PNNL

Abstract

Diurnal precipitation is one fundamental mode of climate variability that climate models need to accurately simulate but continue having difficulties. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges’ DCP project is evaluated over tropical and midlatitude lands. Common model biases such as the excessive precipitation over the tropics, the too frequent light-to-moderate rain, and the missing of propagating convection in the central U.S. still exist. Over the central U.S., the issues of both too weak rainfall intensity and the incorrect timing of DCP in climate runs is reasonably well improved in their hindcast runs with initial conditions from Numerical Weather Prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large-scale environment in convective triggering processes helps resolve the phase-lock issue in many models where precipitation often peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light-to-moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low-resolution climate models in DCP. The hierarchy modeling framework used in this study is useful in identifying the missing physics in models and testing new development of model convective processes over different convective regimes.