblage composition and vertical flux patterns (both in time and space). Arctic regions where polynyas occur may in fact become more temperate than polar in nature. Unfortunately, models do not yet address the physical-biological interactions under conditions of large-scale climate change. In the Antarctic only one area (the Antarctic Peninsula) is experiencing rapid change, as evidenced by the marked decreased in ice concentrations (Kwok and Comiso, 2002) and the dramatic break-up of ice shelves (e.g., the Larson Ice Shelf). This is not necessarily an indication of global climate change, as other parts of the Antarctic (such as the Ross Sea sector) presently are increasing in ice concentrations (Kwok and Comiso, 2002). However, any small, localized polynya in the Peninsula region would presumably be impacted in a manner similar to that of the Arctic: shortened period of integrity, decreased polynya-related production, and a shift to more open water conditions. In the Ross Sea it might be expected that the increased ice cover will restrict the spatial extent of polynya growth, but unless substantial reductions in the seasonal ice coverage occur, will not greatly restrict the annual production. However, because the stratification in the Ross Sea might be reduced under colder conditions (e.g., a mixed layer depth of 30 vs. 20 m), the phytoplankton may become dominated by Phaeocystis rather than diatoms (Arrigo et al., 1999; Smith et al., 2000b). Over longer time periods when stratification is expected to increase (Sarmiento et al., 1998), the assemblage composition may shift from Phaeocystis to diatoms. Changes such as these (Zmix depths) are quite subtle, and cannot be replicated within regional or large-scale models accurately, which in turn implies that many of the biological effects of regional or global climate change may not be predictable using today’s present models. Indeed, modeling the intra-Antarctic climate changes is challenging. Emerging paradigms on the ecology of the different bloom-forming taxa need to be refined, as they do not fully reconcile major observations. Based on reports that Phaeocystis photosynthesize very efficiently at low irradiance in the Arctic (Cota et al., 1994) and the Antarctic (Moisan and Mitchell, 1999), one might expect systematic, initial blooms of the former in early-opening polynyas. So why do diatoms dominate all bloom assemblages in Arctic polynyas studied so far despite large differences in their physical generation and mixing regimes? Why did Phaeocystis not bloom in the Ross Sea during 2003–4? These questions are crucial given the potential importance of species composition on trophic transfers and vertical fluxes. It could be that large changes in iron supply, top-down effects (e.g. differential grazing of microheterotrophs on solitary Phaeocystis cells), supply of seed stock from the ice, or nutrient ratios supersede the role of photosynthetic adaptations in influencing the success of different phytoplankton species. Another puzzling aspect of the carbon dynamics of polynyas is that despite large differences in physical forcing, taxonomic composition and temporal patterns, the efficiency of their biological pump is very low and similar. Estimates of the proportion of the annual particulate primary production intercepted by traps at 200 m are less than or equal to 3%. Even if the sinking fluxes were somewhat underestimated by advective losses or trapping biases, it is clear that most of the phytoplankton production is transformed and retained in surface waters, and that polynyas are not efficient vectors of particulate matter transfer to deep waters. These findings suggest that the positive relationship between pulsed diatom production


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at high latitudes and export efficiency (Buesseler, 1998; Ducklow et al., 2001) may not apply to polynyas. How the export efficiency of polynyas and polar systems in general respond to climate variability and change is presently unknown. Phytoplankton processes of polynyas could be monitored as indicators of large-scale change at the ecosystem level. Successful monitoring can be achieved with remote sensing in conjunction with in situ process studies and automated observatories. Pigments can be monitored from space, and so a remote and rapid tool for change is already available. Various aspects of phytoplankton ecology and their fate in the ecosystem can be monitored using moorings (e.g., physiological state, assemblage composition, nutrient concentrations, vertical distributions, sinking fluxes, etc.), and the data potentially relayed to a land-based location for analysis; hence, the “state of the polynya” can be remotely assessed and monitored over long time periods, thereby providing a record by which change can be measured. Comprehensive process studies, although limited for logistic reasons, will cement the different pieces together and lead to predictive models. Understanding these important systems is crucial as an oceanographic window into change, and it is a tremendous challenge to synthesize our knowledge and derive a means by which further large-scale changes can be recognized, quantified and predicted.

Acknowledgements This work was supported by a NCE grant (Arcticmet) to JET and NSF grants OPP-0087401 and OPP-0337247 to WOS. We thank Michael Dinniman for his help with the Ross Sea climatology, Jill Peloquin with Figure 1, and David Ainley for discussions of the fate of production in the Ross Sea polynya.

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Stenhouse, I.J., Montevecchi, W.A., 1996. Winter distribution and wrecks of Little Auks (Dovekies) Alle a. Alle in the Northwest Atlantic. Sula 10, 219–228. Stirling, I., 1997. The importance of polynyas, ice edges, and leads to marine mammals and birds. Journal of Marine Systems 10, 9–21. Stirling, I., Cleator, H., Smith, T.G., 1981. Marine mammals. In: Stirling, I., Cleator, H. (Eds.), Polynyas in the Canadian Arctic. Occasional paper 45, Ottawa, Canadian Wildlife Service, pp. 45–58. Stringer, W.J., Groves, J.E., 1991. Location and areal extent of polynyas in the Bering and Chukchi Seas. Arctic 44, 164–171. Tagliabue, A., Arrigo, K.R., 2003. Anomalously low zooplankton abundance in the Ross Sea: an alternative explanation. Limnology and Oceanography 48, 686–699. Tortell, P.D., DiTullio, G.R., Sigman, D.D., Morel, F.M.M., 2002. CO2 effects on taxonomic composition and nutrient uptake in an Equatorial Pacific phytoplankton assemblage. Marine Ecology Progress Series 236, 37–43. Touratier, F., Legendre, L., Vezina, A., 2000. Northeast Water Polynya 1993: Construction and modelling of a time series representative of the summer anticyclonic gyre pelagic ecosystem. Journal of Marine Systems 27, 1–3. Tremblay, J.E., Gratton, Y., Carmack, E.C., Payne, C.D., Price, N.M., 2002a. Impact of the large-scale Arctic circulation and the North Water Polynya on nutrient inventories in Baffin Bay. Journal of Geophysical Research 107, doi:10.1029/2000JC000595. Tremblay, J.E., Gratton, Y., Fauchot, J., Price, N.M., 2002b. Climatic and oceanic forcing of new, net and diatom production in the North Water Polynya. Deep-Sea Research II 49, 4927–4946. Tremblay, J.E., Michel, C., Hobson, K.A., Gosselin, M.G., Price, N.M., 2006a. Bloom dynamics in early-opening waters of the Arctic Ocean. Limnology and Oceanography 51, 900–912. Tremblay, J.E., Hattori, H., Michel, C., Ringuette, M., Mei, Z.-P., Lovejoy, C., Fortier, L., Hobson, K.A., Amiel, D., Cochran, K., 2006b. Trophic structure and pathways of biogenic carbon flow in the eastern North Water Polynya. Progress in Oceanography 71, 402–425. Vaillancourt, R.D., Sambrotto, R.N., Green, S., Matsuda, A., 2003. Phytoplankton biomass and photosynthetic competency in the summertime Mertz Glacier Region of East Antarctica. Deep-Sea Research II 50, 1415–1440. van Hilst, C.M., Smith, W.O. Jr., 2002. Photosynthesis/irradiance relationships in the Ross Sea, Antarctica and their control by phytoplankton assemblage composition and environmental factors. Marine Ecology Progress Series 226, 1–12. van Woert, M.L., 1999. Wintertime dynamics of the Terra Nova Bay polynya. Journal of Geophysical Research 104, 7753–7769. Williams, G.D., Bindoff, N.L., 2003. Wintertime oceanography of the Adelie Depression. Deep-Sea Research II 50, 1373–1392. Worthen, D.L., Arrigo, K.R., 2003. A coupled ocean-ecosystem model of the Ross Sea. Part 1: Interannual variability of primary production and phytoplankton community structure. In: DiTullio, G.R., Dunbar, R.B. (Eds.), Biogeochemistry of the Ross Sea. In: Antarctic Research Series, vol. 78. Washington, DC, pp. 93–105. Yang, J., Comiso, J., Walsh, D., Krishfield, R., Honjo, S., 2004. Storm-driven mixing and potential impact on the Arctic Ocean. Journal of Geophysical Research 109, doi:10.1029/2001JC001248.


Chapter 9

Zooplankton Processes in Arctic and Antarctic Polynyas D. Deibel1 and K.L. Daly2 1 Ocean Sciences Centre and Department of Biology, Memorial University, St. John’s,

Newfoundland and Labrador, A1C 5S7 Canada 2 College of Marine Science, University of South Florida, 140 Seventh Ave. S., St. Petersburg, Florida, 33701 USA

Abstract There are various similarities and differences in zooplankton processes between Arctic Ocean (AO) and Southern Ocean (SO) polynyas, many of which are due to fundamental differences in their respective ecosystem properties. The composition of zooplankton communities in AO and SO polynyas is largely dependent upon advection from local, ice-covered waters, with little evidence of an endemic, polynya zooplankton fauna. While copepods are common in both systems, a major difference is the predominance of euphausiids in the SO and appendicularian tunicates in the AO. The same genera of small copepods occur in both the AO and SO and appear to derive little benefit from the higher primary productivity and extended growing season of polynyas. In contrast, larger calanoid copepods appear to derive recruitment and life cycle benefits from the diatom production and heat in polynyas, with higher egg production rates and shorter generation times. Most large calanoid copepods overwinter in diapause in AO polynyas, while some proportion of SO populations remain in surface waters. Grazing impact by copepods in AO polynyas accounts for about 20% of primary productivity d−1 , with appendicularian tunicates accounting for another 20% d−1 . The few estimates of community impact in the SO are variable. In both regions, individual zooplankton feeding rates are high and equivalent to boreal ocean values; thus, grazing impact depends primarily on the biomass of zooplankton and phytoplankton. SO zooplankton contribute to the vertical particulate flux through faecal pellets from euphausiids, copepods and pteropods, while the contribution in AO polynyas is primarily through appendicularian tunicate faecal pellets and shed houses and copepod faeces. Maximum pellet flux in both the AO and SO occurs at times of high biomass of diatoms. The primary benefits of polar polynyas to zooplankton processes results from the greater production of diatoms and extended productive period, with few differences in individual daily rations or food web transfer efficiencies relative to temperate and boreal systems.

Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74009-0

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1 Introduction Polynyas are areas of open water in the midst of ice-covered seas. However, the physical, chemical and biological dynamics of individual polynyas vary. They differ in their interannual persistence, in the time of opening and closing, and in the maximum area of open water (Barber and Massom, 2007). Polynyas are strongly controlled by physical processes, with steep temporal and spatial gradients of chemical and biological properties. There are several ways in which reduced ice cover can force variability in biological responses, including changing levels of irradiance and rates of upwelling of heat and nutrients, and changes in the timing and intensity of mixing and stratification (Ingram et al., 2002). Polynyas occur within a continuum, from those existing for long periods of time to briefly-open polynyas that are difficult to distinguish from surrounding ice-covered seas. The four most studied polynyas in the Arctic Ocean (AO) span a three-fold range in days open per year, from more than 300 days (d) for the St. Lawrence Island Polynya (SLIP), to ca. 100 d for the Northeast Water Polynya (NEW), only slightly longer than the 45–90 d of open water in the nearby Barents Sea (Hirche and Kosobokova, 2003). In general, shelf areas within the Canadian archipelago are ice free for ca. 40 d each year (Ringuette et al., 2002). In the Southern Ocean (SO) the largest, deep-ocean polynya, the Weddell Sea polynya, results from deep convective warming of surface waters over a seamount, while another has been detected in the Cosmonaut Sea (Comiso and Gordon, 1987). Arrigo and van Dijken (2003) detected 52 coastal, winter polynyas using satellite images (SSM/I) of sea ice distribution between June 1997 and May 2002. Some of these consisted of adjacent polynyas that formed a single larger polynya during spring, yielding a total of 37 polynyas that persisted into spring or summer. Twenty-eight of the 37 polynyas were observed during each year of the study.

2 Zooplankton in Arctic Ocean Polynyas 2.1

Species Composition and Abundance

A complete species list of mesozooplankton from AO polynyas is unavailable. This makes regional comparisons of the mesozooplankton community of polynyas and their surrounding, ice-covered waters difficult. Nevertheless, we know that the species composition of the zooplankton community of various Arctic polynyas shares similarities, but that there are also important differences that are related to advection of fauna from nearby water masses. The most complete species list for any Arctic polynya has been compiled for the Northwater Polynya (NOW), consisting of 20 species of crustaceans (Ringuette et al., 2002). The most abundant zooplankton in the NOW are Oithona similis, Metridia longa, Oncaea borealis, Pseudocalanus spp., Microcalanus pygmaeus, Calanus hyperboreus, C. glacialis, Oikopleura spp., and C. finmarchicus (Table 1). This community composition reflects the dual source waters of the NOW (Atlantic waters of the West Greenland Current containing C. finmarchicus, and Arctic water originating from the Nansen and Amundsen basins of the AO). C. glacialis and Fritillaria borealis are considered to be endemic to the Arctic, while Pseudocalanus minutus is characteristic of polar coastal waters (Richter, 1994). The NOW and the Greenland Sea share 4 of the 5 most abundant zooplankton species, while the Arctic, neritic species C. glacialis is abundant in the NOW but not reported from the Greenland Sea. The NOW also shares zooplankton with surface and mid-depth waters of the AO, where Polar Surface Water (PSW) is typified by O. similis, M. pygmaeus and P. minutus (Auel

Zooplankton Processes in Arctic and Antarctic Polynyas

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Table 1: Abundance of zooplankton species (# of animals m−2 ) in the North Water Polynya (NOW) and regional seas. NOW East = eastern stations along the Greenland coast, NOW West = western stations along the coast of Ellesmere Island. Single values are means or medians, either directly from the published paper or taken by eye from published figures. Rare species are not included. NR = species not reported Species & taxa Copepoda Calanus finmarchicus C. glacialis C. hyperboreus Chiridius obtusifrons Paraeuchaeta spp. Gaidius spp. Heterohabdus spp. Metridia longa Microcalanus pygmaeus Pseudocalanus elongatus P. minutus Pseudocalanus spp. Scaphocalanus brevicornis Scolecithricella minor Spinocalanus spp. Temorites brevis Xanthocalanus borealis Oithona similis Oncaea borealis Harpacticoida Tunicata Oikopleura vanhoeffeni Oikopleura spp.

NOW East1 793 2112 3523 20 119 266 152 8990 4941 NR NR 5203 79 42 94 NR NR 14,188 7981 39 NR 40– 11,0003

NOW West1

Barrow Strait1

1170 NR 3143 1005–2394 4399 601–1210 54 0–249 180 10–25 97 NR 39 NR 5940 1251–1485 3532 3201–3518 NR NR NR NR 5165 12,124–700,0004 38 NR 18 0–15 107 NR 3 NR 9 NR 7395 6642–7811 2885 5240–10,005 85 0–117 NR 40– 11,0003

NR 1000

Nansen Basin

Greenland Sea2

25005 , 18526 , 36007 3005 2005 , 11007 NR NR NR NR 10005 , 25007 12007 13007 NR 21,000–12,50008 NR NR NR NR NR 15007 NR NR

2000–6800 NR 3000–18,000 NR NR NR NR 3000–9000 NR NR 7000–30,000 NR NR NR NR NR NR 80,000–480,000 35,000–110,000 NR

6007 NR

NR NR

1 Ringuette et al. (2002). 2 Richter (1994). 3 Acuña et al. (2002). 4 Hattori and Saito (1997). 5 Mumm et al. (1998). 6 Auel and Hagen (2002). 7 Mumm (1993). 8 Hanssen (1997).

and Hagen, 2002), and where the large C. hyperboreus, C. glacialis and M. longa dominate biomass. Barrow Strait (BS) is a second ice-covered region close to the NOW, which is isolated from direct contact with the Atlantic Ocean, being most affected by outflow from the Amundsen and Canada Basins. The BS is ice-covered for most of the year, having an ice-free period of less than 70 d (Ringuette et al., 2002). Calanus finmarchicus is not reported here (Table 1), in accordance with the view that this species does not reproduce in the AO (Conover and Huntley, 1991; Hirche and Kosobokova, 2003). However, the numerically dominant species in the BS are similar to those in the NOW (Table 1). The primary difference is the predominance of Pseudocalanus spp. and relatively low abundance of M. longa in the BS. This is likely due to the adaptation by Pseudocalanus spp. to feed on epontic algae before ice melt (Conover et al., 1986) and to the relative shallowness of the BS, providing insufficient deepwater habitat for M. longa. In addition, the BS appears to support higher abundances of the

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Table 2: Abundance of zooplankton species (# of animals m−2 ) in the Northeast Water Polynya (NEW) in comparison to regional seas. NEW North = the northern polynya including the Westwind and Northern troughs, NEW Central = the central polynya with open water, NEW South = southern polynya including the ice-covered, Southern Trough. For comparison to the upstream Nansen Basin of the Arctic Ocean see Table 1. Rare species not included. NR = species not reported Species & taxa

NEW South1

NEW Central1 NEW North1

East Greenland Current2

Copepoda Calanus finmarchicus C. glacialis C. hyperboreus Metridia longa Microcalanus pygmaeus Pseudocalanus spp. Scolecithricella minor Oithona similis Oncaea borealis Harpacticoids

500–11,000 120–2900 300–3100 600–1800 1000–3000 180–4000 10–1600 22,000–75,000 100–13,000 50–500

0–180 0–950 0–800 50–3100 400–4100 20–850 10–500 0–25,000 1500–10,000 0–120

3555–12,373 30–554 260–2064 NR NR NR NR NR NR NR

Tunicata Appendicularians Fritillaria borealis Oikopleura spp. Chaetognatha Ostracoda Pteropoda Polychaeta

1000–20,000 950–19,000est 50–1000est 150–1000 50–100 0–2000 0–500

0–2300 1000–16,000 200–7900 0–2800 50–3000 620–6800 0–400 1000–35,000 500–8000 0–1100

1000–6000 950–5700est 50–300est

5000–250,000 47,500–237,000est 2500–13,000est

NR NR NR

50–1300 10–5000 510–200 0–50

100–1300 10–1000 50–5200 0–15,000

NR NR NR NR

1 Ashjian et al. (1995, 1997). 2 Hirche et al. (1991). est Values estimated, see text.

appendicularian Oikopleura vanhoeffeni than does the NOW (Table 1). In summary, the zooplankton community of the NOW is most like that of the Nansen Basin (Table 1), reflecting the importance of the inflow of Arctic water (Tremblay et al., 2002). In general, the NOW supports higher abundances of zooplankton than regional, ice-covered waters, particularly C. glacialis, C. hyperboreus and Fritillaria borealis. The Northeast Water Polynya (NEW) has a more ’Atlantic’ zooplankton community. The most abundant species are Fritillaria borealis, Oithona similis, Calanus glacialis, polychaete larvae, Oncaea borealis, Calanus hyperboreus and pteropods (Table 2). Microcalanus pygmaeus, C. finmarchicus and Oikopleura spp. are also abundant. All taxa are relatively rare in the central NEW, but are more abundant downstream. M. longa is the dominant copepod species in the central NEW, and C. glacialis is dominant in the north. C. finmarchicus is most abundant in the ice-covered, southern NEW, which is most heavily influenced by water of Atlantic origin. Thus, the central NEW supports significantly higher densities of C. glacialis, and lower densities of C. finmarchicus, than do regional, ice-covered waters. This is generally similar to the case of the NOW. As was true in the NOW, the density of the cyclopoid copepod O. borealis does not seem to be affected by the presence of the NEW. Understanding of appendicularian ecology in polynyas is limited by taxonomic uncertainties. For example, Ashjian et al. (1997) presented total appendicularian numbers for the NEW, which at several stations were very high (250,000 animals m−2 ). Recounting of some


275

Table 3: Abundance of zooplankton species (# of animals m−2 ) in the Laptev Sea Polynya and upstream Barents Sea. Rare species not included. NR = abundance of species not reported in primary citations Species & taxa

Laptev Sea

Barents Sea

Copepoda Calanus finmarchicus C. glacialis C. hyperboreus Drepanopus bungei Pseudocalanus spp. Oithona similis Harpacticoids

NR 0–150,0003 NR 250–55004 2000–31,0005 NR NR

2417–13,3681 , 10,000–100,0002 138–12621 , 0–80002 645–12,1291 , 10–9002 NR NR 400,000–600,0006 3000–10,0006

Tunicata Fritillaria borealis Oikopleura spp.

1–13005 3–9605

0–130,0006 NR

1 Hirche et al. (1991). 2 Hirche and Kosobokova (2003). 3 Kosobokova and Hirche (2001). 4 Abramova (1999). 5 Hanssen (1997). 6 Arashkevich et al. (2002).

samples indicated that is greater than 99% of the appendicularians were small Fritillaria borealis (Deibel and Lee, unpubl.). This is similar to other reports from the AO, which indicate that >90% of appendicularians are generally fritillarids (Kosobokova and Hirche, 2001). In Table 2 we estimate the abundance of F. borealis and Oikopleura spp. by assuming that 95% of the counts of Ashjian et al. (1997) were fritillarids. The Laptev Sea Polynya (LSP) is a flaw polynya created just west and north of the New Siberian Islands, which is influenced by runoff from the Lena River (Kosobokova et al., 1998). The zooplankton community is more neritic in character than that of the NOW or NEW. Copepods are the most abundant group in spring and summer, with chaetognaths ranking second (Table 3). Drepanopus bungei, Acartia spp., Calanus glacialis, C. finmarchicus and Pseudocalanus spp. dominate at shelf stations (Abramova, 1999), while slope and deep stations are dominated by C. glacialis, C. finmarchicus, C. hyperboreus and Metridia longa (Kosobokova et al., 1998). The presence of C. finmarchicus indicates the influence of Atlantic Water from the Barents Sea. However, it is unlikely that C. finmarchicus reproduces in the Nansen Basin or in the LSP (Kosobokova and Hirche, 2001). Fritillaria borealis and Oikopleura spp. are found at all stations on the Laptev Shelf, regardless of depth (Table 3), suggesting that they may be broadly distributed in the AO. The LSP supports orders of magnitude greater densities of Calanus glacialis than the surrounding waters of the Barents Sea and Nansen Basin (Table 3). As a result, it has been suggested that Eurasian polynyas generally, and the LSP specifically, serve as nursery areas for C. glacialis (Kosobokova and Hirche, 2001). Although the NEW, NOW and LSP are vastly different in bathymetry and hydrography, they all have high densities of C. glacialis. The St. Lawrence Island Polynya (SLIP) is fundamentally different from the other polynyas, as it is surrounded by ice only 2–3 months of the year. A branch of the Anadyr Current flows into the SLIP bringing high levels of inorganic nutrients onto the shallow continental shelf south of St. Lawrence Island (Springer et al., 1989). As a result, the rates of primary production in the SLIP are 5–10 times higher than in surrounding waters.

276


Table 4: Mean areal abundance (# m−2 ) of zooplankton in the St. Lawrence Island Polynya (SLIP) and regional seas. For some taxa range is in parentheses. Stratified vertical tows to near bottom with 100 μm mesh (this paper). n = 13 stations. NR = species not reported. Species occurring in low numbers are not shown Species & taxa

SLIP

Bering Slope

Bering Shelf

Anadyr Strait

Shpanberg Strait1

Copepoda Calanus marshallae Neocalanus plumchrus N. cristatus Eucalanus bungeii Metridia pacifica M. norvegica Pseudocalanus spp. Microcalanus pygmaeus Acartia longiremis Scolecithricella minor C. abdominalis Oithona similis O. conifera Microsetella norvegica

13,251 356 8 70 16,073 1189 245,348 6810 8893 74 20 141,601 29,972 NR

30,953 2136

11,253

95,090 NR 170,452 39,450 1404 NR NR 237,965 162,033 3476

551 NR 282,190 592 6041 NR NR 128,139 3920 785

9859, 10001 40001 151 30001 30001 NR 275,180, 30,0001 27 16,598, 10001 NR 10001 102,351, 20,0001 2674 1179

15,000 NR NR NR 1000 NR 40,000 NR 10,000 NR NR 5000 NR NR

Tunicata Total appendicularia Oikopleura vanhoeffeni O. labradoriensis Fritillaria borealis

NR 48,195 30 90

NR 227 99 154

NR 36,002 NR 1841

35001 83,595 NR 66

500 NR NR NR

7323 (530–29,825)

NR

NR

NR

NR

755

12,647

8246, 2001

60

173

6058

5001

80

NR NR

NR NR

NR NR 375

NR NR 19,303

5001 NR 20001 NR NR 63,605

50 NR 50 NR NR NR

NR

227

14,297

NR

1254

2003

16,997

NR

Chaetognatha Total chaetognaths Parasagitta elegans Cnidaria Euphausidae Amphipoda Pteropoda Ostracoda Decapoda Trochophora Balanidae Polychaeta

3398 (0–28,004) 89 (0–1391) 75 (0–268) 48 (0–285) 16 (0–175) 160 (0–1189) 31,889 (0–136,297) 9750 (0–38,238) 8862 (646–30,701)

1 Springer et al. (1989).

The zooplankton community is dominated by copepods, followed by appendicularians and trochophore larvae (Table 4). The coastal nature of the SLIP area is characterized by the dominance of Pseudocalanus and Acartia, and its cold, productive nature is character-


277

ized by the predominance of O. vanhoeffeni, at abundances greater than any of the other polynyas reviewed here. Composition of the zooplankton community in the SLIP is most like that of the Bering Sea shelf and Anadyr water (Table 4). For example, the three most abundant species had the same rank order in the Bering shelf, Anadyr Current and SLIP stations (Pseudocalanus spp., Oithona similis and O. vanhoeffeni). 2.2

Individual and Community Biomass

Individual dry weights (DW) of copepods in the central NEW are generally lower than is typical of the same species in regional waters in mid-summer, while DW’s to the north and south of the main polynya area are generally higher (Ashjian et al., 1995, Table 5). It is not known whether these differences reflect variations in the reproductive status of populations inside and outside of the polynya, and/or poor nutritional conditions within the polynya (Daly, 1997). Although the central NEW supports high densities of Metridia longa, body size of the animals is not exceptionally large in a regional context. In the NOW the DW’s of Calanus hyperboreus copepodite V’s range from 2.4 to 3.6 mg (Table 5). C. glacialis CV (1.0–1.4 mg) and Metridia longa females (0.4–0.5 mg) weigh considerably less (Stevens et al., 2004a). There is a spatial pattern in DW, with the two calanoids generally larger in the east, and M. longa larger in Arctic-influenced waters (Table 5). The DW of CV C. hyperboreus in the NOW is similar to that in the southern NEW but 2–3 times greater than in the central and northern NEW; additional evidence of the unfavorable food environment for this large copepod in the NEW generally. Appendicularians are abundant in the NOW, showing large spatial variability in body size, abundance and biomass. Oikopleurid appendicularians (primarily O. vanhoeffeni) range in size from less than 0.5–4.8 mm, with an inverse relationship between body size and abundance (Acuña et al., 2002). The population is dominated by small animals above the pycnocline (mean length = 0.26 mm) and by larger animals below (mean = 2.52 mm) (Deibel et al., unpubl.). There is considerable plasticity in body size of appendicularians throughout an annual cycle, making comparison among studies difficult. In Newfoundland waters, size-at-maturity of adult O. vanhoeffeni is greater in the spring when food is abundant, suggesting they may respond to phytoplankton production during the spring bloom (Choe and Deibel, unpubl.). The mean trunk length of O. vanhoeffeni in the NOW during summer is 0.94 mm, similar to the summer mean in Newfoundland of 0.6 mm. This indicates that the NOW (75◦ N) provides as suitable a habitat for O. vanhoeffeni as does the Labrador Current off Newfoundland (47◦ N). The favorable food environment provided by the SLIP is evident in the mean body size of O. vanhoeffeni (3.9 mm), similar to the mean in Newfoundland waters during the spring bloom (3.5 mm). Of the polynyas reviewed here, the SLIP has the highest zooplankton biomass and the LSP the lowest (Figure 1). Total zooplankton biomass in the SLIP ranges from 1.5– 7.4 g C m−2 , with Oikopleura spp. forming 88% of the total. Pseudocalanus spp. dominates the copepod fraction, followed by Calanus marshallae and Acartia spp. Surrounding waters support higher biomass of copepods (1–4 g C m−2 ) but much lower biomass of appendicularians (Shiga and Deibel, unpubl.). The zooplankton biomass in the SLIP is similar to that of the Nansen Basin and central Arctic Ocean, but is 50% less than that of the open waters of the Greenland and Barents seas (Figure 1). Zooplankton biomass in the NOW ranges from 0.1–7.0 mg C m−2 , with higher values in waters influenced by the West Greenland Current (Figure 1). Biomass is dominated in the east by Calanus finmarchicus and C. glacialis, and in the west by C. glacialis and C. hyperboreus (Ringuette et al., 2002). Total biomass in the eastern NOW is much higher than in

278


Table 5: Dry weight and relative total lipid content of mesozooplankton in Arctic polynyas and surrounding ice-covered waters. Modified and amended from S. Smith and SchnackSchiel (1990). Only those stages appear in this table which have matching data from at least one of the polynyas reviewed here. ‘?’ indicates that data were not available in the original reference Species and stage

Location

Month

Dry weight (μg)

Lipid (% DW)

Reference

Calanus glacialis adult female

NEW south NEW central NEW central NEW north Greenland Barrow Strait Arctic T-3 Arctic T-3 Fram Strait Barents Sea Barents Sea Barents Sea Barrow Strait NOW east NOW west Barrow Strait Barents Sea Barents Sea NEW south NEW south NEW NEW NEW north NOW NOW Greenland Jones Sound Barrow Strait Fram Strait Fram Strait Barents Sea ? Arctic T-3 Arctic T-3 NEW south NEW north NOW east NOW west Jones Sound Barrow Strait Fram Strait Fram Strait Barents Sea

July–Aug July–Aug Aug July–Aug May–Aug April–Sept ? ? summer ? June–July ? April–Sept Aug–Sept Aug–Sept April–Sept ? June–July July–Aug Aug July–Aug Aug July–Aug April July May–Aug July–Aug April–Sept summer August ? ? June August July–Aug July–Aug Aug–Sept Aug–Sept July–Aug April–Sept summer August ?

1146–1152 770 680–980 1009–1252 810 851 440 714 533 810 960 600 711 1040–1190 1120–1390 547 600 618 4143–5416 3730–3830 2007 1190–2650 3083–6018 ? ? 1800 4067 (max) 3769 3168 3600–4300 2350 1893 1380 (min) 2300 (max) 3111 1563–1776 2380–3570 2930–3050 2736 (max) 1361 1920 2000 1000

37–43 36 ? 42–46 ? ? 50 56 35 ? 24 ? ? 51–58 32–51 ? ? 48 53–57 ? 44 ? 51–60 19 43 ? 71 ? 34 36–46 ? 26 29 74 57 57–60 46–70 50–61 64 ? 38 41–45 ?

Ashjian et al. (1995) Ashjian et al. (1995) Daly (1997) Ashjian et al. (1995) Daly (1997) Conover and Huntley (1991) Lee et al. (1971) Lee (1975) S. Smith (1990) Hirche and Kosobokova (2003) Tande and Henderson (1988) Hirche and Kosobokova (2003) Conover and Huntley (1991) Stevens et al. (2004a) Stevens et al. (2004a) Conover and Huntley (1991) Hirche and Kosobokova (2003) Tande and Henderson (1988) Ashjian et al. (1995) Daly (1997) Ashjian et al. (1995) Daly (1997) Ashjian et al. (1995) Fisk et al. (2001) Fisk et al. (2001) Daly (1997) Head and Harris (1985) Conover and Huntley (1991) S. Smith (1990) Auel et al. (2003) Hirche and Kosobokova (2003) S. Smith (1990) Lee (1974) Lee (1974) Ashjian et al. (1995) Ashjian et al. (1995) Stevens et al. (2004a) Stevens et al. (2004a) Head and Harris (1985) Conover and Huntley (1991) S. Smith (1990) Auel et al. (2003) Hirche and Kosobokova (2003)

male male CV CV CV CV CV C. hyperboreus adult female

CV CV CV CV CV CV CV CV CV

(Continued on next page)


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Table 5: (Continued) Species and stage Metridia longa adult female

Location

Month

Dry weight (μg)

Lipid (% DW)

Reference

NEW NEW north NOW east NOW west Barrow Strait Arctic T-3 Arctic T-3 Norway Norway

July–Aug July–Aug Aug–Sept Aug–Sept April–Sept ? Oct Oct March

368 387 370–450 460–520 324 176 ? ? ?

32 30 11–30 33–35 ? 34 57 84 (max) 27 (min)

Ashjian et al. (1995) Ashjian et al. (1995) Stevens et al. (2004a) Stevens et al. (2004a) Conover and Huntley (1991) Lee (1974) Falk-Petersen et al. (1987) Falk-Petersen et al. (1987) Falk-Petersen et al. (1987)

Figure 1: Total zooplankton biomass in three arctic polynyas relative to surrounding icecovered waters. Original units in cited papers were often dry weight. DW was converted to units of carbon by multiplying by 0.5. Black dots indicate maximum values of biomass. Data from Conover and Huntley (1991) (Barrow Strait), Kosobokova et al. (1998) (Laptev Sea Polynya), Saunders et al. (2003) (NOW west, NOW east), Mumm et al. (1998) (Central Arctic Ocean Upper, Greenland Sea Upper), Ashjian et al. (1995) (NEW north, NEW central, NEW south), Hopkins (1969), S. Smith (1988) and Hirche et al. (1991) (East Greenland Current), Mumm (1993) (Nansen Basin Upper), Shiga and Deibel (unpubl.) (SLIP), Kosobokova and Hirche (2001) (Central Arctic Ocean), Arashkevich et al. (2002), Sato et al. (2002) and Hirche and Kosobokova (2003) (Barents Sea), Richter (1994) (Greenland Sea). Barrow Strait (3.2 vs. 0.3 g C m−2 , Figure 1). Although mean biomass in the eastern NOW is nearly equal to that in the SLIP (Figure 1), the composition of the community is different, with appendicularians dominant in the SLIP and copepods dominant in the eastern NOW.

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Zooplankton biomass in the NEW is spatially variable, with highest values in the southern NEW, dominated by Calanus finmarchicus (3.3 g C m−2 ) and lower values in the central and northern NEW (1.3 g C m−2 ) (Figure 1). Biomass in the central NEW is dominated by Metridia longa and in the northern NEW by C. glacialis (Ashjian et al., 1995). The NEW does not support unusually high zooplankton biomass in a regional context (Figure 1). If one assumes that ca. 65,000 m−2 of the appendicularians in the NEW were F. borealis weighing 1–2 μg C animal−1 , and that the remaining animals were juvenile Oikopleura spp. of the small size found in other Arctic polynyas and weighing 3.8 μg C animal−1 (Deibel et al., 2005), appendicularian biomass is ca. 0.09 g C m−2 , or about 3% of copepod community biomass. This is within the range of estimates of oikopleurid appendicularian biomass from Young Sound, eastern Greenland, of 0.04–0.10 g C m−2 (Rysgaard et al., 1999). The same assumptions applied to the central and southern NEW result in biomass estimates of 0.01– 0.02 g C m−2 , ca. 0.5% of copepod biomass. This compares with appendicularian biomass estimates made by Pesant et al. (1998, 2000) of 5–10 g C m−2 , based upon a misinterpretation of the data presented by Ashjian et al. (1995). Total copepod biomass in the LSP ranges from 0.05–0.75 g C m−2 , much lower than either the NEW or the eastern NOW, but similar to the Barrow Strait and Arctic-influenced NOW (Figure 1). Biomass is dominated by Drepanopus bungeii and Pseudocalanus on the shelf, and by Calanus hyperboreus and C. glacialis beyond the shelf break. Although the LSP is influenced by inflow from the Barents Sea, it supports much less biomass (0.2–10 g C m−2 for the Barents Sea). 2.3 2.3.1

Individual Feeding Rates, Diet and Community Grazing Individual Feeding Rates

There has been considerable work done on individual feeding rates of copepods and appendicularians in Arctic polynyas, which are as high as those in boreal and temperate latitudes, meaning that population grazing impact is primarily a function of mesozooplankton biomass (Saunders et al., 2003). In the NEW, gut chlorophyll content (a proxy of ingestion rate) of oikopleurid appendicularians increases with increasing body size and decreases both with increasing total chlorophyll and with increasing proportions of large phytoplankton (Acuña et al., 1999). 43% of the total variability in ingestion rate is accounted for by body size and the proportion of large phytoplankton. The authors assume the negative relationship with the biomass of large phytoplankton was due to clogging of the inlet filters of the house by the large cells. The mean ingestion rate is 23 μg C d−1 and the carbon-specific daily ration is 117% d−1 . Highest faecal pellet production rates (directly related to ingestion rate) for Calanus hyperboreus are in May in the southern NEW, and lowest rates are in July in the northern NEW (Daly, 1997). Carbon-specific egestion rates of faecal pellets are ca. 7% d−1 for C. hyperboreus and C. glacialis and ca. 25% d−1 for M. longa. Assuming a carbon assimilation efficiency of 80% gives carbon-specific daily rations of 9% d−1 for the two calanoids and 30% d−1 for M. longa. Carbon-specific egestion of faeces is positively correlated with POC in the chlorophyll maximum (Daly, 1997), contrasting with the inverse relationship between ingestion rate and phytoplankton biomass for Oikopleura (Acuña et al., 1999). There is a phytoplankton bloom in the eastern NOW during May and June. Copepod biomass tracks phytoplankton abundance, with a maximum in the southeast in May, and in the northwest in June. Monthly carbon-specific herbivory rate tracks phytoplankton and copepod biomass, with maxima in the southeast in May–June and in the northwest in June


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Figure 2: Weight-specific ingestion rate of phytoplankton by copepods in the North Water Polynya as a function of initial chlorophyll a concentrations in feeding experiments. BBW is the eastern NOW and SRAW + MIX is the colder, western NOW. Reprinted from Saunders et al. (2003). (Saunders et al., 2003.) Carbon-specific herbivory rate increases linearly with increasing chlorophyll, with no sign of a saturation functional response (Figure 2). Carbon-specific herbivory rates in the NOW are similar to those in the NEW (ca. 10% d−1 for Calanus spp. and M. longa). Oikopleura vanhoeffeni in the NOW has a five-fold increase in gut chlorophyll content over a 5-fold range in chlorophyll concentration (Acuña et al., 2002). Above a chlorophyll concentration of 250 mg m−2 , gut chlorophyll content decreases 30–40%, presumably due to clogging of the inlet filters and increased frequency of back flushing. Gut chlorophyll content also increases as a 1.57 power of body size (Acuña et al., 2002), somewhat lower than the 2.14 power in the NEW (Acuña et al., 1999). This likely reflects the generally larger cell size of phytoplankton in the NOW relative to the NEW. Much work has been done on the metabolism and feeding of Calanus glacialis in the boreal North Atlantic Ocean. The carbon-specific daily ration is 12–14% for stage CIV and CV (Tande and Båmstedt, 1985; Head, 1986). These carbon-specific ingestion rates are close to those of C. hyperboreus and C. glacialis in the NEW and the NOW, of ca. 5–9% (Daly, 1997; Saunders et al., 2003), suggesting that polynyas do not confer an energetic advantage via the daily ingestion rates of calanoid copepods. 2.3.2

Diet

Fatty acid (FA) biomarkers have revealed spatial variability in the diet and trophic linkages among mesozooplankton and their prey in Arctic polynyas. Thus, they have provided crucial tests of hypotheses concerning whether polynyas serve as oases of biological productivity in the midst of ice-covered seas. Furthermore, several phytoplankton FA’s are essential for zooplankton growth and reproduction, and examination of how these FA’s are acquired by

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zooplankton is crucial for understanding recruitment dynamics of polynya zooplankton populations. For example, in the northern NEW, Calanus hyperboreus and C. finmarchicus show decreasing levels of diatom FA’s and increasing levels of dinoflagellate FA’s as a function of distance from the East Greenland Current (Graeve, 1993), suggesting that these two species ingest significant quantities of phytoplankton during the summer in the northern NEW. The fatty acid compositions of Calanus hyperboreus, C. glacialis and Metridia longa, particularly the high levels of 16:1(n-7) and 20:5(n-3) (EPA, an essential FA), indicate that copepods are actively feeding and that diatoms are an important source of food as late as September in the NOW polynya (Stevens et al., 2004b). Diatom production is prolonged in the NOW, with a dense Chaetoceros socialis population persisting well into autumn (Booth et al., 2002). This extended diatom production may be the primary benefit of the NOW to calanoid copepods, resulting in a delay of diapause, reduced generation times and increased annual recruitment (Stevens et al., 2004b). In addition, bacteria form part of the diet of all copepods investigated in the NOW, but to varying extents (Stevens et al., 2004b). It is assumed that copepods do not ingest free-living bacteria, but rather marine snow, bacterivorous microzooplankton, or other particles associated with bacteria. Calanoid copepods have been shown to ingest marine snow both in situ (Dagg, 1993) and in vitro (Dilling et al., 1998) and their ingestion of protozoans is well documented (Froneman et al., 1996; Levinson et al., 2000). In Calanus hyperboreus and Metridia longa, ingestion of bacteria occurs only at southeastern stations where bacterial biomass is highest. C. glacialis, on the other hand, appears to ingest bacteria in both the southeastern and northwestern NOW, often in association with diatom-based herbivory. Compared to the other copepods, C. glacialis may be more likely to eat dying cells or marine snow, where bacterial and diatom biomarkers presumably co-occur (Stevens et al., 2004b). Appendicularians feed non-selectively using a series of fine-mesh, mucous filters (Deibel, 1998). Inlet filters on the outside of the house can prohibit the ingestion of large cells. The pore size of the inlet filters is both species and body size dependent (Deibel, 1998). The diet of appendicularians has been investigated only in the NOW, where HPLC analyses of gut pigments indicate a relatively greater ingestion of chlorophyll b at southeastern stations relative to diatom-rich waters to the northwest (Acuña et al., 2002). This suggests significant ingestion of non-diatom prey at southeastern stations. Microscopic analyses support the pigment data, showing that more than 92% of gut content volume is diatoms at the northwestern stations, while less than 77% of gut content volume is diatoms at two southeastern stations. 2.3.3

Community Grazing Rates

Estimates of the grazing impact of large calanoid copepods in the NEW polynya are seasonally dependent. Soon after the polynya opens in the spring, copepods may ingest as much as 41% of primary production (PP) d−1 (Daly et al., 1999), whereas during late June Hirche et al. (1991) estimate that the copepod community removes 25–33% d−1 , due to a low abundance of animals in the central polynya. In August-September the copepod grazing impact is 15% of daily PP within the central polynya, 500% in the southern NEW and 100% in the northern NEW (Ashjian et al., 1995). Even though copepod grazing was greater than 100% of PP in the northern and southern NEW, there was no indication that copepods were food limited in these two areas. The authors suggest that the copepods may have been feeding on dense patches of phytoplankton or omnivorously in deep troughs. The copepods in the NOW have a weight-specific herbivory rate ranging from 0– 24% d−1 from April through July (Saunders et al., 2003). Given a community biomass


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of 20–3200 mg C m−2 (Figure 1), this is equivalent to a community herbivory range of 0–800 mg C m−2 d−1 . During the spring bloom, copepod community grazing impact is generally less than 10% of daily PP, but following the bloom at southeastern stations, grazing impact ranges from 15–55% of daily PP (Saunders et al., 2003). The carbon-specific herbivory rate of individuals explains neither the timing nor magnitude of community herbivory, which is accounted for only by the biomass of phytoplankton and copepods (Saunders et al., 2003). Saunders et al. (2003) conclude that the generally low grazing impact of copepods in AO polynyas is due to high rates of PP and relatively low standing stocks of grazers. Oikopleurid appendicularian population ingestion rates of chlorophyll in the NOW range from 0.01–2.08 mg C m−2 d−1 (Acuña et al., 2002). Since animals less than 1 mm in size dominate abundance and biomass at most stations, appendicularians from 0.5–1.5 mm long are the primary contributors to population ingestion. These ingestion rates translate into herbivory rates of 0.33–104 mg C m−2 d−1 , or 0.1–33% of PP d−1 in July (Acuña et al., 2002). Given that appendicularians generally remove about twice as much phytoplankton from suspension as is ingested (Gorsky et al., 1984), on average appendicularians may account for 20% of daily PP in the NOW, equivalent to the community ingestion rate of copepods. Herbivorous ingestion rates of appendicularians in the SLIP range from 150–1000 mg C m−2 d−1 , apparently causing depletion of autotrophic nanoplankton across the St. Lawrence Island shelf (Deibel et al., 2005). This is several times higher than estimates of the ingestion rate of copepods in the SLIP (30–50 mg C m−2 d−1 ). However, because PP rates are so high, the grazing impact of appendicularians remains 10–20% of PP, essentially the same as in the NEW. This grazing impact is similar to literature values from elsewhere in the Arctic Ocean, suggesting a general trend of grazing impact of copepods and appendicularians of ca. 20% of PP d−1 for each group. This leaves about 60% of daily PP to be ingested by fritillarid appendicularians or herbivorous protists, or to be exported to the benthos. 2.4

Faecal Pellet Production and Vertical Flux

In the NEW annual biogenic flux is dominated by ice algae and appendicularian houses and faeces (Bauerfeind et al., 1997). Appendicularian carbon flux is 0.2–5.3 mg m−2 d−1 in August and September, equivalent to 10–37% of daily POC flux (Bauerfeind et al., 1997). The integrated, annual flux of appendicularian houses and faeces is 975 mg C m−2 , 14% of POC flux, while copepod faecal pellets contribute less than 5% of the annual POC flux (Bauerfeind et al., 1997), perhaps because of their reingestion by Metridia and Oithona at intermediate depths (Daly, 1997). Thus, appendicularians make at least a 3-fold greater contribution to the total annual flux of POC than do copepods. In the NOW, more than 99% of the copepod faecal pellets produced in the epipelagic zone do not reach 200 m (Sampei et al., 2004). Faecal flux is highest in July/August (20– 33 mg C m−2 d−1 ) when annual maxima of total vertical flux occurs (13–102 mg C m−2 d−1 ). The contribution of phytoplankton to the total flux ranges from 0–16%, with maxima in spring. Faecal pellets contribute from 1–63% to the total flux (mean = 20%), with maxima in the July/August. This annual mean value is similar to the contribution of copepod pellets and appendicularian pellets and houses to the annual vertical flux in the NEW (i.e., = 19% y−1 ). The potential vertical flux of oikopleurid appendicularian faeces in the NOW in July is 8 mg C m−2 d−1 (Acuña et al., 2002). This is more than an order of magnitude larger than estimates of zooplankton faecal flux from sediment traps deployed a month earlier, of 0.5 mg C m−2 d−1 (Sampei et al., 2002), perhaps due to a seasonal increase in the abundance of appendicularians which reach their peak in the NOW in August/September (Acuña et al.,

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2002). The potential flux in the NOW is similar to that in NEW in August (5.3 mg C m−2 d−1 , Bauerfeind et al., 1997). Appendicularian faeces account for ca. 8% of the maximum vertical export of POC from the NOW of ca. 100 mg C m−2 d−1 in July (Sampei et al., 2004). The inclusion of discarded filter houses may raise this flux to ca. 80% of POC flux (Bauerfeind et al., 1997). Thus, the ratio of export of copepod:appendicularian faeces is ca. 2.5 : 1 in the NOW. 2.5

Seasonal Energy Storage and Egg Production Rates

Calanoid copepods of the AO are generally large and store energy in the form of lipids, primarily wax esters (WE) (Lee et al., 1971). For example, the lipid content of adult female Calanus hyperboreus in the AO ranges from a maximum of 74% of dry weight (DW) in August, to a minimum of 29% in June, following spawning (Table 5). AO copepods use lipid stores for overwintering at depth and in some cases, for making eggs the following spring. Under starvation C. hyperboreus adults lose 0.3% of body mass d−1 , 87% of which is combusted lipid (Conover, 1965). In the NOW in September, Calanus hyperboreus stores more lipid (up to 70% of DW) and has a greater proportion of wax esters (84–93%) than do C. glacialis or Metridia longa (Table 5, Stevens et al., 2004b). This interspecies difference in lipid storage seems to occur throughout the AO (Conover and Huntley, 1991). There is little variability within the NOW in either the total lipid or WE content of C. hyperboreus or C. glacialis, while M. longa at southeastern stations has lower lipid levels that do animals from northwestern stations. Stevens et al. (2004b) proposed that these spatial differences in lipid storage by M. longa are due to the dominance of a microbial food web at southeastern stations and a diatom-based one at northwestern stations. Open waters of the NEW do not seem to promote lipid storage by calanoid copepods, with lipid levels of adult female and CV C. hyperboreus and adult C. glacialis lower inside than outside the polynya (Table 5). Unlike in the NOW (Stevens et al., 2004a), there is no spatial trend in the lipid content of M. longa in the NEW (Ashjian et al., 1995), suggesting that this deep-living copepod spawns continuously in the NEW, independently of ice cover. Even though rates of primary productivity are higher in the eastern NOW, followed by the western NOW and the NEW (Klein et al., 2002), levels of lipid storage by calanoid copepods are similar in both (Table 5). Maximum values for adult female Metridia longa are ca. 35% in all regions of both polynyas. Comparative data in Table 5 indicate that earlier and higher levels of primary productivity in Arctic polynyas do not result in higher levels of lipid storage by copepods than in surrounding ice-covered waters. Egg production rates (EPR) of C. glacialis in the NEW range from 40 to 100 female−1 d−1 , among the highest ever reported for this species (Ashjian et al., 1995). However, EPR’s are variable, as Hirche et al. (1991) found EPR’s of 15–45 female−1 d−1 in the central NEW in June and 3–7 female−1 d−1 in the ice-covered East Greenland Current. These values from the NEW are much higher than published values from the field (i.e., 22–42 d−1 in the Fram Strait) or laboratory (i.e., 10–20 d−1 ) and correspond to 3–6% of body C d−1 . However, carbon-specific EPR’s are highest in the polynya because females there mature at a smaller body size (Ashjian et al., 1995), and since egg production appears to depend upon feeding (Hirche and Kosobokova, 2003), it seems that C. glacialis does not rely on lipid stores for egg production in the NEW. Unresolved are the relatively high EPR’s both inside and outside of the NEW polynya, along with relatively low standing stocks of copepods. It is enigmatic that the abundance of


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copepod nauplii is four-times greater in the ice-covered NEW than in the ice-free central polynya. The relatively high stocks of the omnivorous Metridia longa in the central polynya may limit the population of C. glacialis by preying upon eggs and nauplii. The Laptev Sea Polynya is an area of active spawning of Calanus glacialis. In July EPR is highest between 50 and 200 m in open water or near the ice edge. EPR’s decrease from July (20–60 eggs female−1 d−1 ) to September (0–8 female−1 d−1 , Kosobokova and Hirche, 2001). Unlike the southern NEW, there is no egg production by C. glacialis when ice cover exceeds 70%. In July there is no correlation between EPR and chlorophyll, suggesting that eggs are produced from lipid stores, while in September egg production depends upon contemporaneous feeding. This may explain the apparently contradictory results in the NEW, where eggs are produced from lipid stores early in the productive season and depend upon active feeding later. The authors conclude that C. glacialis is no more abundant in the LSP than in other Arctic shelf seas and that spawning variability depends upon the dynamics of ice, with copepod production occurring after the ice melts. Clutch size of Calanus hyperboreus in the NOW decreases with time, from ca. 160 eggs female−1 d−1 early in the spring to 100 eggs female−1 d−1 in late spring (Ringuette et al., 2002). Egg laying begins 1–9 weeks after the final molt to the adult stage, and spent females may loose 90% of their body weight during vitellogenesis and spawning (Conover, 1967). The maximum clutch size of C. hyperboreus in the Fram Strait is 57 eggs female−1 (S. Smith, 1990). Thus, the NOW appears to support much higher EPR’s of C. hyperboreus than surrounding ice-covered waters. 2.6

Secondary Production and Generation Time

Ringuette et al. (2002) use the proportion of CI-CIII as a measure of the ’recruitment success’ of copepod species in the NOW. They find success to increase with increasing chlorophyll for Calanus hyperboreus, with increasing chlorophyll and temperature for C. glacialis and with increasing temperature for Pseudocalanus spp. Thus, they conclude that C. hyperboreus is food limited in the NOW, C. glacialis is co-limited by both food concentration and temperature and Pseudocalanus is primarily temperature limited. Therefore, some polynyas can be sources not only of food, but also of heat, which should accelerate copepod development times relative to surrounding ice-covered waters (Ringuette et al., 2002). The NOW has a productive season of ca. 6 months, apparently long enough to permit early reproduction and reduced generation times of large, calanoid copepods, in some cases removing an entire year from the life cycle (Ringuette et al., 2002). For example, recruitment of copepodite stage I of Calanus hyperboreus, C. glacialis and Pseudocalanus spp. starts in May–June, 1.5–3 months earlier than CI recruitment in the Barrow Strait (Figure 3). CI of Metridia longa recruits in June, one month earlier than in the BS. Thus, for the 4 calanoids which depend upon the diatom bloom, the eastern polynya gives a 2–4 week advantage in spawning time over the western area and a 2–6 week advantage over the BS (Ringuette et al., 2002). Copepods captured in sediment traps indicate that young-of-the-year Calanus hyperboreus start to molt to the adult stage in September in the eastern NOW and in December in the western NOW. This demonstrates the life cycle advantage conferred upon large copepod species by some Arctic polynyas and specifically shows the temporal advancement of the production cycle in the eastern NOW. Life cycle advantages of developing in the NOW do not extend to all copepod species, however, as there is no difference in recruitment time of copepodites of Microcalanus spp.,

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Figure 3: Calendar week of recruitment of CI of three species of copepods in the Northwater Polynya vs. the calendar week of the first onset of the annual phytoplankton bloom. Statistics are given for least squares linear regression. Reprinted from Ringuette et al. (2002). Oithona similis and Oncaea borealis between the NOW and the BS (Ringuette et al., 2002). These species may reproduce year-round independently of ice cover, perhaps depending primarily on the microbial food web for prey (Ringuette et al., 2002). In fact, nauplii of O. borealis are found in all months in which samples are taken. In ice-covered waters of the AO, Calanus hyperboreus has a three-year life cycle (Dawson, 1978), but in Norwegian fjords and the Greenland Sea it is one-year, spawning between January and March (Matthews et al., 1978; S. Smith, 1990). These differences can be accounted for by temperature-dependent development times. C. hyperboreus has been found to require 114 d to develop from egg to CIII at 4–6◦ C, and 250 d from egg to adult at 2–4◦ C (Conover, 1965). Based upon an embryonic development time of 8 d at −2◦ C, S. Smith (1990) estimates a life span of 201–350 d in the AO. C. hyperboreus should be capable of a 1-year life cycle outside of the AO (and perhaps in Arctic polynyas), but requires 2–3 years in the high Arctic. Calanus glacialis in the NEW spawns in July/August, 4–6 weeks behind the eastern NOW and at about the same time as in the ice-covered BS. However, C. glacialis generally has a 2-year life cycle in ice-covered waters, maturing in April–May. Spawning starts when algae begin to sluff off the ice in May, and generally is over by mid-July, when the watercolumn phytoplankton bloom is maximal. Although the NEW is not as advantageous as the NOW for reproduction of C. glacialis, it still provides a one-year life cycle in comparison to the two-year cycle of ice-covered waters.


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3 Zooplankton of the Southern Ocean 3.1

Species Composition and Abundance

Hempel (1985) describes three large-scale, pelagic ecological provinces related to latitude, different water masses, the seasonal variability in sea ice cover, and bathymetry. The two provinces relevant to polynyas are the seasonal sea-ice zone and the pack-ice zone. The seasonal sea-ice zone is ice covered during winter and largely ice-free during summer. This zone may extend as far north as the Polar Front and south over the continental shelf in most sectors of the SO. The euphausiid, Euphausia superba, often dominates zooplankton abundance and biomass. When krill are scarce, four copepod species, Calanoides acutus, Calanus propinquus, Rhincalanus gigas and Metridia gerlachei, dominate the biomass throughout much of the sea-ice region (Voronina, 1998). The most southerly zone (the permanent pack-ice zone) includes regions over the continental shelf (e.g., the southwestern Weddell Sea and southern Ross Sea) and over deep water (e.g., sections of the Ross, Weddell, Admundsen, and Bellingshausen seas). In coastal waters the euphausiid, E. crystallorophias, replaces or co-exists with E. superba. Depending on the location, zooplankton biomass and production may be low in the permanent pack-ice zone and the seasonal pulse of primary production from phytoplankton and ice algae probably sinks out to support a rich epibenthic community (Hempel, 1985). In the coastal zone where most Antarctic polynyas occur, Eicken (1992) proposes an additional four subzones: the ice shelf, fast ice (sea ice that is anchored to land or ice shelves), coastal polynya, and the pack-ice boundary. Ice shelves are a floating extension of the Antarctic ice sheets and a source of fresh melt-water that acts to shoal and stabilize the mixed layer. The coastal polynyas also provide access to open water for predators, such as minke and killer whales, crabeater and leopard seals, and emperor and Adélie penguins (Gill and Thiele, 1997), in addition to being significant regions of primary production. The pack ice boundary, including the edge of the perennial pack ice and along the edges of polynyas, is usually characterized by highly deformed ice with many ridges, which provides a favorable habitat for Euphausia superba and E. crystallorophias, as well as other metazoans which graze on sea ice algae (Daly and Macaulay, 1988; Marschall, 1988; Stretch et al., 1988; Schnack-Schiel et al., 1998). In some areas where freshwater flows from beneath ice shelves, platelet ice forms in the water column and then floats up under fast ice and pack ice. Its open matrix permits biological production that may be up to 10 times higher (i.e. “superblooms”) than surrounding habitats (El-Sayed, 1971; Smetacek et al., 1992). Thus, different types of ice play important roles in the ecology of Antarctic marine zooplankton and their predators. 3.1.1

Zooplankton Abundance near the Weddell Sea Polynya

Data on winter zooplankton abundance are limited, but some information exists for the Weddell Sea polynya, which occurs near the Maud Rise. Even when the Weddell Sea Polynya is not open, a large number of leads and thin sea ice are observed in this region. West of Maud Rise chlorophyll concentrations are elevated during all seasons and 0.1–0.15 mg chl m−3 persists in open water into austral winter (June) (Spiridonov et al., 1996). Densities of Euphausia superba larvae (∼2000 ind 1000 m−3 and copepodite stages CIII–CV of Calanus propinquus (∼20,000 ind 1000 m−3 , Calanoides acutus (∼8000 ind 1000 m−3 ), and Rhincalanus gigas (∼2400 ind 1000 m−3 ) also are elevated in the upper 1000 m. In addition, the copepods, Microcalanus pygmaeus and Ctenocalanus citer, pteropods (Limacina spp.), and appendicularians are common. During late winter/early spring, zooplankton densities

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continue to be elevated near Maud Rise, but C. acutus has the highest abundances (2000– 5450 ind 1000 m−3 ), C. propinquus is intermediate (

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