Covariations between persistent synoptic features and Antarctic sea ice via unsupervised regression learning

Abstract. During the past decade, a succession of record low sea ice events has led to the suggestion that a shift in the overall dynamics of sea ice in the Antarctic region is underway. We attempt to gain fresh insight into how atmospheric drivers may play a role in these anomalous events, particularly their influence on Antarctic sea ice retreat in the warmer months, by studying coupled atmosphere-sea ice variability during the 2016–2017, 2021–2022, and 2023 low sea ice concentration years. We construct a reduced-order model from reanalyzed observations based on a well-developed machine learning algorithm incorporating coupling across subsystems, namely the atmosphere and sea ice. Background persistent events occurring throughout the years of interest are extracted by employing non-stationary transition matrix methods to the resultant temporal sequence of states. These events are analyzed by considering the associated surface pressure, winds, temperature, and sea ice concentration. The results show that persistent patterns in the atmosphere qualitatively affect the rate and spatial patterns of sea ice growth and retreat. In periods of quiescent synoptic variability, we find the a general warming pattern to be the dominant influence on sea ice retreat. Our non-stationary approach provides additional insight over and above what can be inferred from simple monthly or seasonal averages alone, particularly in capturing drivers across varying temporal scales.