SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data
<p>The Arctic sea ice suffers dramatic retreat in summer and fall, which has far-reaching consequences for the global climate and commercial activities. Accurate seasonal sea ice predictions significantly infer climate change and are crucial for planning commercial activities. However, seasona...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-05-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/2665/2025/gmd-18-2665-2025.pdf |
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