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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 …