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Antarctic sea ice has experienced rapid change in recent years, with the total sea ice extent abruptly decreasing after a period of gradual increase from the late 1970s until 2014. Accurate long-term predictions of Antarctic sea ice concentration by dynamical or machine learning models are crucial for supporting the expanding activities in the Southern Ocean, related to for instance scientific research, tourism and fisheries. However, dynamical models often face difficulties in accurately predicting Antarctic sea ice due to limited representations of air-ice-sea interactions, especially on seasonal timescales and during the summer months. In addition, existing deep learning approaches typically rely on historical sea ice data, neglecting the complex interactions between sea ice and other climate variables, and lack interpretability of the underlying physical processes. Moreover, little attention has been paid to extended seasonal forecasts, and systematic evaluations of the predictive skill during extreme years remain scarce. To address these challenges and gaps, we here develop a deep learning model (named ANTSIC-UNet), trained by multiple climate variables, and evaluate its skill for extended up-to-six-months seasonal prediction of Antarctic sea ice concentration. We compare the predictive skill of ANTSIC-UNet in the Pan- and regional Antarctic with two benchmark models (a linear trend and an anomaly persistence model) and a dynamical model (SEAS5). In terms of root-mean-square error (RMSE) of sea ice concentration and integrated ice-edge error (IIEE), ANTSIC-UNet shows much better skills relative to the other models for the extended seasonal prediction, especially for the extreme events in recent years. Sea ice prediction errors increase with lead time, and are smaller during autumn and winter than in summer. The Pacific and Indian Oceans show accurate prediction performance at the sea ice edge during summer, and ANTSIC-UNet provides high predictive skill in capturing the interannual variability of Pan-Antarctic and regional sea ice extent anomalies. In addition, we quantify the importance of variables through a post-hoc interpretation method. This analysis suggests that the ANTSIC-UNet prediction at short lead times is sensitive to sea surface temperature, radiative flux, and atmospheric circulation in addition to sea ice conditions. At longer lead times, zonal wind in the stratosphere appears to be an important influencing factor for the prediction. Building on these findings, we further demonstrate that incorporating physical constraints into deep learning models potentially leads to a gain in the accuracy of the Antarctic sea ice edge prediction on extended seasonal timescales.

Publisher: Copernicus Publications