Large Impacts, Past and Future, of Ozone‐Depleting Substances on Brewer‐Dobson Circulation Trends: A Multimodel Assessment
Bibliographic record
Abstract
Abstract Substantial increases in the atmospheric concentration of well‐mixed greenhouse gases (notably CO2), such as those projected to occur by the end of the 21st century under large radiative forcing scenarios, have long been known to cause an acceleration of the Brewer‐Dobson circulation (BDC) in climate models. More recently, however, several single‐model studies have proposed that ozone‐depleting substances might also be important drivers of BDC trends. As these studies were conducted with different forcings over different periods, it is difficult to combine them to obtain a robust quantitative picture of the relative importance of ozone‐depleting substances as drivers of BDC trends. To this end, we here analyze—over identical past and future periods—the output from 20 similarly forced models, gathered from two recent chemistry‐climate modeling intercomparison projects. Our multimodel analysis reveals that ozone‐depleting substances are responsible for more than half of the modeled BDC trends in the two decades 1980–2000. We also find that, as a consequence of the Montreal Protocol, decreasing concentrations of ozone‐depleting substances in coming decades will strongly decelerate the BDC until the year 2080, reducing the age‐of‐air trends by more than half, and will thus substantially mitigate the impact of increasing CO2. As ozone‐depleting substances impact BDC trends, primarily, via the depletion/recovery of stratospheric ozone over the South Pole, they impart seasonal and hemispheric asymmetries to the trends which may offer opportunities for detection in coming decades.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".