Observation and modeling of surface currents on the Grand Banks: A study of the wave effects on surface currents
Bibliographic record
Abstract
We investigate the effects of surface waves on surface currents using surface drifter data from the Grand Banks and a coupled current‐wave‐drifter model. The theoretical basis of the study is Jenkins' theory of wave‐current interaction in which wind‐generated surface currents are modified by wind‐wave and wave‐current momentum transfers. The total surface current is the sum of the wave modified current, the Stokes drift and the tidal current. Jenkins' formulation was incorporated into the Princeton Ocean Model and applied to the Labrador Sea and the adjacent shelves. The wave energy spectrum from Wavewatch III was used to calculate the momentum transfer and the Stokes drift. A series of model experiments were conducted to simulate the drifter trajectories and examine the sensitivity of the simulations to model parameters. The results show that the Stokes drift is the dominant wave effect, which increases the surface drift speeds by 35% and veers the currents toward the wind directions. The net effect of wind‐to‐wave and wave‐to‐current momentum transfers reduces the surface speeds by a few percent. A statistical analysis of the model currents and drifter data shows that the inclusion of the wave effects improves the model simulations significantly. Model errors due to uncertainties in the model parameters including the eddy viscosity, wave spectrum, air drag of the drifters, and bottom friction are investigated. The model surface currents are shown to be most sensitive to the surface eddy viscosity and the wave energy spectra.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".