Factors contributing to the unusually low Antarctic springtime ozone in 2020-2023
Bibliographic record
Abstract
The 2020–2023 Antarctic spring seasons saw large ozone holes, significant ozone mass deficit, and low polar cap total ozone compared to the second decade of the 21st Century, prompting questions about the pace of ozone recovery over Antarctica. Here we use a stratospheric composition reanalysis developed at the NASA Global Modeling and Assimilation Office, and chemical ozone loss estimates derived from NASA’s Aura Microwave Limb Sounder observations to identify the key factors contributing to these unusually large ozone holes. We find that the below-average Antarctic ozone in each of the years of interest resulted from a different combination of the following: anomalous initial polar vortex ozone content, chemical ozone depletion, dynamical ozone resupply, and the size and geometry of the stratospheric polar vortex, with dynamically-driven factors playing a key role. We also interpret our findings in the broader context of ozone recovery, with a particular focus on September, the month when signs of recovery are most evident. We find no evidence challenging the current consensus that springtime Antarctic ozone is recovering in response to the implementation of the Montreal Protocol and its amendments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".