Response to marine cloud brightening in a multi-model ensemble
Bibliographic record
Abstract
Abstract. Here we show results from Earth system model simulations from the marine cloud brightening experiment G4cdnc of the Geoengineering Model Intercomparison Project (GeoMIP). The nine contributing models prescribe a 50 % increase in the cloud droplet number concentration (CDNC) of low clouds over the global oceans in an experiment dubbed G4cdnc, with the purpose of counteracting the radiative forcing due to anthropogenic greenhouse gases under the RCP4.5 scenario. The model ensemble median effective radiative forcing (ERF) amounts to −1.9 W m−2, with a substantial inter-model spread of −0.6 to −2.5 W m−2. The large spread is partly related to the considerable differences in clouds and their representation between the models, with an underestimation of low clouds in several of the models. All models predict a statistically significant temperature decrease with a median of (for years 2020–2069) −0.96 [−0.17 to −1.21] K relative to the RCP4.5 scenario, with particularly strong cooling over low-latitude continents. Globally averaged there is a weak but significant precipitation decrease of −2.35 [−0.57 to −2.96] % due to a colder climate, but at low latitudes there is a 1.19 % increase over land. This increase is part of a circulation change where a strong negative top-of-atmosphere (TOA) shortwave forcing over subtropical oceans, caused by increased albedo associated with the increasing CDNC, is compensated for by rising motion and positive TOA longwave signals over adjacent land regions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".