Representation of precipitating cloud systems and their effect on the Earth's radiation and water budget remains a key source of uncertainty in weather and climate models. Because of computational limitations, a large spectrum of cloud processes cannot be resolved, and their effect on grid scale thermodynamic state, dynamics, and radiation must be parameterized. Empirical parameters used to approximate cloud scale processes are typically tuned during model development and remain fixed in both space and time. Recent studies have shown that uncertainty associated with poorly known model physics parameters is both large and not well understood. This is in part due to nonlinearity in the relationship between changes to parameters and changes to cloud properties. It is also due to the complex interaction between parameterizations (e.g., cloud, convection, boundary layer, and radiation), and to the sensitivity of parameterizations to model configuration (e.g., grid spacing, number of vertical levels, frequency of coupling between physics and dynamics, and time step). This talk will highlight recent advances in model physics uncertainty quantification research and propose next steps for exploring variability in physics parameters in NASA Goddard's cloud resolving and general circulation models.