A physically-based two-moment microphysics parameterization scheme for convective clouds developed by Song and Zhang (2011) has been implemented in the Zhang and McFarlane (1995, ZM) convection scheme of the NCAR CAM5 to improve the representation of convective clouds and their interaction with large-scale clouds and aerosols. It is shown that this scheme is able to represent the suppression of warm rain formation and the enhancement of freezing in convection when aerosol loading is increased. The interaction between ice-phase microphysics and cumulus thermodynamics was further parameterized in ZM convection scheme to incorporate convection invigoration mechanism in CAM5. The response of convective precipitation to various aerosol loadings is controlled by three mechanisms: warm rain suppression, convection invigoration, and climate feedback. Since cloud microphysics parameterizations are constrained by either limited observations, idealized conditions in the laboratory or theoretical assumptions, large uncertainties still exist in it. For instance, the autoconversion rates from different parameterizations of autoconversion from cloud droplet to rain may vary over three orders of magnitude. In addition, there are a set of tunable parameters in cloud microphysics scheme that are usually tuned for the better climatology. How do the uncertainties related to these parameters and processes in convective cloud microphysics influence the aerosol effects on global convective precipitation in the GCM? In this study we designed a set of sensitivity experiments in the NCAR CAM5 using the Song and Zhang convective microphysics scheme with different parameter values and different autocoversion schemes under various aerosol loadings. The uncertainty of aerosol effects on global convective precipitation in the NCAR CAM5 is systematically evaluated.