The Seasonality of Convective Events in the Labrador Sea
Bibliographic record
Abstract
Abstract Modeling deep convection is a key challenge for climate science. Here two simulations of the Labrador Sea circulation obtained with the Regional Ocean Modeling System (ROMS) run at a horizontal resolution of 7.5 km are used to characterize the response of convection to atmospheric forcing and its seasonal variability over the period 1980–2009. The integrations compare well with the sparse observations available. The modeled convection varies in three key aspects over the 30 years considered. First, its magnitude changes greatly at decadal scales. This aspect is supported by the in situ observations. Second, the initiation and peak of convection (i.e., initiation and maximum) shift by 2–3 weeks between strong and weak convective years. Third, the duration of convection varies by approximately one month between strong and weak years. The last two changes are associated with the variability of the time-integrated surface heat fluxes over the Labrador Sea during winter and spring, while the first results from changes in both atmospheric heat fluxes and oceanic conditions through the lateral inflow of warm Irminger Water from the boundary current system to the basin interior. Changes in surface heat fluxes over the convective region are linked to large-scale modes of variability, the North Atlantic Oscillation and Arctic Oscillation. Implications for modeling the climate variability of the Labrador basin are discussed.
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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".