Articles | Volume 20, issue 4
https://doi.org/10.5194/tc-20-2089-2026
© 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-20-2089-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication: Uncertainties in Southern Ocean sea surface conditions and their impact on Antarctic climate over 1958–1978
Quentin Dalaiden
CORRESPONDING AUTHOR
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Ingo Bethke
Geophysical Institute, Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
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Cited articles
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013. a
Bromwich, D. H., Ensign, A., Wang, S. H., and Zou, X.: Major Artifacts in ERA5 2-m Air Temperature Trends Over Antarctica Prior to and During the Modern Satellite Era, Geophys. Res. Lett., 51, https://doi.org/10.1029/2024GL111907, 2024. a, b, c, d
Dalaiden, Q.: Atmospheric simulations from “Uncertainties in Southern Ocean sea surface conditions and their impact on Antarctic climate over 1958–1978”, Zenodo [data set], https://doi.org/10.5281/zenodo.17521381, 2025a. a
Dalaiden, Q.: Dynamical reconstruction of Southern Ocean and Antarctic climate variability since 1700, Zenodo [data set], https://doi.org/10.5281/zenodo.15472051, 2025b. a
Dalaiden, Q., Rezsöhazy, J., Goosse, H., Thomas, E. R., Vladimirova, D. O., and Tetzner, D.: An Unprecedented Sea Ice Retreat in the Weddell Sea Driving an Overall Decrease of the Antarctic Sea-Ice Extent Over the 20th Century, Geophys. Res. Lett., 50, e2023GL104666, https://doi.org/10.1029/2023GL104666, 2023. a
Fogt, R. L., Schneider, D. P., Goergens, C. A., Jones, J. M., Clark, L. N., and Garberoglio, M. J.: Seasonal Antarctic pressure variability during the twentieth century from spatially complete reconstructions and CAM5 simulations, Clim. Dynam., 53, 1435–1452, https://doi.org/10.1007/s00382-019-04674-8, 2019. a
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. Change, 12, 54–62, https://doi.org/10.1038/s41558-021-01254-9, 2022. a, b
Frieler, K., Clark, P. U., He, F., Buizert, C., Reese, R., Ligtenberg, S. R., Van Den Broeke, M. R., Winkelmann, R., and Levermann, A.: Consistent evidence of increasing Antarctic accumulation with warming, Nat. Clim. Change, 5, 348–352, https://doi.org/10.1038/nclimate2574, 2015. a, b
Fyke, J., Sergienko, O., Löfverström, M., Price, S., and Lenaerts, J. T.: An Overview of Interactions and Feedbacks Between Ice Sheets and the Earth System, Rev. Geophys., 56, 361–408, https://doi.org/10.1029/2018RG000600, 2018. a, b
Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M., Phillips, T. J., Potter, G. L., Santer, B. D., Sperber, K. R., Taylor, K. E., and Williams, D. N.: An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I), Bull. Am. Meteorol. Soc., 80, 29–55, https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2, 1999. a
Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses, Remote Sens., 12, https://doi.org/10.3390/rs12040720, 2020. a
Gossart, A., Helsen, S., Lenaerts, J. T. M., Broucke, S. V., van Lipzig, N. P. M., and Souverijns, N.: An Evaluation of Surface Climatology in State-of-the-Art Reanalyses over the Antarctic Ice Sheet, J. Clim., 32, 6899–6915, https://doi.org/10.1175/JCLI-D-19-0030.1, 2019. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The community earth system model: A framework for collaborative research, Bull. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013. a
Jones, J. M., Gille, S. T., Goosse, H., Abram, N. J., Canziani, P. O., Charman, D. J., Clem, K. R., Crosta, X., De Lavergne, C., Eisenman, I., England, M. H., Fogt, R. L., Frankcombe, L. M., Marshall, G. J., Masson-Delmotte, V., Morrison, A. K., Orsi, A. J., Raphael, M. N., Renwick, J. A., Schneider, D. P., Simpkins, G. R., Steig, E. J., Stenni, B., Swingedouw, D., and Vance, T. R.: Assessing recent trends in high-latitude Southern Hemisphere surface climate, Nat. Clim. Change, 6, 917–926, https://doi.org/10.1038/nclimate3103, 2016. a
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J. F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The community earth system model (CESM) large ensemble project : A community resource for studying climate change in the presence of internal climate variability, Bull. Am. Meteorol. Soc., 96, 1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1, 2015. a
Kittel, C., Amory, C., Agosta, C., Delhasse, A., Doutreloup, S., Huot, P. V., Wyard, C., Fichefet, T., and Fettweis, X.: Sensitivity of the current Antarctic surface mass balance to sea surface conditions using MAR, The Cryosphere, 12, 3827–3839, https://doi.org/10.5194/tc-12-3827-2018, 2018. a, b
Lenaerts, J. T., Gettelman, A., van Tricht, K., van Kampenhout, L., and Miller, N. B.: Impact of Cloud Physics on the Greenland Ice Sheet Near-Surface Climate : A Study With the Community Atmosphere Model, J. Geophys. Res.-Atmos., 1–16, https://doi.org/10.1029/2019JD031470, 2020. a, b
Neale, R., Richter, J., Conley, A., Park, S., Lauritzen, P., Gettelman, A., Williamson, D., Rasch, P., Vavrus, S., Taylor, M., Collins, W., Zhang, M., and Lin, S.-J.: Description of the Community Atmosphere Model (CAM 4.0), NCAR Technical Note, TN-485+STR, https://doi.org/10.5065/GSEB-6470, 2010. a
Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023. a
Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Villaume, S., Haimberger, L., Woollen, J., Buontempo, C., and Thépaut, J.-N.: The ERA5 global reanalysis from 1940 to 2022, Q. J. Roy. Meteorol. Soc., 150, 4014–4048, https://doi.org/10.1002/qj.4803, 2024. a, b
Thomas, E. R., Allen, C. S., Etourneau, J., King, A. C. F., Severi, M., Winton, V. H. L., Mueller, J., Crosta, X., and Peck, V. L.: Antarctic Sea Ice Proxies from Marine and Ice Core Archives Suitable for Reconstructing Sea Ice over the past 2000 Years, Geosciences, 1–33, https://doi.org/10.3390/geosciences9120506, 2019. a
Turner, J., Colwell, S. R., Marshall, G. J., Lachlan-Cope, T. A., Carleton, A. M., Jones, P. D., Lagun, V., Reid, P. A., and Iagovkina, S.: The SCAR READER Project: Toward a High-Quality Database of Mean Antarctic Meteorological Observations, J. Clim., 17, 2890–2898, https://doi.org/10.1175/1520-0442(2004)017<2890:TSRPTA>2.0.CO;2, 2004. a
van Dalum, C. T., van de Berg, W. J., van den Broeke, M. R., and van Tiggelen, M.: The surface mass balance and near-surface climate of the Antarctic ice sheet in RACMO2.4p1, The Cryosphere, 19, 4061–4090, https://doi.org/10.5194/tc-19-4061-2025, 2025. a
Wang, W., Shen, Y., Chen, Q., Wang, F., and Yu, Y.: Spatiotemporal mass change rate analysis from 2002 to 2023 over the Antarctic Ice Sheet and four glacier basins in Wilkes-Queen Mary Land, Sci. China Earth Sci., 68, 1086–1099, https://doi.org/10.1007/s11430-024-1517-1, 2025. a
Short summary
Historical Antarctic climate before satellites contain uncertainties, a modern state-of-the-art atmospheric reanalysis indicates an unrealistically cold Antarctica in 1958–1978. We test how much of this bias comes from uncertain ocean and sea-ice conditions by performing two climate model ensembles with different ocean datasets. These differences affect Antarctic climate, but they explain only a fraction of the cold bias, meaning other factors also contribute.
Historical Antarctic climate before satellites contain uncertainties, a modern state-of-the-art...