Two-dimensional Ekman-Inertial Instability: A comparison with Inertial Instability
Bibliographic record
Abstract
In the ocean, submesoscale flows tend to undergo several hydrodynamic instabilities. In particular, Inertial Instability (InI) and Ekman-Inertial Instability (EII) are known to develop in geostrophically balanced barotropic flows whose lateral shear is larger in magnitude and opposite in sign to the Coriolis parameter. Although these instabilities share some elements, their dynamical nature can lead to fundamental differences. However, the current analytical description of EII is one-dimensional, which makes it difficult to compare against InI in a more realistic scenario. To overcome this limitation, we conduct two-dimensional numerical simulations of both InI and EII in a submesoscale jet and explore the induced vertical flow, the growth rate, and the energetics of each instability. Furthermore, we investigate the sensitivity of our results to variations in the minimum Rossby number of the jet. We find that EII grows faster than InI and induces stronger vertical flow, especially near the surface. Both instabilities radiate inertial waves away from the current, and these waves predominantly propagate across the anticyclonic side of the jet. Finally, when the instabilities weaken, the fluid reaches a stable state that is remarkably similar in both cases. This study highlights the similarities and differences between InI and EII and provides further insight into the mechanism behind EII that makes it capable of outcompeting other submesoscale instabilities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".