Climate Effects of Cloud Modified CCN-Cloud Interactions

Cloud condensation nuclei (CCN) play an important role in the climate system through the indirect aerosol effect (IAE). IAE is one of the least understood aspects of the climate system as many cloud processes are complicated. Many studies of aerosol-cloud interaction involve CCN interaction with cloud droplet concentrations (Nc), cloud microphysics, and radiative properties. However, fewer studies investigate how cloud processes modify CCN. Upon evaporation from non-precipitating clouds, CCN distributions develop bimodal shaped distributions (Hoppel et al. 1986). Activated CCN participate in cloud processing that is either chemical: aqueous oxidation; or physical: Brownian scavenging, collision and coalescence. Chemical processing does not change CCN concentration (NCCN) but reduces critical supersaturations (Sc; larger size) (Feingold and Kreidenweis, 2000) while physical processing reduces NCCN and Sc. These processes create the minima in the bimodal CCN distributions (Hudson et al., 2015). Updraft velocity (W) and NCCN are major factors on how these modified CCN distributions affect clouds. Panel a shows two nearby CCN distributions in the MArine Stratus/stratocumulus Experiment (MASE), which have similar concentrations, but the bimodal one (red) has been modified by cloud processing. In a simplified cloud droplet model, the modified CCN then produces higher Nc (panel b) and smaller droplet mean diameters (MD; panel c) when compared to the unmodified CCN (black) for W lower than 50 cm/s. The better CCN (lower Sc) increase competition among droplets reducing MD and droplet distribution spread (σ) which acts to reduce drizzle. Competition is created by limited available condensate due to lower S created by the low W (<50 cm/s) typical of stratus. The increased Nc of the modified CCN in stratus then increases IAE in the climate system. At higher W (>50 cm/s) typical of cumuli, Ncis reduced and MD is increased from the modified CCN distribution (panels b & c). Here, CCN cloud processing increases MD and σ leading to increased drizzle. Improved climate prediction requires a better understanding of these cyclical CCN-cloud interactions. Feingold and Kreidenweis, 2000, JGRA, 105(D19), 24351-24361. Hoppel, Frick, and Larson, 1986, GRL, 13(1), 125-128. Hudson, Noble and Tabor, 2015, JGRA, 120.