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Record W2608253988 · doi:10.1002/2017gl073056

Microphysical explanation of the RH‐dependent water affinity of biogenic organic aerosol and its importance for climate

2017· article· en· W2608253988 on OpenAlexafffund
Narges Rastak, Aki Pajunoja, Juan C. Acosta Navarro, Mijung Song, Daniel G. Partridge, Alf Kirkevåg, Yu Jun Leong, Weiwei Hu, Nathan F. Taylor, Andrew T. Lambe, K. M. Cerully, Aikaterini Bougiatioti, Perry Liu, Radovan Krejčí, Tuukka Petäjä, Carl J. Percival, P. Davidovits, Douglas R. Worsnop, Annica M. L. Ekman, Athanasios Nenes, Scot T. Martin, J. L. Jiménez, Don Collins, David Topping, Allan K. Bertram, Andreas Zuend, Annele Virtanen, Ilona Riipinen

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British ColumbiaMcGill University
FundersH2020 European Research CouncilNatural Environment Research CouncilHorizon 2020 Framework ProgrammeNorges ForskningsrådNordForskNatural Sciences and Engineering Research Council of CanadaKnut och Alice Wallenbergs StiftelseSvenska Forskningsrådet FormasNational Oceanic and Atmospheric AdministrationSight Research UKU.S. Environmental Protection AgencyNational Supercomputing Centre SingaporeGeorgia Institute of TechnologyU.S. Department of EnergyElectric Power Research InstituteVetenskapsrådetNational Science Foundation
KeywordsIsopreneAerosolEnvironmental scienceAtmospheric sciencesAtmosphere (unit)Climate modelEnvironmental chemistryClimate changeMeteorologyChemistryGeologyOceanographyPolymer

Abstract

fetched live from OpenAlex

A large fraction of atmospheric organic aerosol (OA) originates from natural emissions that are oxidized in the atmosphere to form secondary organic aerosol (SOA). Isoprene (IP) and monoterpenes (MT) are the most important precursors of SOA originating from forests. The climate impacts from OA are currently estimated through parameterizations of water uptake that drastically simplify the complexity of OA. We combine laboratory experiments, thermodynamic modeling, field observations, and climate modeling to (1) explain the molecular mechanisms behind RH-dependent SOA water-uptake with solubility and phase separation; (2) show that laboratory data on IP- and MT-SOA hygroscopicity are representative of ambient data with corresponding OA source profiles; and (3) demonstrate the sensitivity of the modeled aerosol climate effect to assumed OA water affinity. We conclude that the commonly used single-parameter hygroscopicity framework can introduce significant error when quantifying the climate effects of organic aerosol. The results highlight the need for better constraints on the overall global OA mass loadings and its molecular composition, including currently underexplored anthropogenic and marine OA sources.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.339

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 studies0.0000.000
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 head0.036
GPT teacher head0.283
Teacher spread0.246 · 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 designBench or experimental
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

Citations145
Published2017
Admission routes2
Has abstractyes

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