Interannual Variability of the Global Meridional Overturning Circulation Dominated by Pacific Variability
Bibliographic record
Abstract
Abstract The most prominent feature of the time-mean global meridional overturning circulation (MOC) is the Atlantic MOC (AMOC). However, interannual variability of the global MOC is shown here to be dominated by Pacific MOC (PMOC) variability over the full depth of the ocean at most latitudes. This dominance of interannual PMOC variability is robust across modern climate models and an observational state estimate. PMOC interannual variability has large-scale organization, its most prominent feature being a cross-equatorial cell spanning the tropics. Idealized experiments show that this variability is almost entirely wind driven. Interannual anomalies of zonal mean zonal wind stress produce zonally integrated Ekman transport anomalies that are larger in the Pacific Ocean than in the Atlantic Ocean, simply because the Pacific is wider than the Atlantic at most latitudes. This contrast in Ekman transport variability implies greater variability in the near-surface branch of the PMOC when compared with the near-surface branch of the AMOC. These near-surface variations in turn drive compensating flow anomalies below the Ekman layer. Because the baroclinic adjustment time is longer than a year at most latitudes, these compensating flow anomalies have baroclinic structure spanning the full depth of the ocean. Additional analysis reveals that interannual PMOC variations are the dominant contribution to interannual variations of the global meridional heat transport. There is also evidence of interaction between interannual PMOC variability and El Niño–Southern Oscillation.
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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 | 0.000 |
| 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 | 0.000 | 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".