Diurnal cycle of precipitation over the tropics and central U.S.



Xie, Shaocheng — Lawrence Livermore National Laboratory
Tao, Cheng — Lawrence Livermore National Laboratory

Area of research:

General Circulation and Single Column Models/Parameterizations

Journal Reference:

Tao C, S Xie, H Ma, P Bechtold, Z Cui, P Vaillancourt, K Van Weverberg, Y Wang, M Wong, J Yang, G Zhang, I Choi, S Tang, J Wei, W Wu, M Zhang, J Neelin, and X Zeng. 2024. "Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models." Quarterly Journal of the Royal Meteorological Society, 150(759), 10.1002/qj.4629.


U.S. Department of Energy scientists and collaborators at Lawrence Livermore National Laboratory evaluated the performance of a number of general circulation models (GCMs) in simulating the diurnal cycle of precipitation (DCP) in both climate (atmosphere-only) mode and short-term (5-day) weather hindcast mode. Particularly, this study emphasized the central U.S. and central Amazon where long-term Atmospheric Radiation Measurement user facility observations and enhanced field campaign operations are available.


Most models had problems capturing the observed DCP, which is a persistent and well-known problem in GCMs. By applying an hierarchical modeling framework that includes both climate simulation and short-term weather hindcasts, this study provided additional insights into model errors including model physics, basic-state biases, missing convective-environmental interactions, the impact of model resolution, etc.


Models with a revised convection trigger that allows air parcels to be lifted above the boundary layer exhibit much better simulation of nocturnal rainfall that is often associated with the propagation of mesoscale systems, consistent with previous studies. The model bias of “too weak” rainfall intensity of DCP in climate simulations is improved in weather hindcasts over the central U.S. This study also shows that including convective memory in cumulus parameterizations reduces the model bias of too frequent light-to-moderate rain decreases but also weakens the diurnal variability.