Data-driven identification of atmospheric drivers of anomalous Antarctic sea ice loss

Over the past decade there have been unprecedented events of record low sea ice concentration in the Antarctic region. Previous work has attributed these anomalous sea ice loss events to persistent anomalies in various atmospheric drivers such as the Southern Annular Mode (SAM), the Pacific South American (PSA) patterns, and the Amundsen Sea Low (ASL). The majority of such studies employ methodologies that either assume stationarity or use averages over uniform fixed periods (e.g. months). In this study we show how a machine learning method applied to multiscale climate data can extract drivers across subsystems without predefining patterns or time periods. Specifically, we employ a nonstationary data-clustering framework to coupled sea ice and atmosphere reanalysis data to extract persistent coherent events across both systems. We use time-varying Markov transition matrices to extract the dominant states over a sliding time window and identify persistence as an uninterrupted period of a dominant state for at least ten days.

Analysing three years consisting of anomalously low sea ice events, we find that our approach identifies a variety of atmospheric drivers for these events without preconditioning. The dominant drivers vary in spatial extent and duration, as opposed to many stationary methods which require an a priori selection of scales. Here each event’s spatial and temporal boundaries are determined by the optimal model itself. This nonstationary analysis is thus particularly valuable for characterizing multiscale interactions and addressing dynamics across coupled climate subsystems.