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Forecasting sea ice concentration (SIC) and sea ice drift (SID) in the ArcticOcean is of great significance as the Arctic environment has been changed bythe recent warming climate. Given that physical sea ice models require highcomputational costs with complex parameterization, deep learning techniques caneffectively replace the physical model and improve the performance of sea iceprediction. This study proposes a novel multi-task fully conventional networkarchitecture named hierarchical information-sharing U-net (HIS-Unet) to predictdaily SIC and SID. Instead of learning SIC and SID separately at each branch,we allow the SIC and SID layers to share their information and assist eachother's prediction through the weighting attention modules (WAMs).Consequently, our HIS-Unet outperforms other statistical approaches, sea icephysical models, and neural networks without such information-sharing units.The improvement of HIS-Unet is obvious both for SIC and SID prediction when andwhere sea ice conditions change seasonally, which implies that the informationsharing through WAMs allows the model to learn the sudden changes of SIC andSID. The weight values of the WAMs imply that SIC information plays a morecritical role in SID prediction, compared to that of SID information in SICprediction, and information sharing is more active in sea ice edges (seasonalsea ice) than in the central Arctic (multi-year sea ice).

Multi-task Deep Convolutional Network to Predict Sea Ice Concentration and Drift in the Arctic Ocean