Impact of Synoptic-Scale Atmospheric Forcing Conditions on Deep Convection in the Labrador Sea
Bibliographic record
Abstract
During the winter season, stratification in the central Labrador Sea is eroded by surface heat fluxes causing convective overturning exceeding depths of 2km. This is one of the few locations globally in which deep convection occurs, making it an important feature of the climate system and ocean ventilation. Large-scale atmospheric circulation patterns modulate the air-sea interaction that drives the loss of ocean buoyancy. Here we investigate the process by which weather patterns driven by the North Atlantic Oscillation (NAO), and its northern centre of action, the Icelandic Low, modulate convective depths. A one-dimensional ocean model is used to quantify the mixed layer depth’s response to various atmospheric forcing conditions. We find that while net heat flux is the strongest modulating factor of mixed layer depth’s seasonal maximum, it is also strongly affected by the NAO. The Icelandic Low, despite its proximity to the Labrador Sea, does not affect mixed layer deepening as strongly. From geospatial correlation fields with heat flux, NAO, and Icelandic Low time series, it is evident that the NAO more efficiently regulates strong, cold, westerly winds from over the North American continent, which are more effective at cooling the ocean surface boundary layer. These correlations are supported by a compositing approach with a peak-over-threshold technique.
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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 | 0.001 |
| Research integrity | 0.001 | 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".