Articles | Volume 20, issue 3
https://doi.org/10.5194/tc-20-1815-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-1815-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Mar 2026
Research article |
| 30 Mar 2026
| 30 Mar 2026
Challenges in identifying Antarctic coastal polynyas in satellite observations and climate model output to support ecological climate change research
NSF National Center for Atmospheric Research, Boulder, CO, USA
Alice K. DuVivier
NSF National Center for Atmospheric Research, Boulder, CO, USA
Marika M. Holland
NSF National Center for Atmospheric Research, Boulder, CO, USA
Kristen Krumhardt
NSF National Center for Atmospheric Research, Boulder, CO, USA
Zephyr Sylvester
INSTAAR, University of Colorado, Boulder, CO, USA
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Cited articles
Arrigo, K. R. and van Dijken, G. L.: Phytoplankton dynamics within 37 Antarctic coastal polynyas, J. Geophys. Res., 108, 3271, https://doi.org/10.1029/2002JC001739, 2003.
Arrigo, K. R., van Dijken, G. L., and Strong, A. L.: Environmental controls of marine productivity hot spots around Antarctica, J. Geophys. Res.-Oceans, 120, 5545–5565, https://doi.org/10.1002/2015JC010888, 2015.
Bocquet, M., Fleury, S., Rémy, F., and Piras, F.: Arctic and Antarctic sea ice thickness and volume changes from observations between 1994 and 2023, J. Geophys. Res.-Oceans, 129, e2023JC020848, https://doi.org/10.1029/2023JC020848, 2024.
Caton Harrison, T., Biri, S., Bracegirdle, T. J., King, J. C., Kent, E. C., Vignon, É., and Turner, J.: Reanalysis representation of low-level winds in the Antarctic near-coastal region, Weather Clim. Dynam., 3, 1415–1437, https://doi.org/10.5194/wcd-3-1415-2022, 2022.
Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res., 89, 5355–5369, 1984.
Comiso, J. C.: Characteristics of arctic winter sea ice from satellite multispectral microwave observations, J. Geophys. Res., 91, 975–994, 1986.
Comiso, J. C., Cavalieri, D., Parkinson, C., and Gloersen, P.: Passive microwave algorithms for sea ice concentrations: a comparison of two techniques, Remote Sens. Environ., 60, 357–384, 1997.
Criscitiello, A. S., Das, S. B., Evans, M. J., Frey, K. E., Conway, H., Joughin, I., Medley, B., and Steig, E. J.: Ice sheet record of recent sea-ice behavior and polynya variability in the Amundsen Sea, West Antarctica, J. Geophys. Res.-Oceans, 118, 118–130, https://doi.org/10.1029/2012JC008077, 2013.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D., DuVivier, A. K., Edwards, J., Emmons, L., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., 825 Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model version 2 (CESM2), J. Adv. Model. Earth Sy., 2, 1–35, https://doi.org/10.1029/2019ms001916, 2020.
Diamond, R., Sime, L. C., Holmes, C. R., and Schroeder, D.: CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023, Geophys. Res. Lett., 51, e2024GL109265, https://doi.org/10.1029/2024GL109265, 2024.
Duffy, G. A., Montiel, F., Purich, A., and Fraser, C. I.: Emerging long-term trends and interdecadal cycles in Antarctic polynyas, P. Natl. Acad. Sci. USA, 121, https://doi.org/10.1073/pnas.2321595121, 2024.
DuVivier, A. K. and Bailey, D. A.: CESM2 Sea Ice thermodynamics sensitivity experiments, NSF National Center for Atmospheric Research [data set], https://doi.org/10.5065/bgt9-tz46, 2021.
DuVivier, A. K., Holland, M. M., Landrum, L., Singh, H. A., Bailey, D. A., and Maroon, E. A.: Impacts of sea ice mushy thermodynamics in the Antarctic on the coupled Earth system, Geophys. Res. Lett., 48, e2021GL094287, https://doi.org/10.1029/2021GL094287, 2021.
DuVivier, A. K., Molina, M. J., Deppenmeier, A.-L., Holland, M. M., Landrum, L., Krumhardt, K., and Jenouvrier, S.: Projections of winter polynyas and their biophysical impacts in the Ross Sea Antarctica, Clim Dynam., 62, 989–1012, https://doi.org/10.1007/s00382-023-06951-z, 2024.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.: Sea Ice Index, Version 3, National Snow and Ice Data Center, Boulder, Colorado, USA [data set], https://doi.org/10.7265/N5K072F8 (last access: 2 June 2023), 2017.
Fogt, R. L., Sleinkofer, A. M., Raphael, M. N., and Handcock, M. S.: A regime shift in seasonal total Antarctic sea ice extent in the twentieth century, Nat. Clim. Chang., 12, 54–62, https://doi.org/10.1038/s41558-021-01254-9, 2022.
Fons, S., Kurtz, N., and Bagnardi, M.: A decade-plus of Antarctic sea ice thickness and volume estimates from CryoSat-2 using a physical model and waveform fitting, The Cryosphere, 17, 2487–2508, https://doi.org/10.5194/tc-17-2487-2023, 2023.
Fraser, A. D, Ohshima, K. I., Nihashi, S., Massom, R. A., Tamura, T., Nakata, K., Williams, G. D., Carpentier, S., and Willmes, S.: Landfast ice controls on sea-ice production in the Cape Darnley Polynya: A case study, Remote Sens. Environ., 233, 111315, https://doi.org/10.1016/j.rse.2019.111315, 2019.
Gilbert, R. O.: Statistical Methods for Environmental Pollution Monitoring, John Wiley & Sons, ISBN 978-0-471-28878-7, 1987.
Grenfell, T. C., Cavalieri, D. J., Comiso, J. C., Drinkwater, M. R., Onstott, R. G., Rubinstein, I., Steffen, K., and Winebrenner, D. P.: Considerations for microwave remote sensing of thin sea ice, in: Microwave Remote Sensing of Sea Ice, edited by: Carsey, F. D., American Geophysical Union, Washington, D.C., https://doi.org/10.1029/GM068p0291, 1992.
Hobbs, W., Spence, P., Meyer, A., Schroeter, S., Fraser, A. D., Reid, P., Tian, T. R., Wang, Z., Liniger, G., Doddridge, E. W., and Boyd, P. W.: Observational Evidence for a Regime Shift in Summer Antarctic Sea Ice, J. Climate, 37, 2263–2275, https://doi.org/10.1175/JCLI-D-23-0479.1, 2024.
Holland, M. M., Bitz, C. M., Hunke, E. C., Lipscomb, W. H., and Schramm, J. L.: Influence of the sea ice thickness distribution on polar climate in CCSM3, J. Climate, 19, 2398–2414, https://doi.org/10.1175/jcli3751.1, 2006.
Holmes, C. R., Bracegirdle, T. J., Holland, P. R., Stroeve, J., and Wilkinson, J.: Brief communication: New perspectives on the skill of modelled sea ice trends in light of recent Antarctic sea ice loss, The Cryosphere, 18, 5641–5652, https://doi.org/10.5194/tc-18-5641-2024, 2024.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.: CICE: The Los Alamos Sea Ice Model. Documentation and software user's manual, Version 5.1, T-3 Fluid Dynamics Group, Los Alamos National Laboratory, Tech. Rep. LA-CC-06-012, 2015.
Ivanova, N., Pedersen, L. T., Tonboe, R. T., Kern, S., Heygster, G., Lavergne, T., Sørensen, A., Saldo, R., Dybkjær, G., Brucker, L., and Shokr, M.: Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations, The Cryosphere, 9, 1797–1817, https://doi.org/10.5194/tc-9-1797-2015, 2015.
Jeong, H., Lee, S., Park, H. S., and Stewart, A. L.: Future changes in Antarctic coastal polynyas and bottom water formation simulated by a high-resolution coupled model, Commun. Earth Environ., 4, 490, https://doi.org/10.1038/s43247-023-01156-y, 2023.
Jones, R., Renfrew, I., Orr, A., Webber, B., Holland, D., and Lazzara, M.: Evaluation of four global reanalysis products using in situ observations in the Amundsen Sea Embayment, Antarctica, J. Geophys. Res.-Atmos., 121, 6240–6257, 2016.
Kacimi, S. and Kwok, R.: Arctic snow depth, ice thickness, and volume from ICESat-2 and CryoSat-2: 2018–2021, Geophys. Res. Lett., 49, e2021GL097448, https://doi.org/10.1029/2021GL097448, 2022.
Kendall, M. G.: Rank Correlation Methods, 4th edn., 2nd impression, Charles Griffin, London, 1975.
Kobayashi, S., Yukinari, O. T. A., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Miyaoka, K., and Takahahi, K.: The JRA-55 reanalysis: general specifications and basic characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Krumhardt, K. M., Long, M. C., Petrik, C. M., Levy, M., Castruccio, F. S., Lindsay, K., Romashkov, L., Deppenmeier, A.-L., Denéchère, R., Chen, Z., Landrum, L., Danabasoglu, G., and Chang, P.: From nutrients to fish: Impacts of mesoscale processes in a global CESM-FEISTY eddying ocean model framework, Prog. Oceanogr., 227, 103314, https://doi.org/10.1016/j.pocean.2024.103314, 2024.
Kwok, R., Comiso, J. C., Martin, S., and Drucker, R.: Ross Sea polynyas: Response of ice concentration retrievals to large areas of thin ice, J. Geophys. Res., 112, C12012, https://doi.org/10.1029/2006JC003967, 2007.
Labrousse, S., Fraser, A. D., Sumner, M., Tamura, T., Pinaud, D., Wienecke, B., Kirkwood, R., Ropert-Coudert, Y., Reisinger, R., Jonsen, I., Porter-Smith, R., Barbraud, C., Bost, C., Ji, R., and Jenouvrier, S.: Dynamic fine-scale sea icescape shapes adult emperor penguin foraging habitat in East Antarctica, Geophys. Res. Lett., 46, 11206–11218, https://doi.org/10.1029/2019GL084347, 2019.
Landrum, L.: llandrum/Cryosphere_AntarcticPolynyas_Satellite Models_ecosystems: AntarcticPolynyasModelObs Pub (AntarcticPolynyasModelObs_Cryosph_Pub), Zenodo, https://doi.org/10.5281/zenodo.18674467, 2026.
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019.
Li, Y., Ji, R., Jenouvrier, S., Jin, M., and Stroeve, J.: Synchronicity between ice retreat and phytoplankton bloom in circum-Antarctic polynyas, Geophys. Res. Lett., 43, 2086–2093, https://doi.org/10.1002/2016GL067937, 2016.
Long, M. C., Lindsay, K., and Holland, M. M.: Modeling photosynthesis in sea ice-covered waters, J. Adv. Model. Earth Sy., 7, 1189–1206, https://doi.org/10.1002/2015MS000436, 2015.
Long, M. C., Moore, J. K., Lindsay, K., Levy, M., Doney, S. C., Luo, J. Y., Krumhardt, K. M., Letscher, R. T., Grover, M., and Sylvester, Z. T.: Simulations with the Marine Biogeochemistry Library (MARBL), J. Adv. Model. Earth Sy., 13, e2021MS002647, https://doi.org/10.1029/2021MS002647, 2021.
Mann, H. B.: Non-parametric tests against trend, Econometrica, 13, 163–171, 1945.
Markus, T. and Burns, B. A.: Detection of coastal polynyas with passive microwave data, Ann. Glaciol., 17, 351–355, 1993.
Massom, R. A., Harris, P. T., Michael, K. J., and Potter, M. J.: The distribution and formative processes of latent-heat polynyas in East Antarctica, Ann. Glaciol., 27, 420–426, 1998.
Meier, W. N., Peng, G., Scott, D. J., and Savoie, M. H.: Verification of a new NOAA/NSIDC passive microwave sea-ice concentration climate record, Polar Research, 33, https://doi.org/10.3402/polar.v33.21004, 2014.
Meier, W. N., Fetterer, F. Windnagel, A. K., and Stewart, J. S.: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. Southern Hemisphere, 1979–2020, Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center [data set], https://doi.org/10.7265/efmz-2t65 (last access: 3 June 2023), 2021a.
Meier, W. N., Windnagel, A., and Stewart, S.: CDR Climate Algorithm and Theoretical Basis Document: Sea Ice Concentration, NOAA NCEI CDR Program, 2021b.
Mohrmann, M., Heuzé, C., and Swart, S.: Southern Ocean polynyas in CMIP6 models, The Cryosphere, 15, 4281–4313, https://doi.org/10.5194/tc-15-4281-2021, 2021.
Nakata, K., Ohshima, I., and Nihashi, S.: Mapping of Active Frazil for Antarctic Coastal Polynyas, With an Estimation of Sea-Ice Production, Geophys. Res. Lett., 48, e2020GL091353, https://doi.org/10.1029/2020GL091353, 2021.
Nihashi, S. and Ohshima, K. J.: Circumpolar Mapping of Antarctic Coastal Polynyas and Landfast Sea Ice: Relationship and Variability, J. Climate, 28, 3650–3670, https://doi.org/10.1175/JCLI-D-14-00369.1, 2015.
Ohshima, K., Fukamachi, Y., Williams, G. D., Nihashi, S., Roquet, F., Kitade, Y.,Tamura, T., Hirano, D., Herraiz-Borreguero, L., Field, I., Hindell, M., Aoki, S., Wakatsuchi, M.: Antarctic Bottom Water production by intense sea-ice formation in the Cape Darnley polynya, Nature Geosci.,6, 235–240, https://doi.org/10.1038/ngeo1738, 2013.
Oliver, H., Turner, J. S., Castagna, A., Houskeeper, H., and Dierssen, H.: High Antarctic coastal productivity in polynyas revealed by considering remote sensing ice-adjacency effects, Limnol. Oceanogr. Lett., 10, 945–956, https://doi.org/10.1002/lol2.70043, 2025.
OSI SAF: Global Sea Ice Concentration Climate Data Record v3.0 – Multimission, EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_0013, 2022.
Parkinson, C. L.: A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic, P. Natl. Acad. Sci. USA, 116, 14414–14423, 2019.
Parkinson, C. L. and Cavalieri, D. J.: Antarctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 871–880, https://doi.org/10.5194/tc-6-871-2012, 2012.
Purich, A. and Doddridge, E. W.: Record low Antarctic sea ice coverage indicates a new sea ice state, Commun. Earth Environ., 4, 314, https://doi.org/10.1038/s43247-023-00961-9, 2023.
Raphael, M. N. and Handcock, M. S.: A new record minimum for Antarctic sea ice, Nat. Rev. Earth Environ., 3, 215–216, https://doi.org/10.1038/s43017-022-00281-0, 2022.
Raphael, M. N., Maierhofer, T. J., Fogt, R. L., WHobbs, W. R., and Handcock, M. S.: A Twenty-First Century Structural Change in Antarctica's Sea Ice System, Commun. Earth Environ., 6, 1–9, https://doi.org/10.1038/s43247-025-02107-5, 2025.
Richert, I., Yager, P., Dinasquet, J., Logares, R., Riemann, L., Wendeberg, A., Bertilsson, S., and Scofield, D.: Summer comes to the Southern Ocean: how phytoplankton shape bacterioplankton communities far into the deep dark sea, Ecosphere, 10, e02641, https://doi.org/10.1002/ecs2.2641, 2019.
Roach, L. A., Dörr, J., Holmes, C. R., Massonnet, F., Blockley, E. W., Notz, D, Rackow, T., Raphael, M. N., O’Farrell, S. P., Bailey, D. A., and Bitz, C. M.: Antarctic sea ice area in CMIP6, Geophys. Res. Lett., 47, e2019GL086729, https://doi.org/10.1029/2019GL086729, 2020.
Singh, H. K. A., Landrum, L., Holland, M. M., Bailey, D. A., and DuVivier, A. K.: An overview of Antarctic sea ice in the CESM2: analysis of the seasonal cycle, predictability, and atmosphere-ocean-ice interactions, J. Adv. Model. Earth Sy., 13, https://doi.org/10.1029/2020MS002143, 2020.
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J., Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S., Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, M., Peacock, S., Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) reference manual, Ocean component of the Community Climate System Model (CCSM), LANL Tech. Report, LAUR-10-01853, 141 pp., 2010.
Tamura, T., Ohshima, K. I., Enomoto, H., Tateyama, K., Muto, A., Ushio, S., and Massom, R. A.: Estimation of thin sea-ice thickness from NOAA AVHRR data in a polynya off the Wilkes Land coast, East Antarctica, Ann. Glaciol., 44, 269–274, 2006.
Tamura, T., Ohshima, K. I., Markus, T., Cavalieri, D. J., Nihashi, S., and Hirasawa, N.: Estimation of thin ice thickness and detection of fast ice from SSM/I data in the Antarctic Ocean, J. Atmos. Ocean. Tech., 24, 1757–1772, 2007.
Tamura, T., Ohshima, K. I., and Nihashi, S.: Mapping of sea ice production for Antarctic coastal polynyas, Geophys. Res. Lett., 35, L07606, https://doi.org/10.1029/2007GL032903, 2008.
Tamura, T., Ohshima, K. I., Fraser, A. D., and Williams, G. D.: Sea ice production variability in Antarctic coastal polynyas, J. Geophys. Res.-Oceans, 121, 2967–2979, https://doi.org/10.1002/2015JC011537, 2016.
Thompson, L., Smith, M., Thomson, J., Stammerjohn, S., Ackley, S., and Loose, B.: Frazil ice growth and production during katabatic wind events in the Ross Sea, Antarctica, The Cryosphere, 14, 3329–3347, https://doi.org/10.5194/tc-14-3329-2020, 2020.
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W., Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J., Griffies, S. M., Y. Harada, Y., M. Ilicak, M., Josey, S. A., Kobayashi, C., Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J., Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: Jra-55 based surface dataset for driving ocean–sea-ice models (jra55-do), Ocean Model., 130, 79–139, 2018.
Turner, J., Phillips, T., Marshall, G. J., Hosking, J. S., Pope, J. O., Bracegirdle, T. J., and Deb, P.: Unprecedented springtime retreat of Antarctic sea ice in 2016, Geophys. Res. Lett., 44, 6868–6875, 2017.
Turner, J., Holmes, C., Caton Harrison, T., Phillips, T., Jena, B., Reeves-Francois, T., Fogt, R., Thomas, E. R., and Bajish, C. C.: Record low Antarctic sea ice cover in February 2022, Geophys. Res. Lett., 49, e2022GL098904, https://doi.org/10.1029/2022GL098904, 2022.
World Meteorological Organization: WMO sea-ice nomenclature. Terminology, codes and illustrated glossary, Geneva, Secretariat of the World Meteorological Organization, 1970, [ix], 147 pp. [including 175 photos] + corrigenda slip (WMO/OMM/BMO, No. 259, TP. 145, 1970.
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014.
Short summary
Antarctic polynyas – areas of open water surrounded by sea ice or sea ice and land – are key players in Antarctic marine ecosystems. Changes in the physical characteristics of polynyas will influence how these ecosystems respond to a changing climate. This work explores how to best compare polynyas identified in satellite data products and climate model data to verify that the model captures important features of Antarctic sea ice and marine ecosystems.
Antarctic polynyas – areas of open water surrounded by sea ice or sea ice and land – are key...
