Machine Learning-Derived Inference of the Meridional Overturning Circulation From Satellite-Observable Variables in an Ocean State Estimate

The oceanic Meridional Overturning Circulation (MOC) plays a key role in the climate system, and monitoring its evolution is a scientific priority. Monitoring arrays have been established at several latitudes in the Atlantic Ocean, but other latitudes and oceans remain unmonitored for logistical reasons. This study explores the possibility of inferring the MOC from globally-available satellite measurements via machine learning (ML) techniques, using the ECCOV4 state estimate as a test bed. The methodological advantages of the present approach include the use purely of available satellite data, its applicability to multiple basins within a single ML framework, and the ML model simplicity (a feed-forward fully connected neural network (NN) with small number of neurons). The ML model exhibits high skill in MOC reconstruction in the Atlantic, Indo-Pacific, and Southern Oceans. The approach achieves a higher skill in predicting the model Southern Ocean abyssal MOC than has previously been achieved via a dynamically-based approach. The skill of the model is quantified as a function of latitude in each ocean basin, and of the time scale of MOC variability. We find that ocean bottom pressure generally has the highest reconstruction skill potential, followed by zonal wind stress. We additionally test which combinations of variables are optimal. Furthermore, ML interpretability techniques are used to show that high reconstruction skill in the Southern Ocean is mainly due to (NN processing of) bottom pressure variability at a few prominent bathymetric ridges. Finally, the potential for reconstructing MOC strength estimates from real satellite measurements is discussed.