Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)
Bibliographic record
Abstract
Abstract. Present day tropospheric ozone and its changes between 1850 and 2100 are considered, analysing 15 global models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The multi-model mean compares well against present day observations. The seasonal cycle correlates well, except for some locations in the tropical upper troposphere. Most (75%) of the models are encompassed with a range of global mean tropospheric ozone column estimates from satellite data, although there is a suggestion of a high bias in the Northern Hemisphere and a low bias in the Southern Hemisphere. Compared to the present day multi-model mean tropospheric ozone burden of 337 Tg, the multi-model mean burden for 1850 time slice is ~ 30% lower. Future changes were modelled using emissions and climate projections from four Representative Concentration Pathways (RCPs). Compared to 2000, the relative changes for the tropospheric ozone burden in 2030 (2100) for the different RCPs are: −5% (−22%) for RCP2.6, 3% (−8%) for RCP4.5, 0% (−9%) for RCP6.0, and 5% (15%) for RCP8.5. Model agreement on the magnitude of the change is greatest for larger changes. Reductions in precursor emissions are common across the RCPs and drive ozone decreases in all but RCP8.5, where doubled methane and a larger stratospheric influx increase ozone. Models with high ozone abundances for the present day also have high ozone levels for the other time slices, but there are no models consistently predicting large or small changes. Spatial patterns of ozone changes are well correlated across most models, but are notably different for models without time evolving stratospheric ozone concentrations. A unified approach to ozone budget specifications is recommended to help future studies attribute ozone changes and inter-model differences more clearly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".