The Variability of Winds and Fluxes Observed Near Submesoscale Fronts
Bibliographic record
Abstract
Abstract Submesoscale oceanic fronts (SFs), which typically occur on a spatial scale of 0.1–10 km, may have a large influence on the atmospheric surface layer (ASL). However, due to their short temporal‐spatial scales, evaluating their direct impact on this layer remains challenging and characterizing the nature of SF‐ASL interaction has not been done in the field. To address this, a study of the air‐sea response to SFs was conducted using observations collected during the Lagrangian Submesoscale Experiment, which took place in the northern Gulf of Mexico. This manuscript focuses on the meteorological measurements made from a pair of masts installed on the bow of the R/V Walton Smith. This work represents one of the first observation‐based investigations into the potential influence that SFs have on the ASL. Contemporaneous measurements from an X‐band marine radar, moving vessel profiler, and Lagrangian drifters were also used to analyze the SF dynamics. Systematic surface wind velocity changes over several cross‐frontal transects were observed, a process previously associated with mesoscale fronts. A comparison between the eddy covariance and parameterized (COARE 3.5) air‐sea fluxes revealed that the directly observed heat flux was 1.5 times larger than the bulk value in the vicinity of the SFs. This suggests that the hydrodynamic processes near the front enhance the local exchange of sensible and latent heat. Given the prevalence of SF over the global upper ocean, these findings suggest that these features may have a widely distributed and cumulative impact on air‐sea interactions.
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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 | 0.001 | 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) | 0.001 | 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".