IceCT: A MoE‐Based Model for Seasonal Arctic Sea Ice Concentration and Thickness Prediction

Abstract The rapid decline of Arctic sea ice requires accurate prediction of its concentration (SIC) and thickness (SIT). We introduce IceCT, a deep learning model using a Mixture‐of‐Experts (MoE) framework to generate monthly SIC and SIT forecasts at a 12.5 km resolution up to 6 months ahead. Its architecture features two specialized experts—one for SIC, one for SIT—enhanced with Multi‐Head Self‐Attention (MHSA) to capture spatial patterns. A novel Spatial Gate adaptively weights the experts' contributions across the grid. Evaluations show IceCT outperforms other deep learning baselines, achieving an overall Mean Absolute Error (MAE) of 4.56% for SIC and 0.140 m for SIT, and achieves enhanced predictive skill in forecasting sea ice during the crucial summer melting period. Additionally, the model mitigates the Arctic spring predictability barrier by learning to rely more on SIT information for forecasts initialized in spring.