Unsupervised classification identifies warm, fresh and dense regimes of the Antarctic margins

Abstract

The ocean surrounding Antarctica, also known as the Antarctic margins, is characterised 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 categorise regions into similar ‘regimes’, thereby guiding process-based studies and observational analyses. However, this categorisation is traditionally largely subjective and based on temperature, density and bathymetric criteria that are bespoke to the dataset being analysed. In this work, we introduce a method to classify Antarctic shelf regimes using unsupervised learning. We apply Gaussian Mixture Modelling 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.