Effects of Wind on Convection in Strongly and Weakly Baroclinic Flows with Application to the Labrador Sea*
Bibliographic record
Abstract
Abstract Large buoyancy loss driving deep convection is often associated with a large wind stress that is typically omitted in simulations of convection. Here it is shown that this omission is not justified when overturning occurs in a horizontally inhomogeneous ocean. In strongly baroclinic flows, convective mixing is influenced both by the background horizontal density gradient and by the across-front advection of buoyancy due to wind. The former process—known as slantwise convection—results in deeper convection, while the effect of wind depends on the relative orientation of wind with respect to the baroclinic front. For the case of the Labrador Sea, wintertime winds act to destabilize the baroclinic Labrador Current causing a buoyancy removal roughly one-third as large as the air–sea buoyancy loss. Simulations using a nonhydrostatic numerical model, initialized and forced with observed fields from the Labrador Sea, show how the combination of wind and lateral gradients can result in significant convection within the current, in contrast with previous ideas. Though the advection of buoyancy due to wind in weakly baroclinic flows is negligible compared to the surface buoyancy removal typical of convective conditions, convective plumes are substantially deformed by wind. This deformation, and the associated across-front secondary circulation, are explained in terms of the vertical advection of wind-generated vorticity from the surface boundary layer to deeper depths. This mechanism generates vertical structure within the convective layer, contradicting the historical notion that properties become vertically homogenized during convection. For the interior Labrador Sea, this mechanism may be partly responsible for the vertical variability observed during convection, which modeling studies have until now failed to reproduce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".