Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6381-2025
https://doi.org/10.5194/tc-19-6381-2025
Research article
 | 
01 Dec 2025
Research article |  | 01 Dec 2025

Extended seasonal prediction of Antarctic sea ice concentration using ANTSIC-UNet

Ziying Yang, Jiping Liu, Mirong Song, Yongyun Hu, Qinghua Yang, Ke Fan, Rune Grand Graversen, and Lu Zhou

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Short summary
Antarctic sea ice has changed rapidly in recent years. Here we developed a deep learning model trained by multiple climate variables for extended seasonal Antarctic sea ice prediction. Our model shows high predictive skills up to 6 months in advance, particularly in predicting extreme events. It also shows skillful predictions at the sea ice edge and year-to-year sea ice changes. Variable importance analyses suggest what variables are more important for prediction at different lead times.
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