An updated analysis of the attribution of stratospheric ozone and temperature changes to changes in ozone-depleting substances and well-mixed greenhouse gases
Bibliographic record
Abstract
Abstract. This paper presents an analysis of the attribution of past and future changes in stratospheric ozone and temperature to anthropogenic forcings. Recently, Shepherd and Jonsson (2008) argued that such an analysis needs to account for the ozone-temperature feedback, and that the failure to do so could potentially lead to very large errors. This point was illustrated by analyzing chemistry-climate simulations from the Canadian Middle Atmosphere Model (CMAM) and attributing both past and future changes to changes in the abundances of ozone-depleting substances (ODS) and well-mixed greenhouse gases. In the current paper, we have expanded the analysis to account for the nonlinear radiative response to changes in CO2. It is shown that over centennial time scales the relationship between CO2 abundance and radiative cooling in the upper stratosphere is significantly nonlinear. Failure to account for this effect in multiple linear regression analysis would lead to misleading results. In our attribution analysis the nonlinearity is taken into account by using CO2 heating rate, rather than CO2 abundance, as the explanatory variable. In addition, an error in the way the CO2 forcing changes are implemented in the CMAM has been corrected, which significantly affects the results for the recent past. As the radiation scheme, based on Fomichev et al. (1998), is used in several other models we provide some description of the problem and how it was fixed. The updated results are as follows. From 1975–1995, during the period of rapid ozone decline, ODS and CO2 increases contributed roughly equally to upper stratospheric cooling, while the CO2-induced cooling (which increases ozone) masked about 20% of the ODS-induced ozone depletion. From 2010–2040, during the period of most rapid ozone recovery, CO2-induced cooling will dominate the upper stratospheric temperature trend and will contribute roughly equally with the ODS decline to ozone increases, effectively doubling the rate of 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".