Baroclinic Coupling Improves Depth‐Integrated Modeling of Coastal Sea Level Variations Around Puerto Rico and the U.S. Virgin Islands
Bibliographic record
Abstract
Abstract This study applies a baroclinic‐coupled depth‐integrated modeling system to the North Atlantic Ocean, where an unstructured mesh is used to focus resolution down to ∼30 m along the coasts of Puerto Rico and the U.S. Virgin Islands. Ocean baroclinicity is incorporated through one‐way coupling from operational data‐assimilated Global Ocean Forecasting System 3.1 temperature and salinity fields at just 12% additional computational time. The main objectives are to provide a comprehensive analysis of observed and modeled coastal sea levels (spanning from seasonal to supertidal variations) in Puerto Rico and the U.S. Virgin Islands during 2017 and to evaluate the associated model performance with and without baroclinic coupling at 14 National Oceanic and Atmospheric Administration/National Ocean Service tide gauges deployed in the region. It is found that baroclinic coupling increases modeled energy across the entire frequency spectrum, which is more commensurate with observations. In particular, density‐driven effects such as the seasonal cycle and sea level setdown due to trailing cold wakes from passing hurricanes are largely reproduced. Supertidal shelf‐resonant seiching at one to two cycles per hour is observed and modeled at a number of locations, where excitation of these modes is often promoted by the baroclinic coupling. Baroclinicity improves the yearlong model skill at every tide gauge, where the mean total skill is increased from 87% to 93% accuracy (54% to 85% for the nontidal residual). In September 2017 during Hurricanes Irma and Maria, baroclinicity increases model skill at 10 out of 14 tide gauges even when the barotropic mode is adjusted to have no mean offset from the observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".