ML4WX

Climate analysis & attribution

Deep learning based reconstructions of the Atlantic meridional overturning circulation confirm twenty-first century decline

Simon Michel, Henk A. Dijkstra, Francesco Guardamagna, Valérian Jacques-Dumas, René M. van Westen, Anna S. von der Heydt

Environmental Research Letters · · University of Oxford

Paper

In one sentence. Deep learning reconstructs AMOC strength from 1900 using climate model simulations, finding a 21st-century decline consistent with models and 26.5°N observations, unlike older SST-based indices.

Abstract

Abstract Gaining knowledge of the past and present variations of the Atlantic meridional overturning circulation (AMOC) is crucial for the development of accurate future climate projections. The short range covered by direct AMOC observations, inconsistent paleoclimate records, and scattered hydrographic data are insufficient to realistically reconstruct the AMOC strength since 1900. An AMOC proxy index based on sea surface temperatures suggests that the AMOC has declined by 15% since the late 19th century but this index received extensive scientific criticism. Here, we use a deep learning algorithm and climate model simulations to accurately reconstruct the AMOC strength between 20° N and 60° N since 1900. In contrast with the existing indices, our reconstructions are well in agreement with AMOC strength variations simulated by climate models and direct observations at 26.5° N. Our novel set of AMOC reconstructions contribute to a larger confidence in 21st century AMOC decline projections from climate models.

Labels

Topic
Climate analysis & attribution, Benchmarks, datasets & evaluation
Type
New model or method
Time scale
Climate projections / centennial
Domain
Global
Data
CMIP / climate model output, Station / in-situ
Status
Research
Identifiers
DOI:10.1088/1748-9326/add7f0 OpenAlex:W4410335495
Added
2026-10-22

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