Unsupervised Classification Identifies Warm, Fresh, and Dense Regimes of the Antarctic Margins

Abstract

Abstract The ocean surrounding Antarctica, also known as the Antarctic margins, is characterized by complex and heterogeneous process interactions, which have major impacts on the global climate. A common way to understand changes in the Antarctic margins is to categorize regions into similar “regimes,” thereby guiding process-based studies and observational analyses. However, this categorization is traditionally largely subjective and based on temperature, density, and bathymetric criteria that are bespoke to the dataset being analyzed. In this work, we introduce a method to classify Antarctic shelf regimes using unsupervised learning. We apply Gaussian mixture modeling to the across-shelf temperature and salinity properties along the Antarctic margins from a high-resolution ocean model, ACCESS-OM2-01. Three clusters are found to be optimum based on the Bayesian information criterion and an assessment of regime properties. The three clusters correspond to the fresh, dense, and warm regimes identified canonically via subjective approaches. Our analysis allows us to track changes to these regimes in a future projection of the ACCESS-OM2-01 model. We identify the future collapse of dense water formation, and the merging of dense and fresh shelf regions into a single fresh regime that covers the entirety of the Antarctic shelf except for the West Antarctic. Our assessment of these clusters indicates that the Antarctic margins may shift into a two-regime system in the future, consisting only of a strengthening warm shelf in the West Antarctic and a fresh shelf regime everywhere else. Significance Statement The Antarctic margins are characterized by complex interactions of surface and ocean processes, producing distinct regions or “regimes.” Understanding where these regimes are and their future state is critical to understanding climate change. Based on a subjective assessment of ocean conditions, past research has identified fresh, dense, and warm regimes in the Antarctic margins. In this work, we use an unsupervised classification tool, Gaussian mixture modeling, to objectively identify the location of regimes around the Antarctic margins. Our method detects three regimes in an ocean model, which match the location of subjectively identified fresh, dense, and warm regimes, and indicates a future shrinking of the dense regime. Our method is adaptable to multiple datasets, enabling us to identify trends and processes in the Antarctic margins.