Recent Arctic ozone depletion: Is there an impact of climate change?
Bibliographic record
Abstract
After the well-reported record loss of Arctic stratospheric ozone of up to 38% in the winter 2010–2011, further large depletion of 27% occurred in the winter 2015–2016. Record low winter polar vortex temperatures, below the threshold for ice polar stratospheric cloud (PSC) formation, persisted for one month in January 2016. This is the first observation of such an event and resulted in unprecedented dehydration/denitrification of the polar vortex. Although chemistry–climate models (CCMs) generally predict further cooling of the lower stratosphere with the increasing atmospheric concentrations of greenhouse gases (GHGs), significant differences are found between model results indicating relatively large uncertainties in the predictions. The link between stratospheric temperature and ozone loss is well understood and the observed relationship is well captured by chemical transport models (CTMs). However, the strong dynamical variability in the Arctic means that large ozone depletion events like those of 2010–2011 and 2015–2016 may still occur until the concentrations of ozone-depleting substances return to their 1960 values. It is thus likely that the stratospheric ozone recovery, currently anticipated for the mid-2030s, might be significantly delayed. Most important in order to predict the future evolution of Arctic ozone and to reduce the uncertainty of the timing for its recovery is to ensure continuation of high-quality ground-based and satellite ozone observations with special focus on monitoring the annual ozone loss during the Arctic winter.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".