Covariability of Near-Surface Wind Speed Statistics and Mesoscale Sea Surface Temperature Fluctuations
Bibliographic record
Abstract
Abstract The atmospheric (ABL) and ocean (OBL) boundary layers are intimately linked via mechanical and thermal coupling processes. In many regions over the world’s oceans, this results in a strong covariability between anomalies in wind speed and SST. At oceanic mesoscale, this coupling can be driven either from the atmosphere or the ocean. Gridded SST and wind speed data at 0.25° resolution show that over the western North Atlantic, the ABL mainly responds to the OBL, whereas in the eastern North Pacific and in the Southern Ocean, the OBL largely responds to wind speed anomalies. This general behavior is also verified by in situ buoy observations in the Atlantic and Pacific. A stochastic, nondimensional, 1D coupled air–sea boundary layer model is utilized to assess the relative importance of the coupling processes. For regions of little intrinsic SST fluctuations (i.e., most regions of the world’s oceans away from strong temperature fronts), the inclusion of cold water entrainment at the thermocline is crucial. In regions with strong frontal activities (e.g., the western boundary regions), the coupling is dominated by the SST fluctuations, and the frontal variability needs to be included in models. Generally, atmospheric and ocean-driven coupling lead to an opposite relationship between SST and wind speed fluctuations. This effect can be especially important for higher wind speed quantiles.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".