Dataset Information

Worldwide data sets constrain the water vapor uptake coefficient in cloud formation.

ABSTRACT: Cloud droplet formation depends on the condensation of water vapor on ambient aerosols, the rate of which is strongly affected by the kinetics of water uptake as expressed by the condensation (or mass accommodation) coefficient, ?c. Estimates of ?c for droplet growth from activation of ambient particles vary considerably and represent a critical source of uncertainty in estimates of global cloud droplet distributions and the aerosol indirect forcing of climate. We present an analysis of 10 globally relevant data sets of cloud condensation nuclei to constrain the value of ?c for ambient aerosol. We find that rapid activation kinetics (?c > 0.1) is uniformly prevalent. This finding resolves a long-standing issue in cloud physics, as the uncertainty in water vapor accommodation on droplets is considerably less than previously thought.

SUBMITTER: Raatikainen T

PROVIDER: S-EPMC3593859 | biostudies-other | 2013 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Worldwide data sets constrain the water vapor uptake coefficient in cloud formation.

Raatikainen Tomi T Nenes Athanasios A Seinfeld John H JH Morales Ricardo R Moore Richard H RH Lathem Terry L TL Lance Sara S Padró Luz T LT Lin Jack J JJ Cerully Kate M KM Bougiatioti Aikaterini A Cozic Julie J Ruehl Christopher R CR Chuang Patrick Y PY Anderson Bruce E BE Flagan Richard C RC Jonsson Haflidi H Mihalopoulos Nikos N Smith James N JN

Proceedings of the National Academy of Sciences of the United States of America 20130219 10

Cloud droplet formation depends on the condensation of water vapor on ambient aerosols, the rate of which is strongly affected by the kinetics of water uptake as expressed by the condensation (or mass accommodation) coefficient, αc. Estimates of αc for droplet growth from activation of ambient particles vary considerably and represent a critical source of uncertainty in estimates of global cloud droplet distributions and the aerosol indirect forcing of climate. We present an analysis of 10 globa ...[more]

PMID: 23431189

Similar Datasets

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

Worldwide data sets constrain the water vapor uptake coefficient in cloud formation.

Publications

Worldwide data sets constrain the water vapor uptake coefficient in cloud formation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets