Wave-Turbulence Cascades and Deep Ocean Mixing: Inferring Diapycnal Diffusivity in High-Resolution Ocean Models
Bibliographic record
Abstract
Internal wave dynamics play a critical role in understanding ocean diapycnal diffusivity and associated mixing processes, particularly in the deep ocean context. Building upon prior analyses of internal wave breaking and its influence on diapycnal diffusivity, in this study we employ a high-resolution regional ocean model to infer ocean diapycnal diffusivity due to internal wave (IW) breaking [Momeni et al., 2025]. Our work leverages the Bouffard-Boegman parameterization, which distinguishes between reversible and irreversible mixing components. This framework provides a robust methodology to infer diapycnal diffusivity profiles from turbulent dissipation rates, improving upon earlier KPP-based approaches that lacked this critical distinction. This inference is made possible through the work of Skitka et al. [2024], which directly measured dissipation rates from numerical simulations.The findings reinforce and expand on earlier results from dynamically downscaled simulations in the northeast Pacific, which revealed a pronounced wave-turbulence cascade and highlighted the suppression of higher-order IW modes due to the background component of KPP. By deactivating this component, higher-order modes engage in triad resonance interactions with lower-order modes and are effectively energized; they subsequently undergo shear instability, enhancing mixing rates and aligning diffusivity profiles with empirical observations. This mechanism is discussed in detail in Momeni et al. [2024].Our results underscore KPP’s limitations in distinguishing mixing processes and its tendency to overestimate shear contributions to diffusivity. These insights pave the way for improving diapycnal diffusivity parameterizations in low-resolution climate models by emphasizing mechanisms rooted in internal wave breaking rather than simplified parameterizations. Future work will focus on higher-resolution simulations to refine these findings and address basin- and latitude-dependent variations. ReferencesKayhan Momeni, Yuchen Ma, William R Peltier, Dimitris Menemenlis, Ritabrata Thakur, Yulin Pan, Brian K Arbic, Joseph Skitka, and Matthew H Alford. Breaking internal waves and ocean diapycnal diffusivity in a high-resolution regional ocean model: Evidence of a wave-turbulence cascade. Journal of Geophysical Research: Oceans, 129(6):e2023JC020509, 2024.Kayhan Momeni, W Richard Peltier, Joseph Skitka, Yuchen Ma, Brian K Arbic, Dimitris Menemenlis, and Yulin Pan. An alternative buoyancy reynolds number-based inference of ocean diapycnal diffusivity due to internal wave breaking: results from a high-resolution regional ocean model. Geophysical Research Letters, 2025. Submitted for publication.Joseph Skitka, Brian K Arbic, Yuchen Ma, Kayhan Momeni, Yulin Pan, William R Peltier, Dimitris Menemenlis, and Ritabrata Thakur. Internal-wave dissipation mechanisms and vertical structure in a high-resolution regional ocean model. Geophysical Research Letters, 51(17):e2023GL108039, 2024.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.000 | 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".