Energy Cascades in Surface Semigeostrophic Turbulence: Implications for the Oceanic Submesoscale Flows
Bibliographic record
Abstract
Abstract Surface semigeostrophic (SSG) turbulence is examined in this study with emphasis on the effect of ageostrophy on energy cascades across the scales below the deformation radius. In our simulations, the strength of the ageostrophic component is controlled by the Rossby number , varying from 0.01 to 0.2. The flows are asymmetric with preference for cold cyclonic vortices and warm anticyclonic filaments. Strong vertical motions concentrate in small‐scale filaments and at the periphery of vortices where the lateral divergence becomes significant. A negative correlation between the divergence and the relative vorticity is identified using joint probability density functions. Slopes of the kinetic and potential energy spectra vary between −2.2 and −1.7. The features of the simulated flows including the asymmetry, strong vertical motion, and −2 spectral slope agree with the observations of the oceanic submesoscale flows. Analyses of spectral fluxes demonstrate an inverse kinetic energy cascade and a forward cascade of potential energy. As increases, the filaments become more numerous in the flows. They wrap around cyclones, weakening their interactions and subsequent mergers, thus suppressing the inverse cascade of kinetic energy. Ageostrophy promoting the forward potential energy cascade is important for the frontogenesis in the ocean. We characterize lateral dispersion in the SSG flows using the finite‐scale Lyapunov exponents (FSLEs). They are used to identify the Lagrangian coherent structures as well as to investigate the regimes of dispersion at different scales. The results show a smooth transition from hyper‐ballistic diffusion at small scales to normal diffusion at large scales.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".