Hemispheric asymmetry in stratospheric trends of HCl and ozone: Impact of chemical feedback on ozone recovery
Bibliographic record
Abstract
We use trace gas profiles from Atmospheric Chemistry Experiment - Fourier Transform Spectrometer (ACE-FTS) satellite measurements and the TOMCAT three-dimensional chemical transport model to diagnose stratospheric trends in O3, HCl and N2O. We find that the 2004-2021 ACE-FTS trends exhibit a clear lower stratosphere (LS) interhemispheric asymmetry with positive (negative) O3 and N2O (HCl) trends in the Southern Hemisphere (SH), and trends of opposite sign in the Northern Hemisphere (NH). The trends are larger for the shorter time period of 2004-2018. TOMCAT qualitatively agrees with the ACE-FTS LS N2O and HCl trends, confirming that transport variability drives such patterns, despite some discrepancies for O3. An additional model simulation is used to quantify the sensitivity of O3 to long-term changes in chlorine and bromine and thus determine the chemical contribution of the spatially varying halogen trends to both observed and modelled O3 trends. Overall, the recent dynamically induced variation in mid-latitude LS halogen abundance has, through chemical feedback, accentuated the O3 recovery signal in the SH and delayed it in the NH, reflecting the enhanced dynamical variability of the NH. These results further indicate the complexities that exist in the search for the signal of ozone recovery in the mid-latitude lower stratosphere.
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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 | 0.001 | 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".