Untangling microphysical impacts on moist convection applying a novel modeling methodology

Description

Formation and growth of cloud and precipitation particles (“cloud microphysics”) affects such macroscopic cloud field properties as the mean surface rainfall, cloud cover, and liquid/ice water paths. Traditional approaches to investigate the impact that rely on parallel simulations with different microphysical schemes are not reliable because of natural variability of a cloud field that is affected by the feedback between cloud microphysics and dynamics. We developed a novel modeling methodology to assess the impact of cloud microphysics on cloud dynamics and on simulated macroscopic cloud field characteristics. The main idea is to use two sets of thermodynamic variables driven by two microphysical schemes (or by the same scheme with different parameters), with one set coupled to the dynamics and driving the simulation, and the other set piggybacking the simulation, that is, responding to the simulated flow but not affecting it. We will present application of this methodology to cloud field simulations of shallow and deep convection. We will show that the methodology allows assessing the impact of cloud microphysics on cloud field properties with unprecedented accuracy. By switching the sets (i.e., the set driving the simulation becomes the piggybacking one, and vice versa), the impact on cloud dynamics can be isolated from purely microphysical effects. We will show that the new methodology documents a rather insignificant impact of the assumed cloud droplet concentration on convective dynamics for the case of scattered unorganized deep convection. These results cast doubt on the dynamic basis of the deep-convection invigoration in polluted environments.