Breaking Internal Waves and Ocean Diapycnal Diffusivity in a High-Resolution Regional Ocean Model: Evidence of a Wave-turbulence Cascade
Bibliographic record
Abstract
While the primary origin of ocean diapycnal diffusivity is commonly attributed to stratified turbulence induced by breaking internal waves (IWs), verifying diffusivity values in ocean circulation models within specific geographical regions remains challenging due to limited microstructure measurements. Recent analyses of a downscaled global ocean simulation into higher-resolution regional setups northeast of Hawaii, reveal a notably enhanced fit between simulated IW spectra and in-situ profiler measurements like the Garrett-Munk spectrum [Nelson et al. (2020), Pan et al. (2020), Thakur et al. (2022)]. In this study, we utilize this dynamically downscaled ocean simulation to scrutinize the dynamics of IW-breaking and the wave-turbulence cascade in this region explicitly. Employing a modified version of the Kappa Profile Parameterization (KPP), we infer the horizontally-averaged vertical profile of diapycnal diffusivity. Comparing this inferred profile to the background profile used in low-resolution coupled climate models—such as the Community Earth System Model (CESM) by the US National Center for Atmospheric Research (NCAR)—is a central aspect of our investigation. Our exploration reveals that the wavefield in the high-resolution regional domain is dominated by a well-resolved spectrum of low-mode IWs, predictable through appropriate eigenvalue computations for stratified flow. Finally, we propose a novel tentative approach to enhance the KPP parameterization. This approach holds promise for refining our understanding of diapycnal diffusivity, offering valuable insights for improving ocean circulation models. References: AD Nelson, BK Arbic, D Menemenlis, WR Peltier, MH Alford, N Grisouard, and JM Klymak. Improved internal wave spectral continuum in a regional ocean model. Journal of Geophysical Research: Oceans, 125(5):e2019JC015974, 2020. Yulin Pan, Brian K Arbic, Arin D Nelson, Dimitris Menemenlis, WR Peltier, Wentao Xu, and Ye Li. Numerical investigation of mechanisms underlying oceanic internal gravity wave power-law spectra. Journal of Physical Oceanography, 50(9):2713–2733, 2020. Ritabrata Thakur, Brian K Arbic, Dimitris Menemenlis, Kayhan Momeni, Yulin Pan, W Richard Peltier, Joseph Skitka, Matthew H Alford, and Yuchen Ma. Impact of vertical mixing parameterizations on internal gravity wave spectra in regional ocean models. Geophysical Research Letters, 49(16): e2022GL099614, 2022.
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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 | 0.000 | 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".