Influence of Midlatitude Surface Thermal Anomalies on the Polar Midtroposphere in an Idealized Moist Model
Bibliographic record
Abstract
Abstract Evidence from models and observations suggests that the vertical distribution of entropy in the extratropical troposphere reflects the horizontal distribution of entropy at the surface. This isentropic linkage, which is accomplished through moist isentropic mass transport driven by extratropical waves, becomes more apparent when the effect of latent heat release by condensing moist parcels is accounted for. This study focuses on the stratification of the Arctic troposphere, which is connected by moist isentropes to the midlatitude surface. A relatively simple moist general circulation model without radiative feedbacks involving water vapor or clouds is used to study the linkage between the midlatitude surface and the Arctic midtroposphere. Zonally symmetric midlatitude thermal perturbations switched on at the surface drive a moist potential temperature response in the Arctic midtroposphere with a lag of about 2 weeks. This response increases the gross moist vertical stability in the Arctic while generally decreasing it, or increasing it only weakly, in the midlatitudes. The moist isentropic streamfunction is shifted poleward owing to the poleward entropy flux response and is shifted upward (i.e., to higher entropy) owing to the zonal-mean entropy response. The results suggest a potential novel mechanism by which the midlatitudes might influence polar lapse rates and their associated radiative feedbacks.
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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) | 0.001 | 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".