Why are Temperature and Upward Wave Activity Flux Positively Skewed in the Polar Stratosphere?
Bibliographic record
Abstract
Abstract The distribution of temperatures in the wintertime polar stratosphere is significantly positively skewed, which has important implications for the characteristics of ozone chemistry and stratosphere–troposphere coupling. The typical argument for why the temperature distribution is skewed is that radiative balance sets a firm lower limit, while planetary wave driving can force much larger positive anomalies in temperature. However, the distribution of the upward Eliassen–Palm (EP) flux is also positively skewed, and this suggests that dynamics may play an important role in setting the skewness of the temperature distribution. This study explains the skewness of the upward EP flux distribution by appealing to the ideas of linear interference. In this framework, fluxes are decomposed into a linear term (LIN) that measures the coherence of the wave anomaly and the climatological wave and an additional nonlinear term (NONLIN) that depends only on the wave anomaly. It is shown that when filtered by wavenumber, there is a clear nonlinear dependence between LIN and NONLIN: the terms cancel when LIN is negative, but they reinforce each other when LIN is positive. This leads to the positive skewness of the upward wave activity flux. A toy model of wave interference is constructed, and it is shown that the westward vertical tilt of the climatological wave is the key ingredient to a positively skewed upward EP flux distribution. The causes of the skews of the LIN and NONLIN distributions themselves are shown to be related to relationships between wave phase and amplitude, and wave phase and vertical tilt, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".