Summary
Long-range forecasts of Antarctic sea ice are tery much in demand, not only because of the potential importance of sea ice in global climate, but also for the practical purpose of exploring the Antarctic continent. Unfortunately, such forecasts are not yet feasible with any state-of-the-art general circulation models, because the complex air-sea-ice interaction processes on long timescales are still not well understood and are by no means well simulated by these models. An alternative is to apply statistical methods to Antarctic sea ice prediction. The linear Markov model used in this study represents one of the first attempts in this direction. The variability of Antarctic sea ice is likely to be controlled by both remote and local processes. The atmospheric anomalies from low latitudes could excite certain modes of the Antarctic climate system, which then could be amplified and sustained by the local air-seaice interaction. Here we explore the possibility of forecasting Antarctic sea ice anomalies using a technique combining multivariate empirical orthogonal function (MEOF) analysis and linear Markov prediction. Seven atmospheric variables along with sea ice were chosen to define the state of the Antarctic climate, and the MEOF of these variables were used as the building blocks of the model. The predictive skill of the model was evaluated in a cross-validated fashion, and a series of sensitivity experiments were carried out. In both hindcast and forecast experiments, the model showed considerable skill in predicting the anomalous Antarctic sea ice concentration a few seasons in advance, especially in austral winter and in the Antarctic dipole regions. The success of the model is attributed to the domination of the Antarctic climate variability by a few distinctive modes in the coupled air-sea-ice system, and to the model's ability to pick up these modes.