Recurrent replenishment of Labrador Sea Water and associated decadal-scale variability
Bibliographic record
Abstract
Winter convective overturning in the Labrador Sea reached an “aggregate” maximum depth of 1700 m in 2015—the deepest since 1994—with the resulting Labrador Sea Water (LSW) “year class” being one of the deepest and thickest observed outside of the early 1990s. Argo float, annual survey, and moored measurements in recent decades provide an unprecedented view of important seasonal, interannual, and longer-term LSW variability in the Labrador Sea region. During the 2002–2015 “Argo” era, the average winter LSW pycnostad volume was about 70% larger in relatively strong convection years than in relatively weak ones. However, the winter-to-fall LSW disappearance volume was 180% larger, pointing to a factor of 2.8 difference in the potential LSW export rates from the region between relatively strong and weak convection years. Intermittently recurrent deep convection is contributing to predominant decadal-scale variations in intermediate-depth temperature, salinity, and density in the LS, with implications for decadal-scale variability across the subpolar North Atlantic and potentially in the Atlantic Meridional Overturning Circulation. Comparison of the LS ocean heat content changes and cumulative surface heat losses during the fall-winter cooling seasons indicates that anomalously strong winter atmospheric cooling, associated at least in part with the North Atlantic Oscillation, is continuing to be a major forcing of the recurrent convection.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".