Środowiskowe Seminarium Fizyki Atmosfery

Formation and growth of cloud and precipitation particles (“cloud microphysics”) affect cloud dynamics and such macroscopic cloud field properties as the mean surface rainfall, cloud cover, and liquid/ice water paths. Traditional approaches to investigate the impacts rely on parallel simulations with different microphysical schemes or with different scheme parameters. Such methodologies are not reliable because of the natural variability of a cloud field that is affected by the feedback between cloud microphysics and dynamics. A novel modeling methodology, microphysical piggybacking, was developed to assess the impact of cloud microphysics on cloud dynamics and to separate purely microphysical effects from the impact on the dynamics. 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. I will discuss application of this methodology to cloud field simulations of deep convection focusing on the so-called convective invigoration in polluted environments. I 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. Applying single-moment and double-moment bulk microphysics, I will show that the methodology documents a rather small indirect aerosol impact on convective dynamics for the case of scattered unorganized deep convection, but a significant microphysical effect.