Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-731-2025
https://doi.org/10.5194/tc-19-731-2025
Research article
 | 
18 Feb 2025
Research article |  | 18 Feb 2025

Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach

Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino

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Cited articles

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Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011 to 2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes in sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
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