Multi-centennial modulation of Atlantic multi-decadal variability in a 2000-year climate integration
Bibliographic record
Abstract
We explore the amplitude and frequency of Atlantic Multi-decadal Variability (AMV) in a 2,000-year pre-industrial control simulation with the FOCI-OpenIFS coupled climate model. We find a statistically significant AMV-like mode on the 20-year and 80-year time scales. We also find a mode of multi-centennial variability where the North Atlantic Ocean shifts a regime of a warm period to/from a cold period of ~400 years. The warm period is characterised by mean states of a stronger and deeper Atlantic Meridional Overturning Circulation (AMOC), less Arctic sea ice, and more deep convection in the Labrador Sea than the cold period. We find that the AMV has a much higher amplitude in the cold period compared to the warm period, and also that the lead-lag relationship between the AMOC and the AMV is different between the two periods. In the warm period, AMOC leads the AMV; a strong AMOC enhances the oceanic poleward heat transport which warms the North Atlantic Ocean both at the surface and deeper down, producing a positive AMV. In the cold period, however, AMV leads AMOC; a warm surface anomaly reduces the sea ice in the Labrador Sea which enhances local air-sea interactions and deep convection, and later a stronger AMOC. In the cold period, the warm anomaly associated with the AMV does not extend below the mixed layer, suggesting that it is driven by the atmosphere and not ocean dynamics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".