Prediction of M<sub>2</sub> tidal surface currents by a global baroclinic ocean model and evaluation using observed drifter trajectories
Bibliographic record
Abstract
Abstract Global M2 tidal surface currents are predicted using a global baroclinic ocean model with horizontal grid spacing of 1/12° and 19 z‐levels in the vertical. After first showing the predicted tidal elevations are in reasonable agreement with observations made by bottom pressure recorders and altimeters, the predicted tidal surface currents are evaluated by comparing them with independent estimates based on observed drifter trajectories. Both predicted and observed tidal surface currents can exceed 0.1 m s−1 in the deep ocean. Internal tides are shown to make a significant contribution to the predicted tidal surface currents. Phase locking of the surface and internal tides causes spatial changes in the predicted tidal surface currents that vary with approximately the same wavenumber as that of the lowest mode internal tide. Qualitatively similar, small‐scale variations are also detected in the observed estimates but the variations do not line up exactly with the predictions. Possible explanations for the mismatch are given. The seasonal variation of M2 tidal surface currents, and the energy conversion rate from surface to internal tides, is also predicted by initializing, and restoring, the model to an observed seasonal climatology of temperature and salinity. Compared to tidal elevation, the seasonal change of tidal surface current can be large (order 10% for each hemisphere). It is caused by seasonal variations in the vertical structure of the baroclinic modes and the energy conversion rate. In the vicinity of major bathymetric features, the seasonal variation of second and higher order modes can be much larger (up to 50%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".