Capturing the evolution of baroclinic tides into internal solitary waves in the St. Lawrence Estuary with SWOT altimetry
Bibliographic record
Abstract
The Surface Water and Ocean Topography (SWOT) mission will significantly improve and expand near-global mapping of tidal cycles that contribute significantly to sea-level variability by collecting, at an unprecedented resolution, sea-level elevation measurements in nearshore areas that were poorly mapped by previous altimetric missions. Despite SWOT’s limitations in capturing tides due to undersampling, mapping tides is a prerequisite for distinguishing them from other physical processes that occur on comparable scales, such as mesoscale and submesoscale eddies and fronts, which are key targets for SWOT. The mechanisms by which energy moves from barotropic and baroclinic tides to smaller scales have been extensively researched in the global ocean. However, tracking the tidal energy budget in coastal shallower areas remains challenging due to the complex nonlinear deformation of sinusoidal tidal waves caused by bathymetric gradients, water discharge, and ambient stratification. Using numerical modeling and SWOT observations, we seek to decipher the tidal energetics of the St. Lawrence Lower Estuary, a major stratified North American waterway strongly driven by tides, and that also received additional attention during SWOT calibration-validation stage and AirSWOT campaign. Our primary finding is that SWOT can observe the prominent surface elevation signal of internal solitary waves generated by the nonlinear steepening of the low-mode baroclinic tide propagating in the St. Lawrence Estuary, which periodically redistributes the tidal energy toward scales of one or a few kilometers. This reveals the potential of SWOT to capture tidal variability at much finer scales previously missed by its predecessors. Future work will focus on understanding the key mechanisms and physical parameters that control the decay of baroclinic tides into internal solitary waves, which could lead to improved detection of baroclinic tides using altimetry.
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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 | 0.000 |
| 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".