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Grabowski", "listPosition" : 4, "share" : 0.25, "first" : false, "last" : true, "familyName" : "Wojciech", "givenName" : "W. 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This is because modifying even a small element of the system physics typically leads to a different system evolution, and it is difficult to tell whether the difference comes from the physical impact or it merely represents a different flow realization. Piggybacking is a relatively simple and computationally efficient modelling methodology that allows separation of the two. The idea is to use two sets of thermodynamic variables (the temperature, water vapor, and all aerosol, cloud, and precipitation variables) in a single cloud simulation. The two sets differ in a specific element of the physics, such as aerosol properties, microphysics parameterization, large-scale forcing, environmental profiles, etc. One thermodynamic set is coupled to the dynamics and drives the simulated flow, and the other set piggybacks the flow, that is, thermodynamic variables are carried by the flow but they do not affect it. We have recently implemented the piggybacking methodology in Weather Research and Forecasting (WRF) model to study the impact of selected physical processes on the cloud dynamics and microphysics in simulations of convective cases (Sarkadi et al. 2022). This presentation will discuss results from the initial set of simulations applying bulk microphysical parameterizations targeting selected cases from the Convective Precipitation Experiment (COPE) over the south-western UK. COPE convective clouds were well observed, and data are available to compare the observed and simulated microphysical and dynamical characteristics of these clouds. The simulations explore impacts of (i) aerosol loading; (ii) secondary ice production (the Hallett-Mossop process) and (iii) terminal velocity of hydrometeors.\nSarkadi, N., L. Xue, W. W. Grabowski, Z. J. Lebo, H. Morrison, B. White, J. Fan, and J. Dudhia, and I. Geresdi, 2022: Microphysical piggybacking in the Weather Research and Forecasting model. 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