Covariations between persistent synoptic features and record low Antarctic sea ice events 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 persistent atmospheric states 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 then extracted via 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 covary with the rate and spatial patterns of sea ice growth and retreat, noting that some periods of retreat coincide with warm surface temperatures and relatively quiescent synoptic winds. Our non-stationary approach provides additional insight over and above what can be inferred from simple monthly or seasonal averages alone, particularly in capturing events across varying temporal scales.