  Forecasting sea ice concentration (SIC) and sea ice drift (SID) in the Arctic
Ocean is of great significance as the Arctic environment has been changed by
the recent warming climate. Given that physical sea ice models require high
computational costs with complex parameterization, deep learning techniques can
effectively replace the physical model and improve the performance of sea ice
prediction. This study proposes a novel multi-task fully conventional network
architecture named hierarchical information-sharing U-net (HIS-Unet) to predict
daily 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 each
other's prediction through the weighting attention modules (WAMs).
Consequently, our HIS-Unet outperforms other statistical approaches, sea ice
physical models, and neural networks without such information-sharing units.
The improvement of HIS-Unet is obvious both for SIC and SID prediction when and
where sea ice conditions change seasonally, which implies that the information
sharing through WAMs allows the model to learn the sudden changes of SIC and
SID. The weight values of the WAMs imply that SIC information plays a more
critical role in SID prediction, compared to that of SID information in SIC
prediction, and information sharing is more active in sea ice edges (seasonal
sea ice) than in the central Arctic (multi-year sea ice).
