[1] Extreme precipitation is generally underestimated by current climate models relative to observations of present-day rainfall distributions. Possible causes of this systematic error include the convective parameterization in these models that have been designed to reproduce measurements of climatological mean precipitation. One possible approach to improve the interaction of subgrid-scale physical processes and large-scale climate is to replace the conventional convective parameterizations with a high-resolution cloud-system resolving model. A ''super-parameterized'' Community Atmosphere Model (SP-CAM) utilizing this approach is used in this study to investigate the distribution of extreme precipitation in the United States. Results show that SP-CAM better simulates the distributions of both light and intense precipitation compared to the standard version of CAM based upon conventional parameterizations. The improvements are mostly seen in regions dominated by convective precipitation, suggesting that super-parameterization provides a better representation of subgrid convective processes.

We analyze subdaily continental convective precipitation data relative to the Southeastern U.S. from gridded rain gauge measurements, conventional global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive, and a multiscale GCM. GCMs react too quickly to local convective instability and, therefore, overestimate the incidence of middle rainfall events and underestimate the incidence of no, little, and heavy rainfall events. Moreover, GCMs overestimate the persistence of heavy precipitation and underestimate the persistence of no and light precipitation. In general, GCMs with suppression mechanisms in the treatments of convective precipitation compare best with rain gauge derived data and should be trusted more than the others when assessing the risk from extreme precipitation events. The multiscale GCM has the best estimate of the diurnal cycle and a good estimate of heavy rainfall persistence.

[1] The global transport of the surface-emitted short-lived passive tracers radon and methyl iodide is simulated in a cloud-resolving Global Climate Model (GCM) for the first time and compared against simulations with a conventional GCM in which cloud processes are not resolved. Both models are operated in chemical transport mode in which the large scale flow is set to observationally derived dynamic and thermodynamic fields from a meteorological reanalysis. Simulated vertical profiles of tracers concentrations from both models are compared with profiles observed in situ. The comparisons suggest that the cloud-resolving GCM is, to a small degree, better than the conventional GCM in reproducing the vertical gradients and hence the convective entrainment and detrainment of passive tracers. Contrasting only simulated climatological maps of tracers concentrations from the two models, we find consistent and appreciable relative differences that create a quadrupole pattern in the vertical direction. Relative to the conventional GCM, the tracer concentrations from the cloud-resolving GCM results are depleted from the surface to 1 km and from 4 to 12 Km and enriched from 1 to 4 km and above 12 km. This might have important implications for climate and atmospheric chemistry simulations but require further investigations.