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Improving Arctic sea-ice thickness estimates with the assimilation of CryoSat-2 summer observations
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  • Chao Min,
  • Qinghua Yang,
  • Hao Luo,
  • Jack Christopher Landy,
  • Dake Chen,
  • Geoffrey Joseph Dawson,
  • Thomas Krumpen,
  • Nabir Mamnun,
  • Xiaoyu Liu,
  • Lars Nerger
Chao Min
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:[email protected]

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Hao Luo
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
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Jack Christopher Landy
University of Tromsø - The Artic University of Norway
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Dake Chen
State Key Laboratory of Satellite Ocean Environment Dynamics (SOED)
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Geoffrey Joseph Dawson
University of Bristol
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Thomas Krumpen
Alfred Wegener Institute
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Nabir Mamnun
Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research
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Xiaoyu Liu
Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research
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Lars Nerger
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research
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Abstract

Rapidly shrinking Arctic sea ice has had significant impacts on the Arctic Ocean and many outer Arctic regions. It is therefore urgently needed to reliably estimate Arctic sea-ice thickness (SIT) by combined use of available observation and numerical modeling. Here, for the first time, we assimilate the latest CryoSat-2 summer SIT data into a coupled ice-ocean model. In particular, an Incremental Analysis Update scheme is implemented to overcome the discontinuity brought by assimilating biweekly SIT and daily sea ice concentration data. Along with an improvement in sea ice volume, our SIT estimates have smaller errors than that without SIT assimilation in areas where the sea ice is roughest and experiences strong deformation, e.g., around the Fram Strait and Greenland. This study suggests that the newly-developed CryoSat-2 SIT product, when assimilated properly with our approach, has great potential for Arctic sea ice simulation and prediction.
23 Feb 2023Submitted to ESS Open Archive
27 Feb 2023Published in ESS Open Archive