Exploring AMOC Regime Change over the Past Four Decades through Ocean Reanalyses
Bibliographic record
Abstract
We examine North Atlantic climate variability using an ensemble of ocean reanalysis datasets to study the Atlantic Meridional Overturning Circulation (AMOC) from 1979 to 2018. The dataset intercomparison shows good agreement for the latest period (1995–2018) for AMOC dynamics, characterized by a weaker overturning circulation after 1995 and a more intense one during 1979–1995, with varying intensity across the various datasets. The correlation between leading empirical orthogonal functions suggests that the AMOC weakening has connections with cooler (warmer) sea surface temperature (SST) and lower (higher) ocean heat content in the subpolar (subtropical) gyre in the North Atlantic. Barotropic stream function and Gulf Stream index reveal a shrinking subpolar gyre and an expanding subtropical gyre during the strong-AMOC period and vice versa, consistently with Labrador Sea deep convection reduction. We also observed an east–west salt redistribution between the two periods. Additional analyses show that the AMOC variability is related to the North Atlantic Oscillation phase change around 1995. One of the datasets included in the comparison shows an overestimation of AMOC variability, notwithstanding the model SST bias reduction via ERA-Interim flux adjustments: further studies with a set of numerical experiments will help explain this behavior.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".