Local and Remote Influences on the Heat Content of the Labrador Sea: An Adjoint Sensitivity Study
Bibliographic record
Abstract
Abstract The Labrador Sea is one of the few regions on the planet where the interior ocean can exchange heat directly with the atmosphere via strong, localized, wintertime convection, with possible implications for the state of North Atlantic climate and global surface warming. Using an observationally constrained ocean adjoint model, we find that annual‐mean Labrador Sea heat content is sensitive to temperature/salinity changes (1) along potential source water pathways (e.g., the subpolar gyre, the North Atlantic Current, the Gulf Stream) and (2) along the West African and European shelves, which are not significant source water regions for the Labrador Sea. The West African coastal/shelf adjustment mechanism, which may be excited by changes in along‐shelf wind stress, involves pressure anomalies that propagate along a coastal waveguide toward Greenland, changing the across‐shelf pressure gradient in the North Atlantic and altering heat convergence in the Labrador Sea. We also find that nonlocal (in space and time) heat fluxes (e.g., in the Irminger Sea, the seas south of Iceland) can have a strong impact on Labrador Sea heat content. Understanding and predicting the state of the Labrador Sea and its potential impacts on North Atlantic climate and global surface warming will require monitoring of oceanic and atmospheric properties at remote sites in the Irminger Sea, the subpolar gyre, and along the West African and European shelf/coast system, among others.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".