Ozone in a stratospheric aerosol injection scenario
Bibliographic record
Abstract
Stratospheric aerosol injection (SAI) holds the potential to offset some of the future warming of the Earth’s surface. It comes with many potentially dangerous side effects, however, which are currently not well understood and poorly constrained. A major concern is the effect on stratospheric ozone, which could be weakened and delayed in its recovery, given that ozone-depleting substances will take decades to be completely removed. We are interested in ozone depletion and recovery in a scenario, where SAI is employed to keep the global surface temperature constant. Previous analyses have been conducted with models that have widely different treatments of aerosol microphysics and chemistry. To isolate and estimate the uncertainty of the chemical and dynamical effects in a multi-model context, CCMI-2022 proposed a new senD2-sai experiment, where the ocean is kept fixed and the elevated stratospheric aerosol burden, thus, only affects the middle atmospheric composition and temperature. Stratospheric aerosols are also uniformly prescribed for all participating models in order to minimize the uncertainty arising from the treatment of aerosol microphysics. In our work, we perform these experiments with our aerosol-chemistry-climate model SOCOLv4.0, and compare our results with other CCMI-2022 models, with a focus on the stratospheric ozone and temperature changes. We evaluate the role of individual processes, such as ozone destruction cycles and changes in large-scale transport. In addition, we discuss implementation issues related to imposing this aerosol forcing, as this will help in the interpretation of the main inter-model uncertainties. Finally, we discuss the implications of this work for our understanding of chemical feedbacks in future climate in the context of mitigation via SAI, and its relevance for future ozone assessments.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".