The role of the meridional sea surface temperature gradient in controlling the Caribbean low‐level jet
Bibliographic record
Abstract
Abstract The Caribbean low‐level jet (CLLJ) is an important modulator of regional climate, especially precipitation, in the Caribbean and Central America. Previous work has inferred, due to their semiannual cycle, an association between CLLJ strength and meridional sea surface temperature (SST) gradients in the Caribbean Sea, suggesting that the SST gradients may control the intensity and vertical shear of the CLLJ. In addition, both the horizontal and vertical structure of the jet have been related to topographic effects via interaction with the mountains in Northern South America (NSA), including funneling effects and changes in the meridional geopotential gradient. Here we test these hypotheses, using an atmospheric general circulation model to perform a set of sensitivity experiments to examine the impact of both SST gradients and topography on the CLLJ. In one sensitivity experiment, we remove the meridional SST gradient over the Caribbean Sea and in the other, we flatten the mountains over NSA. Our results show that the SST gradient and topography have little or no impact on the jet intensity, vertical, and horizontal wind shears, contrary to previous works. However, our findings do not discount a possible one‐way coupling between the SST and the wind over the Caribbean Sea through friction force. We also examined an alternative approach based on barotropic instability to understand the CLLJ intensity, vertical, and horizontal wind shears. Our results show that the current hypothesis about the CLLJ must be reviewed in order to fully understand the atmospheric dynamics governing the Caribbean region.
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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 | 0.000 | 0.000 |
| 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".