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Record W2051145375 · doi:10.1175/2007jas2276.1

Aerosol–Cloud Interactions in a Mesoscale Model. Part II: Sensitivity to Aqueous-Phase Chemistry

2008· article· en· W2051145375 on OpenAlexaff
Irena Ivanova, H. G. Leighton

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMcGill University
Fundersnot available
KeywordsAerosolMesoscale meteorologyCloud condensation nucleiCloud physicsLiquid water contentEffective radiusCondensationAtmospheric chemistryMeteorologyParticle (ecology)Atmospheric sciencesCloud computingEnvironmental sciencePhysicsOzoneGeologyAstrophysicsComputer science

Abstract

fetched live from OpenAlex

Abstract The feedbacks between aerosols, cloud microphysics, and cloud chemistry are investigated in a mesoscale model. A simple bulk aqueous-phase sulfur chemistry scheme was fully coupled to the existing aerosol and microphysics schemes. The representation of aerosol and microphysics follows the explicit bulk double-moment approach. A case of summertime stratocumulus cloud system is simulated at high resolution (3-km grid spacing), and the evolution of an observed continental aerosol spectrum that changes during the course of the simulation as a result of cloud processing is examined. The results demonstrate that the bulk approach to the aerosol and droplet spectra correctly represents the feedbacks in the coupled system. The simulations capture the characteristic bimodal aerosol size spectrum resulting from cloud processing, with the first mode consisting of particles that did not participate as cloud condensation nuclei and the second mode, in the region of 0.08–0.12-μm radii, comprising the particles that were affected by processing. New information is revealed about the impact of the two main processing pathways and about the spatial distribution of the processed aerosol. One cycle of physical processing produced a relatively modest impact of 3%–5% on the processed particle mean radius of the order that was comparable to the impact of chemical processing, while continuous physical recycling produced a much larger impact as high as 30%–50%. A strong constraint on the chemical processing was found to be the initial chemistry input and the assumption of bulk chemical composition. Simple tests with a more slowly depleting primary oxidant (H2O2) and including the droplet chemical heterogeneity effect favor stronger sulfate production, by, respectively, the H2O2 and O3 oxidation reaction, and both show a larger impact on the processed particle mean radius of similar magnitude, 10%–20%. Spatially, the impact of processing is found initially in the downdraft regions below cloud and at later times at substantial distances downwind. It is shown that cloud processing can either enhance or suppress the number of activated drops in subsequent cycles.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.262
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2008
Admission routes1
Has abstractyes

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