# On the relationship between cloud water composition and cloud droplet number concentration

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 (inline-formulaNd). Even though rigorous first-principle approaches exist to calculate inline-formulaNd, the cloud and aerosol research community also relies on empirical approaches such as relating inline-formulaNd to aerosol mass concentration. Here we analyze relationships between inline-formulaNd 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 inline-formulaNd using chemical composition. Single-species regressions reveal that the species that best predicts inline-formulaNd is total sulfate (inline-formula $M7inlinescrollmathml{R}_{\mathrm{normal adj}}^{normal 2}=normal 0.40$ 55pt18ptsvg-formulamathimg9763185c48b2e0e01a51f9671e4c15dc acp-20-7645-2020-ie00001.svg55pt18ptacp-20-7645-2020-ie00001.png ). 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 inline-formulaNd 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–inline-formulaNd correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between inline-formulaNd 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 inline-formulaNd 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 inline-formulaNd at cloud top, whereas iron and oxalate correlated best with inline-formulaNd at cloud base.

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MacDonald, Alexander B. / Hossein Mardi, Ali / Dadashazar, Hossein / et al: On the relationship between cloud water composition and cloud droplet number concentration. 2020. Copernicus Publications.

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