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Record W2510311016 · doi:10.5194/acp-2016-711

Shortwave radiative forcing and feedback to the surface by sulphate geoengineering: Analysis of the Geoengineering Model Intercomparison Project G4 scenario

2016· article· en· W2510311016 on OpenAlexaff
Hiroki Kashimura, Manabu Abe, Shingo Watanabe, Takashi Sekiya, Duoying Ji, John C. Moore, Jason N. S. Cole, Ben Kravitz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsShortwaveRadiative forcingEnvironmental scienceAlbedo (alchemy)Forcing (mathematics)Atmospheric sciencesShortwave radiationCloud forcingCloud feedbackClimatologyCloud albedoAerosolRadiative transferGeoengineeringClimate modelMeteorologyRadiationClimate changeCloud coverClimate sensitivityCloud computingPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract. This study evaluates the forcing 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 sulphate aerosol precursor, into the lower stratosphere from year 2020 to 2070 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 feedback effects 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 feedback effects due to changes in the water vapour and cloud amounts on net shortwave radiation have heating effects ranging from approximately 0.4 to 1.5 W m−2 and weaken the effect of SRM by around 50 %. The surface albedo changes have a cooling effect, which 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 sulphate geoengineering, include large inter-model variability both in the direct SRM forcing and the feedback from changes in the cloud amount, and imply a high uncertainty in modelled processes of sulphate aerosols and clouds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.226
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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