Surface Wave Effects on the Wind-Power Input to Mixed Layer Near-Inertial Motions
Bibliographic record
Abstract
Abstract Ocean surface waves play an essential role in a number of processes that modulate the momentum fluxes through the air–sea interface. In this study, the effects of evolving surface waves on the wind-power input (WPI) to near-inertial motions (NIMs) are examined by using momentum fluxes from a spectral wave model and a simple slab ocean mixed layer model. Single-point numerical experiments show that, without waves, the WPI and the near-inertial kinetic energy (NI-KE) are overestimated by about 20% and 40%, respectively. Globally, the overestimate in WPI is about 10% during 2005–08. The largest surface wave effects occur in the winter storm-track regions in the midlatitude northwestern Atlantic, Pacific, and in the Southern Ocean, corresponding to large inverse wave age and rapidly varying strong winds. A relatively low frequency of occurrence of wind sea is found in the midlatitudes, which implies that the influence of evolving surface waves on WPI is intermittent, occurring less than 10% of the total time but making up the dominant contributions to reductions in WPI. Given the vital role of NIMs in diapycnal mixing at the base of the mixed layer and the deep ocean, the present study suggests that it is necessary to include the effects of surface waves on the momentum flux, for example, in studies of coupled ocean–atmosphere dynamics or climate models.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".