RESUMO
There is little consensus among global climate models (CGMs) regarding the response of lightning flash rates to past and future climate change, largely due to graupel not being included in models. Here a two-moment prognostic graupel scheme was incorporated into the MIROC6 GCM and applied in three experiments involving pre-industrial aerosol, present-day, and future warming simulations. The new microphysics scheme performed well in reproducing global distributions of graupel, convective available potential energy, and lightning flash rate against satellite retrievals and reanalysis datasets. The global mean lightning rate increased by 7.1% from the pre-industrial period to the present day, which was attributed to increased graupel occurrence. The impact of future warming on lightning activity was more evident, with the rate increasing by 18.4[Formula: see text] through synergistic contributions of destabilization and increased graupel. In the Arctic, the lightning rate depends strongly on the seasonality of graupel, emphasizing the need to incorporate graupel into GCMs for more accurate climate prediction.
RESUMO
Aerosols affect climate by modifying cloud properties through their role as cloud condensation nuclei or ice nuclei, called aerosol-cloud interactions. In most global climate models (GCMs), the aerosol-cloud interactions are represented by empirical parameterisations, in which the mass of cloud liquid water (LWP) is assumed to increase monotonically with increasing aerosol loading. Recent satellite observations, however, have yielded contradictory results: LWP can decrease with increasing aerosol loading. This difference implies that GCMs overestimate the aerosol effect, but the reasons for the difference are not obvious. Here, we reproduce satellite-observed LWP responses using a global simulation with explicit representations of cloud microphysics, instead of the parameterisations. Our analyses reveal that the decrease in LWP originates from the response of evaporation and condensation processes to aerosol perturbations, which are not represented in GCMs. The explicit representation of cloud microphysics in global scale modelling reduces the uncertainty of climate prediction.