Predicting Arctic Sea Ice Anomalies with Kernel Ensemble Analog Forecasting

Motivated by Arctic intra-annual variability phenomena such as SST \& sea ice re-emergence, 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 kernel, which take into account nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan Arctic and regional sea ice concentration anomalies from CCSM4 model data, and in many cases find improvement over the persistence forecast, notably a 2 month increase in predicting September sea ice extent.