High-Latitude Eddy Statistics from SWOT assessed by in situ observations
Bibliographic record
Abstract
Abstract. Mesoscale eddies play a key role in the transport of heat, salt, and momentum, yet their statistical characterization at high latitudes has remained elusive due to the coarse resolution of conventional satellite altimetry. Here we present the first statistical description of mesoscale eddies in the Labrador Sea using observations from the Surface Water and Ocean Topography (SWOT) mission. We apply an eddy-detection algorithm directly to the native 2-km SWOT swaths, without gridding or assimilation, and validate the detections against in situ measurements from shipboard current profiler data from one cruise in 2024, as well as against a statistically derived shipboard current-profiler–based eddy census. The comparison demonstrates excellent agreement in eddy size and intensity, confirming SWOT’s ability to resolve high-latitude mesoscale structures previously undetectable in gridded altimetry. The SWOT-derived eddy census based on a full-calendar year reveals a predominance of energetic anticyclones (Irminger Rings) in the basin interior and smaller cyclones along the continental slopes, with clear seasonal variability linked to boundary current instability. These findings provide the first observational benchmark for mesoscale activity in the Labrador Sea and illustrate SWOT’s potential to extend eddy statistics to high-latitude and ice-influenced regions, opening the way for a global assessment of mesoscale variability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".