On the Relationship Between the Mass Concentration of Water-Soluble Species in Cloud Droplets 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 (N_d). This study analyzed relationships between N_d and cloud water chemical composition, in addition to the effect environmental factors have on the degree of the relationships. Warm, marine, stratocumulus clouds off the coast of California 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. Multi-variable log-log linear regressions were performed to predict N_d using chemical composition. The chemical species that best predicted N_d was total sulfate (r_adj² = 0.40). Despite their abundance, species that were not well correlated with N_d were markers for sea salt and dust such as sodium (r_adj² = 0.19) and iron (r_adj² = 0.05), respectively. Previous studies have suggested that the prediction of N_d can be improved by considering multiple chemical species as predictors. We found that increasing the number of predictors increases r_adj². The highest statistically significant value of r_adj² = 0.56 was obtained with five predictors. After five predictors, the regressions do not improve in predictive skill. Factors such as the presence of smoke, turbulence, and in-cloud height dependency were explored in terms of how they affected the regression models.