Evaluation of interannual variability of Arctic and Antarctic ozone loss since 1989
Bibliographic record
Abstract
Ozone depletion over Polar Regions is monitored each year by satellite and ground-based instruments. The first signs of healing of the ozone layer linked to the decrease of ozone destructive substances (ODSs) were observed in Antarctica using different metrics (ozone mean values, ozone mass deficit, area of the ozone hole) and simple or sophisticated models. Chemistry climate models predict that climate change will not affect expected ozone recovery over Antarctica but will accelerate recovery in the Arctic due to the possible enhancement of the Brewer Dobson circulation. However, ozone loss observations by SAOZ UV-Vis spectrometers do not show a clear sign of recovery in the latter region. In addition, a record of 38% ozone loss in 2010/2011 and 2019/2020 was estimated. In this study, the vortex-averaged ozone loss in the last three decades will be evaluated for both Polar Regions using the passive ozone tracer of two chemical transport models (REPROBUS and SLIMCAT CTMs) and total ozone observations from SAOZ and satellite observations (IASI/METOP and Multi-Sensor Reanalysis (MSR-2)). The tracer method allows us to determine the evolution of the daily rate of ozone destruction, and the amplitude of the cumulative loss at the end of the winter. The cumulative ozone destruction in the Artic varies between 0-10% in relatively warm winters with short vortex duration to up to 25-38% in colder winters with longer vortex persistence, while in Antarctica it is mostly stable, around 50%. Interannual variability of 10-days average rate will be analyzed and compared between both hemispheres as well as the timing to reach different thresholds of absolute ozone loss values. Finally, linear trend of ozone loss and temperature since 2000 will be estimated in both Polar Regions in order to evaluate possible ozone recovery.
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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 | 0.001 | 0.001 |
| 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".