Evaluating Model-Simulated Monthly Sea Levels During 1993–2023 in the Northwest Atlantic: Influence of Model Resolution and Data Assimilation
Bibliographic record
Abstract
This study evaluates monthly sea levels during 1993–2023 from four ocean models using tide gauge and altimeter data in the Northwest Atlantic with its shelf seas, including the Gulf of Maine, Scotian Shelf, Gulf of St. Lawrence, and the Newfoundland and Labrador Shelf. The evaluation is carried out for four different aspects: the multi-decadal mean and linear trend, seasonal cycle, and the de-trended and de-seasonalized anomalies. Overall, the high-resolution model with advanced data assimilation (GLORYS12v1) possesses skills in all four aspects. The other three models show different discrepancies in reproducing the observed sea level variations relative to GLORYS12v1. They possess low or no skills for the timing (despite reasonable standard deviations) of sea level anomalies at time scales longer than 20 months along the coast, and at all time scales on the shelf, over the shelf break, and in the deep ocean. Without data assimilation, the models with high and medium resolutions show biases in the time-mean sea levels in the Labrador Sea that can be attributed to the simulated stronger and weaker deep convection (deeper and shallower mixed layer depth), respectively. The medium-resolution model, using a different data assimilation approach than GLORYS12v1, shows biases in the seasonal amplitude and multi-decadal trends.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".