Breaking Internal Waves and Ocean Diapycnal Diffusivity in a High‐Resolution Regional Ocean Model: Evidence of a Wave‐Turbulence Cascade
Bibliographic record
Abstract
Abstract It is generally understood that the origin of ocean diapycnal diffusivity is primarily associated with the stratified turbulence produced by breaking internal (gravity) waves (IW). However, it requires significant effort to verify diffusivity values in ocean general circulation models in any particular geographical region of the ocean due to the scarcity of microstructure measurements. Recent analyses of downscaled IW fields from an internal‐wave‐admitting global ocean simulation into higher‐resolution regional configurations northwest of Hawaii have demonstrated a much‐improved fit of the simulated IW spectra to the in‐situ profiler measurements such as the Garrett‐Munk (GM) spectrum. Here, we employ this dynamically downscaled ocean simulation to directly analyze the nature of the IW‐breaking and the wave‐turbulence cascade in this region. We employ a modified version of the Kappa Profile Parameterization (KPP) to infer what the horizontally averaged vertical profile of diapycnal diffusivity should be, and compare this to the background profile that would be employed in the ocean component of a low‐resolution coupled climate model such as the Community Earth System Model (CESM) of the US National Center for Atmospheric Research (NCAR). In pursuing this goal, we also demonstrate that the wavefield in the high‐resolution regional domain is dominated by a well‐resolved spectrum of low‐mode IWs that are predictable by solving an appropriate eigenvalue problem for stratified flow. We finally suggest a new tentative approach to improve the KPP 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".