Impact of Cloud Microphysics Parameterization on Model Simulation of Chemistry-Aerosol-Cloud Interaction: a Case Study

Clouds play an important role in processing and cycling of atmospheric gaseous and aerosol tracers. Particularly, it is known that a large portion of the atmospheric particulate sulphate is produced in cloud via aqueous-phase oxidation. Recently there is also increasing evidence that reactions involving organic compounds in the aqueous phase may be an important pathway in the formation of secondary organic aerosols in the atmosphere. On the other hand, cloud processed aerosols, often physically (size spectrum) and chemically (composition) modified, can behave differently in activating to form cloud droplets and/or ice crystals in subsequent cloud cycles, which can in turn impact precipitation generation, cloud life time, and cloud optical properties. Past studies have shown that the modeling of cloud processing is highly dependent on model simulated clouds (e.g., spatial and temporal distribution, cloud liquid water content), which can vary significantly from different cloud microphysical parameterizations employed in the model.

In this study, first, the impact of different cloud microphysical parameterizations on model simulated cloud processing of urban and industrial plumes is investigated by using an Environment Canada off-line air quality modeling system AURAMS. The investigation focuses on a study case based on airborne measurements from two flights during the ICARTT field campaign in summer 2004. Two different explicit microphysics schemes were used, namely the Kong-Yau scheme and the Milbrandt-Yau scheme. The sensitivity of modeled clouds and its subsequent impact on modeled aerosol mass and size distribution will be discussed in conjunction with comparisons to available aircraft observations.

Furthermore, under phase II of the Air Quality Model Evaluation International Initiative (AQMEII), the feedback from the chemically speciated and size distributed aerosols to cloud microphysics and dynamics (via the Milbrandt-Yau double moment scheme) is explored using the Environment Canada's on-line air quality prediction model GEM-MACH. The feedback is introduced through aerosol activation (or droplet nucleation). The coupling methodology and preliminary results will also be presented in this talk.