Processing of cloud condensation nuclei by collision-coalescence in a mesoscale model

The Naval Research Laboratory's Coupled Ocean‐Atmosphere Mesoscale Prediction System (COAMPS) is employed to explore the relative importance of source, sink, and transport processes in producing an accurate forecast of the aerosol‐cloud‐drizzle system. Cloud processing, defined to be the reduction of cloud condensation nuclei (CCN) via collision‐coalescence, is not uniquely related to total particle concentration, a behavior which stems from the roughly inverse dependence on cloud droplet concentration between autoconversion and accretion depletion terms. Instead, the behavior of cloud processing in COAMPS suggests relationships (scalings) based on cloud base drizzle rate (R) and cloud droplet concentration (Nc). Cloud processing is found to be correlated with drizzle, a relationship that can be represented as a power law for drizzle rates less than 0.6 mm d−1. A scaling for cloud processing based on the product of Nc and R is accurate over a wider range of drizzle rates. Results from large eddy simulation with size‐resolved microphysical processes demonstrate reasonable agreement with COAMPS and the two parameter scaling. Entrainment plays an important role in strongly modulating the mean marine boundary layer (MBL) concentration, both increasing and decreasing CCN, depending upon the entrainment velocity we and the difference between MBL and free tropospheric CCN concentrations. The importance of entrainment suggests that transport processes, especially in the vertical, play a fundamental role in the overall MBL CCN balance. In situ sources rates of CCN, taken to represent heterogeneous chemical processes and sea salt flux of submicron size particles from the ocean surface, must be unrealistically large in order to be of the same magnitude as cloud processing. Because of the prevailing importance of cloud processing and entrainment over timescales of a typical mesoscale forecast, we argue that incorporating accurate vertical aerosol profiles into the model update cycles, either from remote sensing or from global chemistry models, is more important than highly constrained local CCN source rates.