Abstract/Summary

We have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model, SP-CAM. The new configuration, called the multiple-instance superparameterized-CAM, or MP-CAM, uses the average heating and drying rates from 10 independent two-dimensional cloud-permitting models (CPMs) in each grid column of the global model, instead of a single CPM. The 10 CPMs start from slightly different initial conditions, and simulate alternative realizations of the convective cloud systems. By analyzing the ensemble of possible realizations, we can study the predictability of the cloud systems, and identify the weather regimes and physical mechanisms associated with chaotic convection. We explore alternative methods for quantifying the predictability of precipitation. Our results show that unpredictable precipitation occurs when the simulated atmospheric state is close to critical points as defined by Peters and Neelin (2006, https://doi.org/10.1038/nphys314). The predictability of precipitation is also influenced by the convective available potential energy (CAPE) and the degree of mesoscale organization. It is strongly controlled by the large-scale circulation. A companion paper compares the global atmospheric circulations simulated by SP-CAM and MP-CAM.