Full-Depth Reconstruction of Long-Term Meridional Overturning Circulation Variability From Satellite-Measurable Quantities via Machine Learning

The meridional overturning circulation (MOC) plays a crucial role in the global distribution of heat, carbon, and other climate-relevant tracers. Monitoring the evolution of MOC is essential for understanding climate variability, yet direct MOC observations are sparse and geographically limited. Although satellite measurements have shown potential for short-term monitoring of the MOC, it remains unclear whether MOC variability on decadal and longer timescales can be detected remotely. In this study, we leverage machine learning to reconstruct long-term MOC variability from satellite-measurable quantities, using climate simulations under pre-industrial conditions. We demonstrate that our proposed non-local dual-branch neural network (DBNN) effectively reconstructs both the strength and vertical structure of the Atlantic MOC (AMOC) and the Southern Ocean MOCs across sub-annual to multi-decadal timescales. Using a neural network interpretation technique, we identify ocean bottom pressure near the western boundary and along dense-water export pathways as the dominant input features for MOC reconstruction. This indicates that DBNN's predictions can be interpreted as an approximation of geostrophic balance. The DBNN also effectively reconstructs the AMOC in the equatorial region, where geostrophy breaks down. This success is attributed to the capability of DBNN in utilizing latitudinally non-local ocean bottom pressure information and the meridional coherence of AMOC variability. Additionally, the DBNN accurately reconstructs Southern Ocean MOCs using only sea surface height and zonal wind stress as inputs, thereby avoiding reliance on ocean bottom pressure, which is subject to considerable measurement uncertainty in practice. This work demonstrates the possibility of continuous, long-term MOC monitoring using satellite measurements.