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

## TL;DR

This paper introduces a kernel analog forecasting method for Arctic sea ice anomalies, leveraging nonlinear data geometry and ensemble analogs to improve short- to medium-term predictions over traditional methods.

## Contribution

The paper presents a novel ensemble analog forecasting approach using a dynamics-adapted kernel, enhancing sea ice anomaly predictions in the Arctic from climate model data.

## Key findings

- Improved 3-6 month pan-Arctic sea ice area forecasts.
- Successful 3-12 month sea ice volume anomaly predictions in central Arctic basins.
- Enhanced forecast accuracy in regions with high interannual variability.

## 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.