Monthly Arctic Sea-Ice Prediction With a Linear Inverse Model

Abstract

We evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea-ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. We find that more than 500 years of training data and 100 years of validation data are needed to reliably estimate LIM forecast skill. The best LIM has skill up to 8 months lead time and outperforms an autoregressive model of order one (AR1) forecast at all locations, with particularly large outperformance near the ice edge. However, for out-of-sample validation tests using data from various different model simulations and reanalysis products, they underperform an AR1 model due to differences in the location of the sea-ice edge from the training data. We present a metric for predicting LIM forecast skill, based on the spatial correlation of the variance in the training and validation data sets.