Quantification of Chemical and Dynamical loss in Recent Antarctic Ozone depletion
Bibliographic record
Abstract
The Montreal Protocol which mandated the global phase-out of ozone-depleting substances, contributed to the gradual recovery of Antarctic stratospheric ozone. Current projections estimate that the Antarctic ozone level recovers to the 1980 values by 2066. However, anomalous behaviours of the Antarctic ozone hole such as increased ozone hole area and prolonged ozone depletion have been observed since 2020. During this period, extreme events such as the Australian bushfires in 2020 and the Hunga Tonga–Hunga Haʻapai volcanic eruption in 2022 injected a significant amount of aerosols into the lower stratosphere. These aerosols provided the surface for chlorine activation reactions, contributing to chemical ozone loss in the polar lower stratosphere. Concurrently, previous studies suggest that the observed ozone depletion is also attributed to dynamic changes in the polar vortex and the descent of mesospheric air to the lower stratosphere. However, the relative percentage contribution of chemical and dynamical loss contributing to total ozone loss in recent years remains unquantified. In this study, we decompose the total ozone loss into chemical and dynamical losses using the passive tracer method, where ozone is considered as a passive tracer and simulated in the Chemical Lagrangian Model of the Stratosphere (CLaMS) using reanalysis data. The difference between the observed and simulated ozone provides information about the chemical ozone loss. These findings will help in advancing our understanding of the factors leading to the recent enhanced ozone depletion and the potential implications on the long-term healing of the ozone layer.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".