Comment on “Observation of large and all-season ozone losses over the tropics” [AIP Adv. 12, 075006 (2022)]
Bibliographic record
Abstract
Lu (2022) (hereafter L2022) used the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) to argue that there has been very substantial ozone depletion (>80%) in the tropical (30°S–30°N) lower stratosphere (LS) since the 1960s. This was labeled a “large and all-season ozone hole.” Here, we show that this claim is false due to erroneously large tropical ozone values in the interpolated sparse historical TOST data. In addition, L2022 repeats the suggestion made in a number of the author’s earlier papers that cosmic rays are involved in stratospheric ozone depletion. This claim is also not valid; a huge body of work has explained the observed stratospheric ozone depletion through a well-established gas phase and heterogeneous chemistry following the emission of ozone-depleting substances (ODSs) through human activities. We expand on these points below. In particular, we present a simple analysis of the TOST dataset used by L2022 and show its unsuitability for the application performed. In contrast, we then summarize the much smaller observed variations in ozone in the tropical LS based on many international efforts of data validation and quality assurance, which are not cited by L2022. We then discuss flaws in the cosmic-ray electron-induced mechanism proposed by L2022 as being the main driver of stratospheric ozone losses.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.021 |
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".