Revealing mechanisms of change in the Atlantic Meridional Overturning Circulation under global heating

The North Atlantic ocean is key to climate through its role in heat transport and storage, but the response of the circulation’s drivers to a changing climate is poorly constrained. The transparent machine learning method Tracking global Heating with Ocean Regimes (THOR) identifies drivers of circulation with minimal input: depth, dynamic sea level and wind stress. Beyond a black box approach, THOR's predictive skill is transparent. A dataset is created with features engineered and labeled by an explicitly interpretable equation transform and k-means application. A multilayer perceptron is then trained, explaining its skill using relevance maps and theory. THOR reveals a weakened circulation with abrupt CO₂ quadrupling, due to a shift in deep water formation areas and locations of the Gulf Stream and Trans Atlantic Current transporting heat northward. If CO₂ is increased 1% yearly, similar but transient patterns emerge. THOR could accelerate model analysis and facilitate process oriented intercomparisons.