What happens when an inertially unstable jet approaches a lateral boundary?
Bibliographic record
Abstract
Much of our understanding of inertial instability in geophysical flows comes from atmospheric physics, and these studies have neglected the impact of lateral boundaries. To address this shortcoming, we performed a series of high-resolution 3D numerical simulations in Oceananigans in the context of the nonhydrostatic Boussinesq equations assuming a rigid-lid approximation. An inertially unstable baroclinic jet was investigated both far away and adjacent to a vertical boundary. The jet was chosen to be in thermal-wind balance and the buoyancy field was perturbed to instigate the instability.We found that when the unstable jet is sufficiently close to the vertical boundary, the wavenumber of the fastest-growing unstable mode nearly doubled when compared to the jet far away from the boundary. We have not observed this shift to smaller scales in the context of a barotropic jet. The growth rates of the instability, measured by taking the l2 norm of the velocity components, showed an initial linear growth phase in the first few days with no significant differences regarding the positioning of the jet. After this period, non-linear saturation stabilized the jet to inertial instability, and a secondary baroclinic instability developed. These findings suggest a previously unaccounted factor that can influence the bio-physicochemical properties of the ocean in proximity to coastal boundaries, contributing to the current understanding of the importance of submesoscale phenomena.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".