Attribution of observed changes in stratospheric ozone and temperature
Bibliographic record
Abstract
Abstract. Three recently-completed sets of simulations of multiple chemistry-climate models with greenhouse gases only, with all anthropogenic forcings, and with anthropogenic and natural forcings, allow the causes of observed stratospheric changes to be quantitatively assessed using detection and attribution techniques. The total column ozone response to halogenated ozone depleting substances and to natural forcings is detectable and consistent in models and observations. However, the total ozone response to greenhouse gases in the models and observations appears to be inconsistent, which may be due to the models' inability to properly simulate tropospheric ozone changes. In the middle and upper stratosphere, simulated and observed SBUV/SAGE ozone changes are broadly consistent, and separate anthropogenic and natural responses are detectable in observations. The influence of ozone depleting substances and natural forcings can also be detected separately in observed lower stratospheric temperature, and the magnitudes of the simulated and observed responses to these forcings and to greenhouse gas changes are found to be consistent. In the mid and upper stratosphere the simulated natural and combined anthropogenic responses are detectable and consistent with observations, but the influences of greenhouse gases and ozone-depleting substances could not be separately detected in our analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".