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Record W2126591422 · doi:10.5194/acp-10-9851-2010

Constraints on interactions between aerosols and clouds on a global scale from a combination of MODIS-CERES satellite data and climate simulations

2010· article· en· W2126591422 on OpenAlexafffund
Xiaoyan Ma, Knut von Salzen, Jason N. S. Cole

Bibliographic record

VenueAtmospheric chemistry and physics · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of VictoriaEnvironment and Climate Change Canada
FundersCanadian Foundation for Climate and Atmospheric SciencesLangley Research CenterNational Aeronautics and Space Administration
KeywordsAerosolEffective radiusCloud computingLiquid water pathEnvironmental scienceAtmospheric sciencesCloud albedoLiquid water contentSatelliteMeteorologyCloud coverPhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract. Satellite-based cloud top effective radius retrieved by the CERES Science Team were combined with simulated aerosol concentrations from CCCma CanAM4 to examine relationships between aerosol and cloud that underlie the first aerosol indirect (cloud albedo) effect. Evidence of a strong negative relationship between sulphate, and organic aerosols, with cloud top effective radius was found for low clouds, indicating both aerosol types are contributing to the first indirect effect on a global scale. Furthermore, effects of aerosol on the cloud droplet effective radius are more pronounced for larger cloud liquid water paths. While CanAM4 broadly reproduces the observed relationship between sulphate aerosols and cloud droplets, it does not reproduce the dependency of cloud top droplet size on organic aerosol concentrations nor the dependency on cloud liquid water path. Simulations with a modified version of the model yield a more realistic dependency of cloud droplets on organic carbon. The robustness of the methods used in the study are investigated by repeating the analysis using aerosol simulated by the GOCART model and cloud top effective radii derived from the MODIS Science Team.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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 designObservational
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

Citations18
Published2010
Admission routes2
Has abstractyes

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