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Season-Aware Hybrid Convolutional-Transformer for Antarctic Sea Ice Concentration Forecasting

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Antarctic sea ice concentration (SIC) forecasting is an important yet challenging task due to the coexistence of complex spatial structure, long-range temporal dependencies, and strong seasonal variability. Conventional convolution-based models are effective at capturing local spatial patterns, but often have limited ability to model long-term temporal evolution. To address these challenges, we build on a hybrid Convolutional-Transformer forecasting framework for monthly Antarctic SIC forecasting. This framework combines convolutional encoding for spatial feature extraction with factorised sel

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