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The global ocean is central to the planet’s health and modulates global levels of heat and carbon, biological productivity, and sea level. However, open questions remain about what drives the circulation which hinders our understanding and ability to monitor ongoing, rapid changes. Climate models suggest that the ocean surrounding Antarctica, a critical region, is changing. However, the limited observations in one of the Earth’s most extreme and inaccessible environments poorly constrain the physical drivers. Here, machine learning is combined with equation driven physical understanding to construct hypotheses that lead to new theoretical understanding of the circulation, and to design a monitoring framework that can assess sensitivity to climate change. Monitoring the circulation is challenging because observations are generally limited to sparse data from the surface. With our theoretical insight, we developed a new physics-informed methodology to fill this gap using available data. The monitoring method Tracking global Heating with Ocean Regimes (THOR) can ‘reason’ using geophysical fluid dynamics. It is explicitly transparent and consists of a series of neural networks that combine eXplainable AI and Bayesian confidence scores for its predictions. We reveal differences in model physics that cause model divergence and spread in projections, opening the door to further discovery and observational strategies.