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Average Cloud Droplet Size and Composition: Good Assumptions for Predicting Oxidants in the Atmospheric Aqueous Phase?

ABSTRACT: Chemical models that describe the atmospheric multiphase (gas/aqueous) system often include detailed kinetic and mechanistic schemes describing chemical reactions in both phases. The present study explores the importance of properties including the chemical composition of droplet populations, such as pH value and iron present in only a few droplets, as well as droplet size and their distribution. It is found that the assumption of evenly distributed iron in all cloud droplets leads to an underestimate by up to 1 order of magnitude of OH concentrations in the aqueous phase, whereas the predicted iron(II)/iron(total) ratio is overestimated by up to a factor of 2. While the sulfate mass formed in cloud droplets is not largely affected by any of the assumptions, the predicted secondary organic aerosol mass varies by an order of magnitude. This sensitivity study reveals that multiphase chemistry model studies should focus not only on chemical mechanism development but also on careful considerations of droplet properties to comprehensively describe the atmospheric multiphase chemical system.

SUBMITTER: Ervens B

PROVIDER: S-EPMC9662182 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Average Cloud Droplet Size and Composition: Good Assumptions for Predicting Oxidants in the Atmospheric Aqueous Phase?

Ervens Barbara B

The journal of physical chemistry. A 20221101 44

Chemical models that describe the atmospheric multiphase (gas/aqueous) system often include detailed kinetic and mechanistic schemes describing chemical reactions in both phases. The present study explores the importance of properties including the chemical composition of droplet populations, such as pH value and iron present in only a few droplets, as well as droplet size and their distribution. It is found that the assumption of evenly distributed iron in all cloud droplets leads to an underes ...[more]

PMID: 36318926

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Project description:Aerosol-cloud interactions are the largest source of uncertainty in quantifying anthropogenic radiative forcing. The large uncertainty is, in part, due to the difficulty of predicting cloud microphysical parameters, such as the cloud droplet number concentration (N d). Even though rigorous first-principle approaches exist to calculate N d, the cloud and aerosol research community also relies on empirical approaches such as relating N d to aerosol mass concentration. Here we analyze relationships between N d and cloud water chemical composition, in addition to the effect of environmental factors on the degree of the relationships. Warm, marine, stratocumulus clouds off the California coast were sampled throughout four summer campaigns between 2011 and 2016. A total of 385 cloud water samples were collected and analyzed for 80 chemical species. Single- and multispecies log-log linear regressions were performed to predict N d using chemical composition. Single-species regressions reveal that the species that best predicts N d is total sulfate ( Radj2=0.40 ). Multispecies regressions reveal that adding more species does not necessarily produce a better model, as six or more species yield regressions that are statistically insignificant. A commonality among the multispecies regressions that produce the highest correlation with N d was that most included sulfate (either total or non-sea-salt), an ocean emissions tracer (such as sodium), and an organic tracer (such as oxalate). Binning the data according to turbulence, smoke influence, and in-cloud height allowed for examination of the effect of these environmental factors on the composition-N d correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between N d with both total sulfate and sodium increased at higher turbulence conditions, consistent with turbulence promoting the mixing between ocean surface and cloud base. Considering the influence of smoke significantly improved the correlation with N d for two biomass burning tracer species in the study region, specifically oxalate and iron. When binning by in-cloud height, non-sea-salt sulfate and sodium correlated best with N d at cloud top, whereas iron and oxalate correlated best with N d at cloud base.

Dataset Information

Average Cloud Droplet Size and Composition: Good Assumptions for Predicting Oxidants in the Atmospheric Aqueous Phase?

Publications

Average Cloud Droplet Size and Composition: Good Assumptions for Predicting Oxidants in the Atmospheric Aqueous Phase?

Similar Datasets

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