Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 9
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.
https://doi.org/10.5194/gmd-18-2665-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 9
Development and technical paper
 | 
14 May 2025
Development and technical paper |  | 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, Xiaofeng Li, and Yunhe Wang

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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.
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