Injecting solid particles into the stratosphere could mitigate global warming but currently entails great uncertainties
Bibliographic record
Abstract
Stratospheric aerosol injection could mitigate harmful effects of global warming, but could have undesirable side effects, such as warming the stratosphere and depleting the ozone layer. We explore the potential benefits of solid alumina and calcite particles as alternatives to sulfate aerosols by using an experimentally informed aerosol-chemistry-climate model. Compared to sulfur dioxide, injection of solids reduces stratospheric warming by up to 70% and diffuse radiation by up to 40%, highlighting their potential benefits. Achieving −1 W m−2 of radiative forcing would likely result in very small ozone changes, but sizable uncertainties remain. These arise from poorly understood heterogeneous chemical and microphysical processes, which, under less likely assumptions, could lead to larger global ozone column changes between −14% and +4%. Our work provides recommendations for improving the understanding of stratospheric aerosol injection using materials other than sulfur dioxide, and underscores the need for kinetic laboratory studies. Solar radiation modification by stratospheric solid particle injection could significantly reduce risks from stratospheric heating compared with equivalent sulfate aerosol injection, but impacts on global ozone levels are uncertain, suggest simulations of aerosol microphysics with chemical and radiative interactions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".