摘要：Abstract. In a chaotic system, like moist convection, it isdifficult to separate the impact of a physical process from effects ofnatural variability. This is because modifying even a small element of thesystem physics typically leads to a different system evolution and it isdifficult to tell whether the difference comes from the physical impact orit merely represents a different flow realization. This paper discusses arelatively simple and computationally efficient modelling methodology thatallows separation of the two. The methodology is referred to as thepiggybacking or the master-slave approach. The idea is to use two sets ofthermodynamic variables (the temperature, water vapor, and all aerosol,cloud, and precipitation variables) in a single cloud simulation. The twosets differ in a specific element of the physics, such as aerosolproperties, microphysics parameterization, large-scale forcing,environmental profiles, etc. One thermodynamic set is coupled to thedynamics and drives the simulated flow, and the other set piggybacks theflow, that is, thermodynamic variables are carried by the flow but they donot affect it. By switching the two sets (i.e. the set driving thesimulation becomes the piggybacking one, and vice versa), the impact on thecloud dynamics can be evaluated. This paper provides details of the methodand reviews results of its application to such problems as the postulateddeep convection invigoration in polluted environments, the impact of changesin environmental profiles (e.g., due to climate change) on convectivedynamics, and the role of cloud-layer heterogeneities for shallow convectivecloud field evolution. Prospects for applying piggybacking technique toother areas of atmospheric simulation (e.g., weather prediction orgeoengineering) are also mentioned.
