Long‐Term Historical Slowdown in Atlantic Meridional Overturning Circulation

ABSTRACT Atlantic Meridional Overturning Circulation (AMOC) is an important component of the climate system that profoundly affects the climate of surrounding areas. AMOC is expected to slowdown due to anthropogenic climate change. The scientific community has long been at odds regarding how much AMOC has already slowed down, and whether there was any weakening at all. Key reasons for this conundrum are a limited direct observational record and the presence of substantial natural variability. The existing reconstruction methods using simple statistical approaches are not adequate to address these challenges. Moreover, they typically provide results without any uncertainty estimates. Here, we utilize a climate model ensemble experiment and a deep learning‐based approach to reconstruct the long‐term historical AMOC trend based on direct and indirect AMOC observations, while filtering out natural variability and providing adequate uncertainty quantification. Our analysis rules out the possibility of no decline with greater than 95% probability.

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Publication Details

Journal
Environmetrics
Published
2026-05-01
DOI
https://doi.org/10.1002/env.70101
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Long‐Term Historical Slowdown in Atlantic Meridional Overturning Circulation

Won Chang, Yizi Cheng, Roman Olson, Jongsoo Shin et al.
Environmetrics
Climate variability and models
article

Long‐Term Historical Slowdown in Atlantic Meridional Overturning Circulation

Won Chang, Yizi Cheng, Roman Olson, Jongsoo Shin, Soon‐Il An
article en

Abstract

ABSTRACT Atlantic Meridional Overturning Circulation (AMOC) is an important component of the climate system that profoundly affects the climate of surrounding areas. AMOC is expected to slowdown due to anthropogenic climate change. The scientific community has long been at odds regarding how much AMOC has already slowed down, and whether there was any weakening at all. Key reasons for this conundrum are a limited direct observational record and the presence of substantial natural variability. The existing reconstruction methods using simple statistical approaches are not adequate to address these challenges. Moreover, they typically provide results without any uncertainty estimates. Here, we utilize a climate model ensemble experiment and a deep learning‐based approach to reconstruct the long‐term historical AMOC trend based on direct and indirect AMOC observations, while filtering out natural variability and providing adequate uncertainty quantification. Our analysis rules out the possibility of no decline with greater than 95% probability.

EnvironmetricsVol. 37(4)
Seoul National University (KR), Tokyo University of Information Sciences (JP), Chiba Institute of Science (JP), Yonsei University (KR), University of Cincinnati (US), Woods Hole Oceanographic Institution (US), Advanced Institute of Industrial Technology (JP), The University of Tokyo (JP)
Woods Hole Oceanographic Institution, Environmental Restoration and Conservation Agency, Seoul National University, Ministry of Education, National Research Foundation of Korea, National Institute of Polar Research, Korea Research Environment Open Network, Japan Society for the Promotion of Science, Japan Science and Technology Agency, Japan Aerospace Exploration Agency, National Supercomputing Center, Korea Institute of Science and Technology Information, JST-Mirai Program
Climate action
Openalex Percentile: Top 7%
Climate variability and models
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