Title: Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting,
Abstract: Predicting Arctic sea ice extent is a notoriously difficult forecasting
problem, even for lead times as short as one month. Motivated by Arctic
intraannual variability phenomena such as reemergence of sea surface
temperature and sea ice anomalies, 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. Here an ensemble of analogs are used to make 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 pan-Arctic and regional sea ice area and
volume anomalies from multi-century climate model data, and in many cases find
improvement over the benchmark damped persistence forecast. Examples of success
include the 3--6 month lead time prediction of pan-Arctic area, the winter sea
ice area prediction of some marginal ice zone seas, and the 3--12 month lead
time prediction of sea ice volume anomalies in many central Arctic basins. We
discuss possible connections between KAF success and sea ice reemergence, and
find KAF to be successful in regions and seasons exhibiting high interannual
variability.