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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 19 Mar 2020

Submitted as: research article | 19 Mar 2020

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This preprint is currently under review for the journal ACP.

On the Relationship Between Cloud Water Composition and Cloud Droplet Number Concentration

Alexander B. MacDonald1, Ali Hossein Mardi1, Hossein Dadashazar1, Mojtaba Azadi Aghdam1, Ewan Crosbie2,3, Haflidi H. Jonsson4, Richard C. Flagan5, John H. Seinfeld5, and Armin Sorooshian1,6 Alexander B. MacDonald et al.
  • 1Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
  • 2Science Systems and Applications, Inc., Hampton, VA, USA
  • 3NASA Langley Research Center, Hampton, VA, USA
  • 4Naval Postgraduate School, Monterey, CA, USA
  • 5Department of Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
  • 6Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA

Abstract. 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 (Nd). Even though rigorous first-principle approaches exist to calculate Nd, the cloud and aerosol research community also relies on empirical approaches such as relating Nd to aerosol mass concentration. Here we analyze relationships between Nd 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 79 chemical species. Single- and multi-species log-log linear regressions were performed to predict Nd using chemical composition. Single-species regressions reveal that the species that best predicts Nd is total sulfate (R2adj = 0.40). Multi-species 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 multi-species regressions that produce the highest correlation with Nd 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 examination of the effect of these environmental factors on the composition-Nd correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between Nd 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 Nd 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 Nd at cloud top, whereas iron and oxalate correlate best with Nd at cloud base.

Alexander B. MacDonald et al.

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Status: open (until 14 May 2020)
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Alexander B. MacDonald et al.

Data sets

A Multi-Year Data Set on Aerosol-Cloud-Precipitation-Meteorology Interactions for Marine Stratocumulus Clouds A. Sorooshian, A. B. MacDonald, H. Dadashazar, K. H. Bates, M. M. Coggon, J. S. Craven, E. Crosbie, S. P. Hersey, N. Hodas, J. J. Lin, A. Negrón Marty, L. C. Maudlin, A. R. Metcalf, S. M. Murphy, L. T. Padró, G. Prabhakar, T. A. Rissman, T. Shingler, V. Varutbangkul, Z. Wang, R. K. Woods, P. Y. Chuang, A. Nenes, H. H. Jonsson, R. C. Flagan, and J. H. Seinfeld

Alexander B. MacDonald et al.


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Publications Copernicus
Short summary
Understanding how humans affect Earth's climate requires understanding of how particles in the air affect the number concentration of droplets in a cloud (Nd). We use the air-equivalent mass concentration of different chemical species contained in cloud water to predict Nd. In this study we found that the prediction of Nd is: (1) best described by total sulfate, (2) improved when considering up to five species, and (3) dependent on factors like turbulence, smoke presence, and in-cloud height.
Understanding how humans affect Earth's climate requires understanding of how particles in the...