N <sub>2</sub> O as a regression proxy for dynamical variability in stratospheric trace gas trends
Bibliographic record
Abstract
Trends in stratospheric trace gases like HCl, N 2 O, O 3 , and NO y show a hemispheric asymmetry over the last 2 decades, with trends having opposing signs in the Northern Hemisphere and Southern Hemisphere. Here we use N 2 O, a long-lived tracer with a tropospheric source, as a proxy for stratospheric circulation in the multiple linear regression model used to calculate stratospheric trace gas trends. This is done in an effort to isolate trends due to circulation changes from trends due to the chemical effects of ozone-depleting substances. Measurements from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) and the Optical Spectrograph and InfraRed Imager System (OSIRIS) are considered, along with model results from the Whole Atmosphere Community Climate Model (WACCM). Trends in HCl, O 3 , and NO y for 2004–2018 are examined. Using the N 2 O regression proxy, we show that observed HCl increases in the Northern Hemisphere are due to changes in the stratospheric circulation. We also show that negative O 3 trends above 30 hPa in the Northern Hemisphere can be explained by a change in the circulation but that negative ozone trends at lower levels cannot. Trends in stratospheric NO y are found to be largely consistent with trends in N 2 O.
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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 | 0.000 | 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".