Aerosols that serve as cloud condensation nuclei (CCN) and ice nuclei (IN) have the potential to profoundly influence precipitation processes. Furthermore, changes in orographic precipitation have broad implications for reservoir storage and flood risks. As part of the CalWater field campaign (2009-2011), the variability and associated impacts of different aerosol sources on precipitation were investigated in the California Sierra Nevada using an aerosol time-of-flight mass spectrometer for precipitation chemistry, S-band profiling radar for precipitation classification, remote sensing measurements of cloud properties, and surface meteorological measurements. The composition of insoluble residues in precipitation samples collected at a surface site contained mostly local biomass burning and longrange- transported dust and biological particles (2009), local sources of biomass burning and pollution (2010), and longrange transport (2011). Although differences in the sources of insoluble residues were observed from year to year, the most consistent source of dust and biological residues were associated with storms consisting of deep convective cloud systems with significant quantities of precipitation initiated in the ice phase. Further, biological residues were dominant (up to 40 %) during storms with relatively warm cloud temperatures (up to -15 degrees C), supporting the important role bioparticles can play as ice nucleating particles. On the other hand, lower percentages of residues from local biomass burning and pollution were observed over the three winter seasons (on average 31 and 9 %, respectively). When precipitation quantities were relatively low, these insoluble residues most likely served as CCN, forming smaller more numerous cloud droplets at the base of shallow cloud systems, and resulting in less efficient riming processes. Ultimately, the goal is to use such observations to improve the mechanistic linkages between aerosol sources and precipitation processes to produce more accurate predictive weather forecast models and improve water resource management.

http://dx.doi.org/10.5194/acp-15-6535-2015

Journal Article