Interannual Variability of the Heat Budget in the Tropical Pacific Ocean and Its Link to the Overturning Circulation
Bibliographic record
Abstract
Abstract Using a suite of coupled climate models and an extensive set of ocean heat budget diagnostics, we address the relative roles of heat convergence and surface heat flux in driving the annual rate of ocean heat content (OHC) change in the tropical Pacific and its interannual variability. The net heat convergence is further separated into convergences associated with the large‐scale ocean circulation, (parameterized) mesoscale effects and small‐scale mixing. It is found that the heat convergence due to the large‐scale ocean circulation provides the dominant contribution to the annual OHC tendency. Interannual variations of heat convergence are larger in the tropical Pacific than in the tropical Atlantic. These heat convergence variations are linked to interannual variations of the Pacific meridional overturning circulation (PMOC), driven by the associated variations in the northward Ekman transport (EkT). Northward variations of the tropical PMOC and EkT are typically associated with heat divergence and negative annual OHC tendency in the central and eastern near‐equatorial Pacific along with heat convergence and positive annual OHC tendency in the western and northwestern tropical Pacific. In the Niño3.4 region, interannual variations of the near‐surface OHC tendency negatively (positively) correlate with interannual PMOC variations at zero lag (1 year lag, when PMOC leads OHC).
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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 | 0.000 | 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".