Modelling aerosol–cloud–meteorology interaction: A case study with a fully coupled air quality model (GEM-MACH). (August 2015) - Gong, W.; Makar, P.A.; Zhang, J.; Milbrandt, J.; Gravel, S.; Hayden, K.L.; Macdonald, A.M.; Leaitch, W.R.
Abstract: A fully coupled on-line air quality forecast model, GEM-MACH, was used to study aerosol–cloud interactions for a case of an urban-industrial plume impacting stratocumulus. The aerosol effect on the cloud microphysics was achieved by the use of parameterization of cloud droplet nucleation predicted from the on-line size- and composition-resolved aerosols and coupled with a double-moment cloud microphysics parameterization. The model simulations with and without the on-line aerosol effect on cloud microphysics were compared and evaluated against in-situ aerosol and cloud observations from ICARTT 2004. Inclusion of the on-line aerosol interaction with cloud resulted in an increase in modelled cloud amount and cloud liquid water content (LWC) due to increased cloud droplet number concentration (Nd ), a decrease in cloud droplet size and a reduction in warm precipitation. The modelled LWC and Nd agreed more closely with the observations when the on-line aerosol was allowed to affect the cloud than when aerosol effects on cloud were not explicitly simulated. The increased cloud amount due to the aerosol effects reduced the modelled downward shortwave radiative flux and air temperature at the surface, contributing to a decrease in ozone over the region of enhanced cloud and an increase in particle sulphate from an increased capacity for aqueous-phase production. Aerosol activation is shown to have a significant influence on the cloud microphysics and cloud processing of trace gases and aerosols. The importance of reasonable parameterization of cloud updraft speed is demonstrated. Highlights: A case of processing of urban/industrial plume by stratocumulus clouds was studied. Simulations with and without aerosol feedback to cloud microphysics were compared. In-situ microphysics and chemistry data were used to evaluate the model results. The feedback led to increased cloud amount, higher Nd, better comparison with obs. The feedback also impacted modelled chemical tracers due to the increased cloudiness.