The piggybacking approach can be used to separate microphysical and dynamical responses to changes in various model fields or parameterizations. In this project, we are using the approach to pinpoint the processes with the largest sensitivity in cloud microphysics schemes. This is done by using a combination of difference microphysics schemes and the same scheme with various processes changed or adjusted. The goal is to determine the processes in which small uncertainties can have large impacts on the modeled fields, which is critical for improving numerical weather prediction models. Current efforts have highlighted the role of ice nucleation parameterizations in the models. More work is needed to further elucidate the roles of other processes and quantify the sensitivity.