Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting.

Predicting the Arctic sea ice extent is a notoriously difficult forecasting problem, even from lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as sea surface temperature reemergence and sea ice anomaly reemergence, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog's historical trajectory. We use an ensemble of analogs for our forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting regional and pan-Arctic sea ice concentration and volume anomalies from multi-century climate model data, and in many cases find improvement over the damped persistence forecast. Moreover the patterns of predictive skill we see by region and season are consistent with different types of sea ice anomaly reemergence.