Solar Geoengineering in the Polar Regions: A Review
Bibliographic record
Abstract
Abstract Solar geoengineering refers to proposals, including stratospheric aerosol injection (SAI), to slow or reverse climate change by reflecting away incoming sunlight. The rapid changes ongoing in the Arctic and Antarctic, and the risk of exceeding tipping points in the cryosphere within decades, make limiting such changes a plausible objective of solar geoengineering. Here, we review the impacts of SAI on polar climate and cryosphere, including the dependence of these impacts on the latitude(s) of injection, and make recommendations for future research directions. SAI would cool the polar regions and reduce many changes in polar climate under future warming scenarios. Some under‐cooling of the polar regions relative to the global mean is expected under SAI without high latitude injection, due to latitudinal variation in insolation and CO2 forcing, the forcing dependence of the polar lapse rate feedback, and altered atmospheric dynamics. There are also potential limitations in the effectiveness of SAI to arrest changes in winter‐time polar climate and to prevent sea‐level rise from the Antarctic ice sheet. Finally, we also review the prospects for three other solar geoengineering proposals targeting the poles: marine cloud brightening, cirrus cloud thinning, and sea‐ice albedo modification. Sea‐ice albedo modification appears unlikely to be viable on pan‐Arctic or Antarctic scales. Whether marine cloud brightening or cirrus cloud thinning would be effective in the polar regions remains uncertain. Solar geoengineering is an increasingly prominent proposal and a robust understanding of its consequences in the polar regions is needed to inform climate policy in the 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".