O<sub>3</sub>‐N<sub>2</sub>O correlations from the Atmospheric Chemistry Experiment: Revisiting a diagnostic of transport and chemistry in the stratosphere
Bibliographic record
Abstract
Our knowledge of stratospheric O3‐N2O correlations is extended, and their potential for model‐measurement comparison assessed, using data from the Atmospheric Chemistry Experiment (ACE) satellite and the Canadian Middle Atmosphere Model (CMAM). ACE provides the first comprehensive data set for the investigation of interhemispheric, interseasonal, and height‐resolved differences of the O3‐N2O correlation structure. By subsampling the CMAM data, the representativeness of the ACE data is evaluated. In the middle stratosphere, where the correlations are not compact and therefore mainly reflect the data sampling, joint probability density functions provide a detailed picture of key aspects of transport and mixing, but also trace polar ozone loss. CMAM captures these important features, but exhibits a displacement of the tropical pipe into the Southern Hemisphere (SH). Below about 21 km, the ACE data generally confirm the compactness of the correlations, although chemical ozone loss tends to destroy the compactness during late winter/spring, especially in the SH. This allows a quantitative comparison of the correlation slopes in the lower and lowermost stratosphere (LMS), which exhibit distinct seasonal cycles that reveal the different balances between diabatic descent and horizontal mixing in these two regions in the Northern Hemisphere (NH), reconciling differences found in aircraft measurements, and the strong role of chemical ozone loss in the SH. The seasonal cycles are qualitatively well reproduced by CMAM, although their amplitude is too weak in the NH LMS. The correlation slopes allow a “chemical” definition of the LMS, which is found to vary substantially in vertical extent with season.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".