Turbulence Generation via Nonlinear Lee Wave Trailing Edge Instabilities in Kuroshio‐Seamount Interactions
Bibliographic record
Abstract
Abstract Physical processes behind flow‐topography interactions and turbulent transitions are essential for parameterization in numerical models. We examine how the Kuroshio cascades energy into turbulence upon passing over a seamount, employing a combination of shipboard measurements, tow‐yo microstructure profiling, and high‐resolution mooring. The seamount, spanning 5 km horizontally with two summits, interacts with the Kuroshio, whose flow speed ranges from 1 to 2 m s−1, modulated by tides. The forward energy cascade process is commenced by forming a train of 2–3 nonlinear lee waves behind the summit with a wavelength of 0.5–1 km and an amplitude of 50–100 m. A train of Kelvin‐Helmholtz (KH) billows develops immediately below the lee waves and extends downstream, leading to enhanced turbulence. The turbulent kinetic energy dissipation rate is O (10−7–10−4) W kg−1, varying in phase with the upstream flow speed modulated by tides. KH billows occur primarily at the lee wave's trailing edge, where the combined strong downstream shear and low‐stratification recirculation trigger the shear instability, Ri < 1/4. The recirculation also creates an overturn susceptible to gravitational instability. This scenario resembles the rotor, commonly found in atmospheric mountain waves but rarely observed in the ocean. A linear stability analysis further suggests that critical levels, where the KH instability extracts energy from the mean flow, are located predominantly at the strong shear layer of the lee wave's upwelling portion, coinciding with the upper boundary of the rotor. These novel observations may provide insights into flow‐topography interactions and improve physics‐based turbulence parameterization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".