Relative Contribution of Atmospheric Forcing, Oceanic Preconditioning and Sea Ice to Deep Convection in the Labrador Sea
Bibliographic record
Abstract
The relative contribution of atmospheric forcing, oceanic preconditioning, and sea ice to Labrador Sea Deep Convection (LSDC) is investigated by conducting three ensemble experiments using a global coupled sea ice–ocean model for the first time. Simulated results show that the atmospheric activities dominate the interannual and decadal variability, accounting for 70% of LSDC. Oceanic preconditioning is more significant in the shallow LSDC years that the water column is stable, accounting for 21%, especially in the central Labrador Sea and Irminger Sea. Moreover, the sea ice contribution is negligible over the whole Labrador Sea, while its contribution is significant in the sea ice-covered slope regions, accounting for 20%. The increasingly importance of sea ice on LSDC and the water mass transformation will be found in the North Atlantic Ocean, if the Arctic sea ice declines continuously. Additionally, there is a 10 Sv increase (85%) in atmospheric forcing to the subpolar gyre in the North Atlantic Ocean, while oceanic preconditioning contributes a 7 Sv decrease (12%). These findings highlight the importance of summer oceanic preconditioning to LSDC and the subpolar gyre, and therefore it should be appropriately accounted for in future studies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".