Warm rain collision coalescence has been persistently difficult to parameterize in bulk microphysics schemes. Here we use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that the biggest limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution with or without an explicit functional form, the simulation of cloud-to-rain conversion is substantially improved. We find that the use of an assumed double-mode gamma distribution and the choice of predicted distribution moments do somewhat influence the ability of AMP to simulate rain production, but much less than using a single liquid category compared to separate cloud and rain categories. Traditional two category configurations of AMP are always too slow in producing rain due to their struggle to capture the emergence of the rain mode. Single category configurations may produce rain either too slowly or too quickly, with too slow production more likely for initially narrow droplet size distributions. However, the average error magnitude is much smaller using a single category than two categories. Optimal moment combinations for the single category approach appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision-coalescence rates.

The amount of aerosol is a key parameter modulating the cloud microphysics and its subsequent long-term impact on climate through Twomey (cloud albedo) effect and Albrecht (lifetime) effect. As the relative dispersion (ε; standard deviation divided by mean) of the cloud droplet size distribution is an important factor in controlling the droplet collision rate and precipitation, along with its relevance in cloud modeling, it would be valuable to study the influence of aerosol concentration (Na) on ε. The impact on ε from Na can be observed from two aspects: intra-cloud and inter-cloud correlation. The intra-cloud correlation may reveal the cloud microphysical processes that are affected by the local variability of aerosol concentration, whereas inter-cloud correlation sheds light onto how different background aerosol concentration affects the macrophysics of clouds under different meteorological settings. Past studies that investigated from these two aspects have shown inconsistent correlation between Na and ε. Hence, the objective of this study is to identify whether and how the Na affects ε, observed from these two aspects. In this study, to ensure statistical significance, we choose five in-situ campaign data sets that were collected in vastly different places around the world: VOCALS (Chilean west coast), MASE (California), POST (California, sampling emphasized near the cloud top), GoMACCS (Texas, emphasized on highly polluted areas), and ORACLES (Central African west coast). Surprisingly, we observe that both intra-cloud and inter-cloud Na-ε correlations are largely nonexistent, whether within individual campaigns or across campaigns. We also found that the intra-cloud correlation is instead pronounced only between cloud droplet concentration (Nd) and relative dispersion, suggesting that the cloud microphysics related to Nd and ε is not strongly influenced by Na but by other cloud processes. This strong (negative) correlation could also help models better predicting relative dispersion from droplet concentration to construct a more close-to-reality droplet size distribution.