Validation of Ocean Model Predictions of Mean Dynamic Topography in Shallow, Tidally Dominated Regions Using Observations of Overtides
Bibliographic record
Abstract
Abstract In shallow, tidally dominated regions, overtides and the mean state of the ocean are coupled through their simultaneous generation by nonlinear processes. We present a new method that uses observed overtides (e.g., M 4 ) and mean currents to independently assess the accuracy of mean dynamic topography (MDT) predicted by ocean models. This is useful in regions where no sufficiently long, geodetically referenced sea level records are available for validation of the predicted MDT. We apply the new method to a regional model of the Gulf of Maine/Scotian Shelf region (GoMSS) and a barotropic, higher resolution model focused on the upper Bay of Fundy (UBoF). We first show that the tides and mean circulation predicted by UBoF are in good agreement with observations and a significant improvement over GoMSS. Next, we use UBoF to demonstrate that observed overtides are useful in selecting the optimal bathymetry and constraining parameters of an ocean model. An accurate bathymetry is critical for capturing the dominant nonlinear processes that generate overtides and control the form of MDT in shallow, tidally dominated regions. Finally, we use the observed overtides to argue that the MDT predicted by UBoF is more realistic than the prediction by GoMSS. In the vicinity of headlands, both horizontal advection and bottom friction in UBoF generate harmonics of the tidal flow and local setdowns of coastal MDT of (10 cm). The prediction of such features, validated by observed overtides, can provide guidance in future deployments of tide gauges in support of geoid and ocean model validation.
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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 | 0.002 |
| 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 | 0.001 | 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".