High‐Frequency Submesoscale Motions Enhance the Upward Vertical Heat Transport in the Global Ocean
Bibliographic record
Abstract
Abstract The rate of ocean heat uptake depends on the mechanisms that transport heat between the surface and the ocean interior. A recent study found that the vertical heat transport driven by motions with scales smaller than 0.5° (submesoscales) and frequencies smaller than 1 day −1 is upward. This transport competes with the other major components of the global heat transport, namely, the downward heat transport explained by the large‐scale wind‐driven vertical circulation and vertical diffusion at small scales and the upward heat transport associated with mesoscale eddies (50‐ to 300‐km size). The contribution from motions with small spatial scales (<0.5°) and frequencies larger than 1 day −1 , including internal gravity waves, has never been explicitly estimated. This study investigates this high‐frequency (subdaily) submesoscale contribution to the global heat transport. The major result of this study, based on the analysis of a high‐resolution ocean model, is that including this high‐frequency contribution surprisingly doubles the upward heat transport due to submesoscales in winter in the global ocean. This contribution typically concerns depths down to 200–500 m and can have a magnitude of up to 500 W m −2 in terms of wintertime heat fluxes at 40‐m depth, which causes a significant upward heat transport of ~7 PW when integrated over the global ocean. Thus, such submesoscale heat transport, which is not resolved by climate models, impacts the heat uptake in the global ocean. The mechanisms involved in these results still need to be understood, which should be the scope of future work.
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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 | 0.001 |
| 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".