Journal Article

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

Wei, Huaiyu, Srinivasan, Kaushik, Stewart, Andrew L., Solodoch, Aviv, Hogg, Andrew McC, Manucharyan, Georgy E.

2025

Overview

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. Plain Language Summary The meridional overturning circulation (MOC) is a key ocean circulation system that moves heat, carbon, and other important tracers throughout the globe. Changes in the MOC, especially over decades or longer, greatly influence global climate. It is important to track these changes to better understand climate variability, but direct MOC measurements are logistically challenging and resource‐intensive. A possible solution is using satellite data, like sea surface height, to monitor the MOC remotely. Previous research has managed to track monthly‐to‐yearly changes of MOC using this indirect method, but it remains unclear if this can be done over multi‐year or multi‐decadal periods. In this study, we demonstrate the capability of “neural networks” to achieve long‐term MOC monitoring from quantities that satellites can measure, using simulations of hundreds to thousands of years of climate evolution as a test bed. Our approach also performs well near the equator, where traditional methods often fail. We additionally applied a neural network interpretation technique, which reveals that its prediction of the MOC primarily uses local and non‐local information about east‐to‐west pressure changes, consistent with physical expectations. Our results thus provide a pathway toward accurate monitoring of the MOC using satellite data over climate‐relevant timescales. Key Points We developed a non‐local dual‐branch neural network to reconstruct the long‐term variability of the meridional overturning circulation (MOC) Ocean bottom pressure near western boundaries and along dense water export pathways are identified as the dominant input features Latitudinally non‐local ocean bottom pressure information is crucial for equatorial Atlantic MOC reconstruction

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: Atlantic Meridional Overturning Circulation (AMOC) / Basins / Bottom pressure / Climate / Climate variability / Geostrophy / Machine learning / meridional overturning circulation / neural network / Neural networks / ocean bottom pressure / Ocean circulation / Ocean floor / Oceans / physical oceanography / Proxies / satellite observation / Satellites / Sea surface / Tracers / Variability / Vertical profiles / Wind stress / Zonal winds