Shortwave radiative forcing, rapid adjustment, and feedback to the surface by sulfate geoengineering: analysis of the Geoengineering Model Intercomparison Project G4 scenario
Bibliographic record
Abstract
Abstract. This study evaluates the forcing, rapid adjustment, and feedback of net shortwave radiation at the surface in the G4 experiment of the Geoengineering Model Intercomparison Project by analysing outputs from six participating models. G4 involves injection of 5 Tg yr−1 of SO2, a sulfate aerosol precursor, into the lower stratosphere from year 2020 to 2069 against a background scenario of RCP4.5. A single-layer atmospheric model for shortwave radiative transfer is used to estimate the direct forcing of solar radiation management (SRM), and rapid adjustment and feedbacks from changes in the water vapour amount, cloud amount, and surface albedo (compared with RCP4.5). The analysis shows that the globally and temporally averaged SRM forcing ranges from −3.6 to −1.6 W m−2, depending on the model. The sum of the rapid adjustments and feedback effects due to changes in the water vapour and cloud amounts increase the downwelling shortwave radiation at the surface by approximately 0.4 to 1.5 W m−2 and hence weaken the effect of SRM by around 50 %. The surface albedo changes decrease the net shortwave radiation at the surface; it is locally strong (∼ −4 W m−2) in snow and sea ice melting regions, but minor for the global average. The analyses show that the results of the G4 experiment, which simulates sulfate geoengineering, include large inter-model variability both in the direct SRM forcing and the shortwave rapid adjustment from change in the cloud amount, and imply a high uncertainty in modelled processes of sulfate aerosols and clouds.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".