Revealing mechanisms of change in the Atlantic Meridional Overturning Circulation under global heating
<p>The North Atlantic ocean is key to climate through its role in heat transport and storage, but the response of the circulation&#8217;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 CO<sub>2</sub> quadrupling, due to a shift in deep water formation areas and locations of the Gulf Stream and Trans Atlantic Current transporting heat northward. If CO<sub>2</sub> is increased 1% yearly, similar but transient patterns emerge. THOR could accelerate model analysis and facilitate process oriented intercomparisons.</p>