Drivers of the decline of the Atlantic meridional overturning circulation under climate change in a hierarchy of climate models
Bibliographic record
Abstract
Over the past decades, oceans have absorbed most of the heat energy added to the climate system by human activities, which has damped global warming. This heat energy is redistributed into the ocean interior by the Meridional Overturning Circulation (MOC) with large impacts on changes in regional and global climate. Changes in the MOC pattern and strength are thus important to understand and quantify. Here, we investigate the magnitude and spatial distribution of the climate response of the various drivers of the Atlantic MOC (AMOC), such as geostrophic transport, wind-driven transport, and mesoscale eddy transport. A hierarchy of three climate models of varying resolution in the ocean (1°, 0.25°, 0.10°) called the CM2-O suite is used. The AMOC shows the strongest reduction under climate change in the eddy-parameterized model (1°), while the weakest is found in the eddy-permitting model (0.25°). The decomposition of the AMOC into its drivers reveals that most of the AMOC reduction is due to a weakening of the geostrophic transport driven by temperature anomalies, partly opposed by a strengthening of the geostrophic transport driven by salinity anomalies. In contrast, changes in wind-driven transport have little effect, except in the eddy-rich model (0.10°), where it contributes significantly to the AMOC decline. Changes in the mesoscale eddy transport contribute to ~20% of the AMOC decline in the eddy-rich and eddy-parameterized models, but induce very little change in the eddy-permitting model. The key changes in density causing the geostrophic weakening occurs at intermediate depths, where the density anomalies are transported from deep-water formation region within the North Atlantic Deep Water
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".