The impact of Labrador Sea temperature and salinity variability on density and the subpolar AMOC in a decadal prediction system
Bibliographic record
Abstract
Abstract Labrador Sea density variability is important for Atlantic Meridional Overturning Circulation (AMOC) dynamics and hence decadal variability in the Atlantic. We investigate whether temperature or salinity dominate top 500 m interannual Labrador Sea density variability in gridded observations, an assimilation of the observations, and a set of multiannual hindcasts. We find that salinity dominates in the observations and assimilation. In the hindcasts salinity remains dominant for the first year but from year three these revert to the same temperature dominance seen in the underlying climate model. This is due to damping of the interannual salinity variability, possibly caused by unrealistically large convection that develops. Crucially, the hindcasts have high correlation skill in temperature/salinity throughout, but no skill in density, dynamic sea level, or the subpolar AMOC due to the incorrect drivers. This highlights the importance of correctly simulating both the sign and magnitude of temperature/salinity variability in a prediction system.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".