The Importance of a Properly Represented Stratosphere for Northern Hemisphere Surface Variability in the Atmosphere and the Ocean
Bibliographic record
Abstract
Major sudden stratospheric warmings (SSWs) are extreme events during boreal winter, which not only impact tropospheric weather up to three months but also can influence oceanic variability through wind stress and heat flux anomalies. In the North Atlantic region, SSWs have the potential to modulate deep convection in the Labrador Sea and thereby the strength of the Atlantic meridional overturning circulation. The impact of SSWs on the Northern Hemisphere surface climate is investigated in two coupled climate models: a stratosphere-resolving (high top) and a non-stratosphere-resolving (low top) model. In both configurations, a robust link between SSWs and a negative NAO is detected, which leads to shallower-than-normal North Atlantic mixed layer depth. The frequency of SSWs and the persistence of this link is better captured in the high-top model. Significant differences occur over the Pacific region, where an unrealistically persistent Aleutian low is observed in the low-top configuration. An overrepresentation of SSWs during El Niño conditions in the low-top model is the main cause for this artifact. Our results underline the importance of a proper representation of the stratosphere in a coupled climate model for a consistent surface response in both the atmosphere and the ocean, which, among others, may have implications for oceanic deep convection in the subpolar North Atlantic.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".