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The impacts of optimizing model-dependent parameters on the Antarctic sea ice data assimilation
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  • Hao Luo,
  • Qinghua Yang,
  • Matthew R. Mazloff,
  • Lars Nerger,
  • Dake Chen
Hao Luo
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
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Qinghua Yang
Sun Yat-sen University

Corresponding Author:yangqh25@mail.sysu.edu.cn

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Matthew R. Mazloff
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Lars Nerger
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research
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Dake Chen
State Key Laboratory of Satellite Ocean Environment Dynamics (SOED)
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Given the role played by the historical and extensive coverage of sea ice concentration (SIC) observations in reconstructing the long-term variability of Antarctic sea ice, and the limited attention given to model-dependent parameters in current sea ice data assimilation studies, this study focuses on enhancing the performance of the Data Assimilation System for the Southern Ocean (DASSO) in assimilating SIC through optimizing the localization and observation error estimate, and two assimilation experiments were conducted from 1979 to 2018. By comparing the results with the sea ice extent of the Southern Ocean and the sea ice thickness in the Weddell Sea, it becomes evident that the experiment with optimizations outperforms that without optimizations due to achieving more reasonable error estimates. Investigating uncertainties of the SIV anomaly modeling reveals the nonnegligible role played by the sea ice-ocean interaction during the SIC assimilation, implying the necessity of assimilating more oceanic and sea-ice observations.
28 Jul 2023Submitted to ESS Open Archive
04 Aug 2023Published in ESS Open Archive