Forcings and feedbacks in the GeoMIP ensemble for a reduction in solar irradiance and increase in CO<sub>2</sub>
Bibliographic record
Abstract
Abstract The effective radiative forcings (including rapid adjustments) and feedbacks associated with an instantaneous quadrupling of the preindustrial CO2 concentration and a counterbalancing reduction of the solar constant are investigated in the context of the Geoengineering Model Intercomparison Project (GeoMIP). The forcing and feedback parameters of the net energy flux, as well as its different components at the top‐of‐atmosphere (TOA) and surface, were examined in 10 Earth System Models to better understand the impact of solar radiation management on the energy budget. In spite of their very different nature, the feedback parameter and its components at the TOA and surface are almost identical for the two forcing mechanisms, not only in the global mean but also in their geographical distributions. This conclusion holds for each of the individual models despite intermodel differences in how feedbacks affect the energy budget. This indicates that the climate sensitivity parameter is independent of the forcing (when measured as an effective radiative forcing). We also show the existence of a large contribution of the cloudy‐sky component to the shortwave effective radiative forcing at the TOA suggesting rapid cloud adjustments to a change in solar irradiance. In addition, the models present significant diversity in the spatial distribution of the shortwave feedback parameter in cloudy regions, indicating persistent uncertainties in cloud feedback mechanisms.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".