Abstract
For Earth’s climate system, the study of the seasonal variability of sea-ice is important as the sea-ice has a significant impact on the net radiative flux, which can influence the mean seasonal behaviours of the atmosphere and ocean. In this study, the seasonal hindcast of 14 austral winter seasons is conducted to assess the skill of a coupled model in simulating the seasonal Antarctic sea-ice and its connection with the other ocean and atmospheric variables. The GloSea4 set-up of the HadGEM3 coupled model is used for the seasonal simulations at the NCMRWF. The model could reproduce the sea-ice extent over the Antarctic for the Austral winter seasons with an average correlation value of 0.98. However, there are moderate biases in the sea-ice concentration. The sea-ice thickness in the model generally shows negative bias, which is not seen to be related to the surface air temperature biases in the coupled system. The moderate positive (warm) biases in the sea surface temperature extending into the upper ocean (30 m), combined with the sea-ice drift bias pattern away from the sea-ice region are the main reasons for the underestimation of sea-ice thickness in the model. The sea surface current bias pattern shows a poleward component that brings the warm water from the warm biased locations of the exterior region into the sea-ice region and explains the presence of warmer waters in the sea-ice regions. The anti-clockwise bias in the surface wind is seen to impact the surface current, Antarctic circumpolar current (ACC), having a similar anti-clockwise current bias. Despite these moderate biases in the model, the inter-annual variability of sea-ice extent is having a reasonably good skill. The model is suitable for extended/seasonal prediction of sea-ice during Austral winter for Antarctic.
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References
Arribas A, Glover M, Maidens A, Peterson K, Gordon M, MacLachlan C, Graham R, Fereday D, Camp J, Scaife A A, Xavier P, McLean P, Colman A and Cusack S 2011 The GloSea4 ensemble prediction system for seasonal forecasting; Mon. Wea. Rev. 139 1891–1910, https://doi.org/10.1175/2010MWR3615.1.
Balmaseda M A, Mogensen K and Weaver A T 2013 Evaluation of the ECMWF ocean reanalysis system ORAS4; Quart. J. Roy. Meteorol. Soc. 139 1132–1161, https://doi.org/10.1002/qj.2063.
Bitz C M and Lipscomb W H 1999 An energy-conserving thermodynamic model of sea ice; J. Geophys. Res. Ocean. 104 15,669–15,677, https://doi.org/10.1029/1999JC900100.
Cavalieri D J and Parkinson C L 2008 Antarctic sea ice variability and trends, 1979–2006; J. Geophys. Res. 113 1–19, https://doi.org/10.1029/2007JC004564.
Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M A, Balsamo G, Bauer P, Bechtold P, Beljaars A C M, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer A J and Vitart F 2011 The ERA-Interim reanalysis: Configuration and performance of the data assimilation system; Quart. J. Roy. Meteorol. Soc. 137 553–597, https://doi.org/10.1002/qj.828.
Feltham D 2015 Arctic sea ice reduction: The evidence, models and impacts; Phil. Trans. Roy. Soc. A 373 1–3, https://doi.org/10.1098/rsta.2014.0171.
Hewitt H T, Copsey D, Culverwell I D, Harris C M, Hill R S R, Keen A B, McLaren A J and Hunke E C 2011 Design and implementation of the infrastructure of HadGEM3: The next-generation Met Office climate modelling system; Geosci. Model Dev. 4 223–253, https://doi.org/10.5194/gmd-4-223-2011.
Hunke E C and Lipscomb W H 2008 CICE: The Los Alamos Sea Ice Model Documentation and Software User’s Manual Version 4.0; Los Alamos National Laboratory Tech. Rep. LA-CC-06-012, 76p.
Madec G and the NEMO team 2008 NEMO ocean engine. Note du Pole de mod´elisation’; Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No. 1288-161.
Parkinson C L and Cavalieri D J 2012 Antarctic sea ice variability and trends, 1979–2010; Cryosphere 6 871–880, https://doi.org/10.5194/tc-6-871-2012.
Parkinson C L 2019 A 40-yr record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic; Proc. Natl. Acad. Sci. USA 116 14,414–14,423, https://doi.org/10.1073/pnas.1906556116.
Parkinson C L and DiGirolamo N E 2016 New visualizations highlight new information on the contrasting Arctic and Antarctic sea-ice trends since the late 1970s; Remote Sens. Environ. 183 198–204, https://doi.org/10.1016/j.rse.2016.05.020.
Rae J G L, Hewitt H T, Keen A B, Ridley J K, West A E, Harris C M, Hunke E C and Walters D N 2015 Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model; Geosci. Model Dev. 8 2221–2230, https://doi.org/10.5194/gmd-8-2221-2015.
Rayner N A, Parker D E, Horton E B, Folland C K, Alexander L V, Rowell D P, Kent E C and Kaplan A 2003 Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century; J. Geophys. Res. 108 4407, https://doi.org/10.1029/2002JD002670.
Reynolds R W, Smith T M, Liu C, Chelton D B, Casey K S and Schlax M G 2007 Daily high-resolution-blended analyses for sea surface temperature; J. Clim. 20 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.
Saheed P P, Mitra A K, Momin I M, Keen A B, Rajagopal E N, Hewitt H T and Milton S F 2018 Arctic summer sea-ice seasonal simulation with a coupled model: Evaluation of mean features and biases; J. Earth Syst. Sci. 128 1–24, https://doi.org/10.1007/s12040-018-1043-z.
Semtner A J Jr 1976 A model for the thermodynamic growth of sea ice in numerical investigations of climate; J. Phys. Oceanogr. 6 379–389, https://doi.org/10.1175/1520-0485.
Zhang J L and Rothrock D A 2003 Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates; Mon. Wea. Rev. 131 845–861.
Acknowledgements
ORAP5 ocean reanalysis dataset was taken from http://marine.copernicus.eu. ERA-Interim reanalysis was taken from ECMWF. HadISST data was taken from the Hadley Center, UK. GIOMAS observed sea-ice datasets were taken from Polar Science Centre, University of Washington, USA. NEMO ocean model is part of the NEMO consortium. The CICE model was taken from the Los Alamos National Laboratory sea-ice model repository. The NCMRWF component of the work is done under the MoES Belmont forum project BITMAP.
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Saheed P P: Conceptualization, methodology, writing original draft, software. Ashis K Mitra, Imranali M Momin and Vimlesh Pant: Writing – review and editing.
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Communicated by C Gnanaseelan
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Saheed, P.P., Mitra, A.K., Momin, I.M. et al. Antarctic winter sea-ice seasonal simulation with a coupled model: Evaluation of mean features and biases. J Earth Syst Sci 130, 204 (2021). https://doi.org/10.1007/s12040-021-01714-y
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DOI: https://doi.org/10.1007/s12040-021-01714-y