An investigation of the relationship between tropical monsoon precipitation changes and stratospheric sulfate aerosol optical depth
Bibliographic record
Abstract
Abstract Stratospheric aerosol geoengineering (SAG) is one of the several solar geoengineering options that have been proposed to counteract climate change. In the case of SAG, reflective aerosols injected into the stratosphere would reflect more sunlight and cool the planet. When assessing the potential efficacy and risks of SAG, the sensitivity of tropical monsoon precipitation changes should be also considered. Using a climate model, we perform several stylized simulations with different meridional distributions and amounts of volcanic sulfate aerosols in the stratosphere. Because tropical monsoon precipitation responds to global mean and interhemispheric difference in radiative forcing or temperature, we quantify the sensitivity of tropical monsoon precipitation to SAG in terms of two parameters: global mean aerosol optical depth (GMAOD) and interhemispheric AOD difference (IHAODD). For instance, we find that the simulated northern hemisphere monsoon precipitation has a sensitivity of −1.33 ± 0.95% per 0.1 increase in GMAOD and −7.62 ± 0.27% per 0.1 increase in IHAODD. Our estimated precipitation changes in terms of the two sensitivity parameters for the global mean precipitation and for the indices of tropical, northern hemisphere, southern hemisphere and Indian summer monsoon precipitation are in good agreement with the model simulated precipitation changes. Similar sensitivity estimates are also made for unit changes in global mean and interhemispheric differences in effective radiative forcing and surface temperature. Our study based on planetary energetics provides a simpler framework for understanding the tropical monsoon precipitation response to external forcing agents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".