Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss

TL;DR

This study introduces IceDMD, a regularized Dynamic Mode Decomposition model that forecasts Antarctic sea ice concentration anomalies up to two years ahead, outperforming existing methods and offering physical interpretability.

Contribution

The paper presents a novel regularized DMD-based predictive model for Antarctic sea ice, enabling accurate, interpretable forecasts with low computational cost.

Findings

Climate change signal in sea ice emerges by 2012 and dominates by 2022.

IceDMD outperforms existing forecasting approaches in accuracy.

The framework can be generalized to other multi-scale systems.

Abstract

Antarctic sea ice has undergone unprecedented changes in recent years, raising questions about how this key geophysical system is responding to climate change. Decades of slow expansion were replaced by a precipitous decline in 2014-2017, a subsequent apparent recovery, and a renewed collapse from 2022 to the present. We diagnosed sea ice concentration (SIC) from satellite observations with a hierarchical decomposition method based on Dynamic Mode Decomposition (DMD) that finds coherent spatiotemporal modes. We find that the 2014-2017 decline and apparent recovery are the result of interacting interannual modes and that a climate change signal emerges in 2012, which becomes unambiguous by 2022 when it dominates over interannual variability. These rapid changes underscore the need for seasonal-to-annual forecasts of SIC. However, existing forecasts are subject to limited prediction horizons combined with high computational costs. Our predictive DMD model (IceDMD) is regularised to prioritize the stationary spatiotemporal modes found by the decomposition. The predictive model can forecast SIC anomalies in 2023-2024 up to two years in advance, outperforming all existing approaches with the additional benefits of physical interpretability and extremely cheap computational cost. Finally, this framework for regularising predictive DMD models can be generalized to a range of multi-scale systems.