Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models - Tao, Cheng; Xie, Shaocheng; Ma, Hsi‐Yen; Bechtold, Peter; Cui, Zeyu; Vaillancourt, Paul A.; Van Weverberg, Kwinten; Wang, Yi‐Chi; Wong, May; Yang, Jing; Zhang, Guang J.; Choi, In‐Jin; et al. (Quarterly journal of the Royal Meteorological Society, 2024)
Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is 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‐locking issue in many models where precipitation often incorrectly 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 the 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 from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes. Key findings: (1) The model bias of too weak rainfall intensity of diurnal cycle of precipitation in climate simulations is improved in weather hindcasts over the central United States (2) Models with a revised convection trigger that allows air parcels to be lifted above the boundary layer show much better simulation of nocturnal rainfall which is often associated with the propagation of mesoscale systems. (3) Including convective memory in cumulus parameterizations helps to decrease the model bias of too frequent light‐to‐moderate rain but it also weakens the diurnal variability. The composite mean diurnal cycle of precipitation (mm·day−1) for (a) climate runs (June–August, 2011–2018) and (b) hindcast runs (June 1–July 15, 2015). Results from the five‐day hindcasts are averaged over Day 2 to Day 5 hindcast lead time. Domain‐mean precipitation from merged radar gauge products that is available from the ARM continuous forcing data set and the VARANAL for PECAN are used in (a) and (b) respectively.