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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Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nat. Commun, 12, 1, https://doi.org/10.1038/s41467-021-25257-4, 2021. 
Blackport, R., Screen, J. A., van der Wiel, K., and Bintanja, R.: Minimal influence of reduced Arctic sea ice on coincident cold winters in mid-latitudes, Nat. Clim. Chang., 9, 697–704, https://doi.org/10.1038/s41558-019-0551-4, 2019. 
Blanchard-Wrigglesworth, E., Armour, K. C., Bitz, C. M., and Deweaver, E.: Persistence and inherent predictability of arctic sea ice in a GCM ensemble and observations, J. Climate, 24, 231–250, https://doi.org/10.1175/2010JCLI3775.1, 2011. 
Blanchard-Wrigglesworth, E., Cullather, R. I., Wang, W., Zhang, J., and Bitz, C. M.: Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook, Geophys. Res. Lett., 42, 8042–8048, https://doi.org/10.1002/2015GL065860, 2015. 
Blanchard-Wrigglesworth, E., Bushuk, M., Massonnet, F., Hamilton, L. C., Bitz, C. M., Meier, W. N., and Bhatt, U. S.: Forecast Skill of the Arctic Sea Ice Outlook 2008–2022, Geophys. Res. Lett., 50, 6, https://doi.org/10.1029/2022GL102531, 2023. 
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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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