  Complex ocean systems such as the Antarctic Circumpolar Current play key
roles in the climate, and current models predict shifts in their strength and
area under climate change. However, the physical processes underlying these
changes are not well understood, in part due to the difficulty of
characterizing and tracking changes in ocean physics in complex models. Using
the Antarctic Circumpolar Current as a case study, we extend the method
Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy
permitting climate model and identify regions of the ocean characterized by
similar physics, called dynamical regimes, using readily accessible fields from
climate models. To this end, we cluster grid cells into dynamical regimes and
train an ensemble of neural networks, allowing uncertainty quantification, to
predict these regimes and track them under climate change. Finally, we leverage
this new knowledge to elucidate the dynamical drivers of the identified regime
shifts as noted by the neural network using the 'explainability' methods SHAP
and Layer-wise Relevance Propagation. A region undergoing a profound shift is
where the Antarctic Circumpolar Current intersects the Pacific-Antarctic Ridge,
an area important for carbon draw-down and fisheries. In this region, THOR
specifically reveals a shift in dynamical regime under climate change driven by
changes in wind stress and interactions with bathymetry. Using this knowledge
to guide further exploration, we find that as the Antarctic Circumpolar Current
shifts north under intensifying wind stress, the dominant dynamical role of
bathymetry weakens and the flow intensifies.
