Bottom-Enhanced Diapycnal Mixing Driven by Mesoscale Eddies: Sensitivity to Wind Energy Supply
Bibliographic record
Abstract
Abstract It has been estimated that much of the wind energy input to the ocean general circulation is removed by mesoscale eddies via baroclinic instability. While the fate of this energy remains a subject of research, arguments have been presented suggesting that a fraction of it may get transferred to lee waves that, upon breaking, result in bottom-enhanced diapycnal mixing. Here the authors propose several parameterizations of this process and explore their impact in a low-resolution ocean–climate model, focusing on their impact on the abyssal meridional overturning circulation (MOC) of Antarctic Bottom Water. This study shows that, when the eddy energy is allowed to maintain diapycnal mixing, the abyssal MOC generally intensifies with increasing wind energy input to the ocean. In such a case, the whole system is driven by the wind: wind steepens isopycnals and generates eddies, and the (parameterized) eddies generate small-scale mixing, driving the MOC. It is also demonstrated that if the model diapycnal diffusivity, eddy transfer coefficient, and surface climate are decoupled from the winds, then stronger wind stress in the Southern Ocean may lead to a weaker MOC in the abyss—in line with previous results. A simple scaling theory, describing the response of the abyssal MOC strength to wind energy input, is developed, providing a better insight on the numerical results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".