Articles | Volume 19, issue 8
https://doi.org/10.5194/tc-19-3279-2025
© Author(s) 2025. 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-19-3279-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2025
Research article |
| 26 Aug 2025
| 26 Aug 2025
Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning
Zikang He
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China
College of Oceanography, Hohai University, Nanjing, 210098, China
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Jahnebakken 3, Bergen, 5007, Norway
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Jahnebakken 3, Bergen, 5007, Norway
Julien Brajard
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Jahnebakken 3, Bergen, 5007, Norway
Xidong Wang
CORRESPONDING AUTHOR
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China
College of Oceanography, Hohai University, Nanjing, 210098, China
Zheqi Shen
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China
College of Oceanography, Hohai University, Nanjing, 210098, China
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Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show that both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and...
