Forcing Space: An Alternative to Regime Diagrams for Predicting Characteristics of Turbulence in the Ocean Surface Mixing Layer
Bibliographic record
Abstract
Abstract Various forms of regime diagrams have become an accepted means of identifying the dominant type of forcing of turbulence in the ocean surface layer. However, all of the proposed forms share a number of issues, demonstrated here, that make them an imperfect tool for this purpose. Instead, I suggest a forcing space consisting of surface buoyancy flux (usually dominated by surface heat flux) and a growth rate defined as the inverse of a theoretical time scale for growth of Langmuir circulations in an unstratified water column. Using coastal data, it is demonstrated that, provided forcing conditions are roughly constant for several hours, location in the upper half-plane of this forcing space predicts organizational characteristics of observed turbulence that range in a systematic way between those of “pure” convection and those of full depth Langmuir circulations. In this upper half-plane, where a convective scale velocity exists and the surface Stokes drift velocity can be computed, allowing calculation of a Stokes scale velocity, a linear combination of the two scale velocities provides a consistent estimate of observed rms turbulent vertical velocity. Time dependence is nevertheless a frequent characteristic of ocean surface layer forcing, if only because of the (usually large) diurnal variation in surface heat flux. It is shown that the time scale of response of surface layer turbulence to time variable forcing depends on whether the major change is due to wind/wave or buoyancy forcing. Relevant modeling studies are suggested.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".