Variations of the residual circulation in the Northern Hemispheric winter
Bibliographic record
Abstract
A multiyear time series of the vortex‐averaged diabatic descent for 47 Arctic winters from 1957/1958 until 2003/2004 is presented. The climatology of diabatic descent is based on trajectory calculations coupled with diabatic heating rate calculations carried out in the polar lower stratosphere of the Northern Hemisphere winters. We demonstrate the improved performance of the approach based on diabatic heating rates compared to the approach based on vertical winds from meteorological analysis. The time series of the vortex‐averaged diabatic descent gives a detailed picture of intensity and altitude dependence of the stratospheric vertical transport processes during the Arctic winter. In addition to the overall vortex‐averaged diabatic descent, the spatial structure of the descent is analyzed for two different Arctic winters. We demonstrate for this case study that not only the intensity but also the zonal structure of the diabatic descent depends on the meteorological conditions in the polar vortex. The climatology is characterized by very pronounced interannual variability which is linked to the variability of temperature anomalies and to the variability of Eliassen‐Palm (EP)‐flux anomalies, wherein strong planetary wave activity leads to strong diabatic descent and vice versa. The correlation between EP‐flux and descent shows that tropospheric dynamics have a strong influence on the strength of the polar branch of the residual circulation by means of the atmospheric wave activity.
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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 | 0.000 |
| 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) | 0.001 | 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".