Role of Mixed‐Layer Instabilities in the Seasonal Evolution of Eddy Kinetic Energy Spectra in a Global Submesoscale Permitting Simulation
Bibliographic record
Abstract
Abstract A submesoscale‐permitting global ocean simulation is used to study the upper ocean turbulence in high kinetic energy (KE) regions. Submesoscale processes peak in winter so that the geostrophic KE spectra tend to be relatively shallow in winter ( ) with steeper spectra in summer ( ). This transition in KE spectral scaling has two phases. In the first phase (late autumn), KE spectra show the presence of two spectral regimes: power‐law in mesoscales and power‐law in submesoscales. The first phase appears with the onset of mixed‐layer instabilities, which convert available potential energy into KE, and this process results in a flattening of KE spectra at submesoscales. However, KE spectra at longer wavelengths follow scaling associated with a forward enstrophy transfer. In the second phase (late winter), KE produced through mixed‐layer instabilities is transferred to larger scales, and power‐law also develops in mesoscales.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".