Articles | Volume 18, issue 9
https://doi.org/10.5194/gmd-18-2665-2025
© 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/gmd-18-2665-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
14 May 2025
Development and technical paper |
| 14 May 2025
| 14 May 2025
SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data
Yibin Ren
Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Qingdao Key Laboratory of Artificial Intelligence Oceanography, Qingdao, China
Xiaofeng Li
CORRESPONDING AUTHOR
Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Qingdao Key Laboratory of Artificial Intelligence Oceanography, Qingdao, China
Yunhe Wang
Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Qingdao Key Laboratory of Artificial Intelligence Oceanography, Qingdao, China
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Short summary
This study developed a transformer-based deep learning model to predict the Arctic sea ice seasonally. By integrating the sea ice thickness data into the model, the spring prediction barrier of Arctic sea ice is optimized significantly. The model achieves better skills than the typical numerical model in predicting September’s sea ice extent seasonally. The sea ice thickness data play a key role in reducing the prediction errors of the Beaufort Sea, the East Siberian Sea and the Laptev Sea.
This study developed a transformer-based deep learning model to predict the Arctic sea ice...
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