The daily rainfall cycle is the most simply forced pattern of convection in the atmosphere but current climate models have trouble representing it. The new superparameterization* climate simulation technique is investigated as a method for improving daily rainfall, both in order to study the mechanisms supporting it and with an eye towards reducing uncertainties in climate prediction.

Satellite validation shows that the global statistics of the diurnal rainfall cycle improve with super-parameterization overall.  Regionally, the superparameterization approach at first fails in the Central US, where the non-local physics of long lived traveling mesoscale convective systems are important. But reconfiguring the way superparameterization is implemented shows that this form of propagating warm season nocturnal convection can in fact be captured. Understanding how this is possible in spite of severe idealizations in the embedded interior models adds to an ongoing debate about the role of fast-manifold vs. large-scale controls on this important regional form of diurnal convection.

* "Superparameterized" climate models are a next generation climate modeling technology that uses thousands of embedded cloud resolving models (CRMs) to handle sub-grid cloud physics instead of conventional statistical techniques ("parameterizations"). This study focuses on the SuperParameterized Community Atmosphere Model (SP-CAM).