Adiabatic Processes Contribute to the Rapid Warming of Subpolar North Atlantic During 1993–2010
Bibliographic record
Abstract
Abstract The Subpolar North Atlantic (SPNA) is a region with complex dynamics, and governs the global heat transport by regulating the Atlantic Meridional Overturning Circulation. During 1993–2010, the upper ocean of SPNA has rapidly warmed. Most studies to date focused on the diabatic processes and meridional heat transport leading to this rapid warming, neglecting the role of adiabatic processes and associated heat redistribution. Here, we investigate the ability of adiabatic Rossby wave adjustment to produce this warming event by designed numerical experiments with a set of simple models, including one‐layer model, reduced‐gravity model and two‐layer model. The comparison between these numerical simulations with observations demonstrates that this rapid warming in the western SPNA is partly generated by the wind stress anomalies. The wind stress curl anomalies in the central and eastern of SPNA trigger the topographic and planetary Rossby waves, propagating the downwelling signals along their waveguides to redistribute heat in the upper ocean and warm the Labrador Sea and Irminger Sea with a 4‐ or 7‐year time lag. Hence, the baroclinic mode dominates the magnitude of the adiabatic warming in the SPNA and the topography shapes its spatial pattern. In addition, local and remote wind forcing jointly contributes to this warming.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".