Microphysical piggybacking applied previously in shallow convection simulations is employed to highlight microphysical impacts on deep convection. The main idea is to apply two sets of thermodynamic variables, one coupled to the dynamics and driving the simulation and the second one piggybacking the simulation: that is, responding to the simulated flow but not affecting it. The two sets can be driven either by different microphysical schemes or by the same scheme with different scheme parameters. For illustration, two single-moment bulk microphysical schemes are implemented, one with a simple representation of ice processes and the second one with a more comprehensive approach. Each scheme is applied assuming contrasting cloud droplet concentrations, pristine versus polluted, with simulations mimicking dynamical effects of pollution on deep convection. The modeling setup follows the case of daytime convective development over land based on observations during the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Microphysical piggybacking with small ensembles of simulations allows for separating dynamical and microphysical impacts on deep convection with high confidence and enables extracting small differences in the surface precipitation, cloud cover, and liquid and ice water paths with unprecedented accuracy. It also shows that the cloud buoyancy above the freezing level is only weakly affected by contrasting cloud droplet concentrations. The latter casts doubt on the convective invigoration hypothesis for the case of unorganized deep convection considered in this study, at least when investigated with a single-moment microphysical scheme.
