The Potential Environmental and Climate Impacts of Stratospheric Aerosol Injection: A Review
Bibliographic record
Abstract
Given the rise in global mean temperature as a direct consequence of increasing levels of greenhouse gases (GHG) in the atmosphere, a variety of climate engineering approaches, including stratospheric aerosol injection (SAI), have been proposed. Often criticized as a distraction from global efforts towards reducing GHG emissions, SAI aims to increase the Earth’s albedo by seeding aerosols in the lower stratosphere. SAI has been explored extensively in modeling studies based on observations of temporary cooling of the Earth’s surface following major volcanic eruptions which introduced significant loadings of sulfate particles into the stratosphere. The cooling effect is accompanied by other significant consequences including stratospheric heating, stratospheric ozone (O3) depletion, and reduced global mean precipitation. In order to understand the potential environmental and climate impacts of SAI, we review the state of the knowledge regarding these issues, starting from an aerosol science perspective. We summarize aerosol radiative properties and the role they play in defining the optimal chemical and physical aerosol characteristics for SAI, and their implications for lower stratospheric warming. We then review in depth the impacts of stratospheric aerosol heterogeneous chemistry on global O3 levels. We review SAI modeling studies as well as their uncertainties, in comparison to the observed environmental and climate impacts of volcanically derived sulfate aerosols, including impacts on global temperature, stratospheric warming, and hydrological cycle. We also discuss the current governance and economic considerations of the application of SAI and raise essential questions from both research and social standpoints that must be addressed before SAI is deployed for climate change mitigation.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".