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Record W2945038143 · doi:10.5194/acp-19-12397-2019

Photomineralization mechanism changes the ability of dissolved organic matter to activate cloud droplets and to nucleate ice crystals

2019· article· en· W2945038143 on OpenAlexfundno aff
Nadine Borduas‐Dedekind, Rachele Ossola, Robert O. David, Lin S. Boynton, Vera Weichlinger, Zamin A. Kanji, Kristopher McNeill

Bibliographic record

VenueAtmospheric chemistry and physics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEidgenössische Technische Hochschule ZürichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsCloud condensation nucleiIce crystalsIce nucleusChemistryDissolved organic carbonSupercoolingNucleationOrganic matterFormic acidAtmosphere (unit)AerosolPhotochemistryEnvironmental chemistryMeteorologyOrganic chemistry

Abstract

fetched live from OpenAlex

An organic aerosol particle has a lifetime of approximately 1 week in the atmosphere during which it will be exposed to sunlight. However, the effect of photochemistry on the propensity of organic matter to participate in the initial cloud-forming steps is difficult to predict. In this study, we quantify on a molecular scale the effect of photochemical exposure of naturally occurring dissolved organic matter (DOM) and of a fulvic acid standard on its cloud condensation nuclei (CCN) and ice nucleation (IN) activity. We find that photochemical processing, equivalent to 4.6 d in the atmosphere, of DOM increases its ability to form cloud droplets by up to a factor of 2.5 but decreases its ability to form ice crystals at a loss rate of −0.04 ∘ CT50 h −1 of sunlight at ground level. In other words, the ice nucleation activity of photooxidized DOM can require up to 4 ∘ C colder temperatures for 50 % of the droplets to activate as ice crystals under immersion freezing conditions. This temperature change could impact the ratio of ice to water droplets within a mixed-phase cloud by delaying the onset of glaciation and by increasing the supercooled liquid fraction of the cloud, thereby modifying the radiative properties and the lifetime of the cloud. Concurrently, a photomineralization mechanism was quantified by monitoring the loss of organic carbon and the simultaneous production of organic acids, such as formic, acetic, oxalic and pyruvic acids, CO and CO 2 . This mechanism explains and predicts the observed increase in CCN and decrease in IN efficiencies. Indeed, we show that photochemical processing can be a dominant atmospheric ageing process, impacting CCN and IN efficiencies and concentrations. Photomineralization can thus alter the aerosol–cloud radiative effects of organic matter by modifying the supercooled-liquid-water-to-ice-crystal ratio in mixed-phase clouds with implications for cloud lifetime, precipitation patterns and the hydrological cycle. Highlights. During atmospheric transport, dissolved organic matter (DOM) within aqueous aerosols undergoes photochemistry. We find that photochemical processing of DOM increases its ability to form cloud droplets but decreases its ability to form ice crystals over a simulated 4.6 d in the atmosphere. A photomineralization mechanism involving the loss of organic carbon and the production of organic acids, CO and CO 2 explains the observed changes and affects the liquid-water-to-ice ratio in 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 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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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 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

Citations63
Published2019
Admission routes1
Has abstractyes

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