Journal
CLIMATE DYNAMICS
Volume 45, Issue 3-4, Pages 727-744Publisher
SPRINGER
DOI: 10.1007/s00382-014-2301-7
Keywords
Cumulus parameterization; Regional climate model; Extreme events; Diurnal cycle; Frequency distribution
Categories
Funding
- NOAA Education Partnership Program (EPP) COM Howard [631017]
- Climate Prediction Program for the Americas (CPPA) [NA08OAR4310575, NA08OAR4310875]
- East China Normal University [44505880/004]
Ask authors/readers for more resources
This study comprehensively evaluates the effects of twelve cumulus parameterization (CUP) schemes on simulations of 1993 and 2008 Central US summer floods using the regional climate-weather research and forecasting model. The CUP schemes have distinct skills in predicting the summer mean pattern, daily rainfall frequency and precipitation diurnal cycle. Most CUP schemes largely underestimate the magnitude of Central US floods, but three schemes including the ensemble cumulus parameterization (ECP), the Grell-3 ensemble cumulus parameterization (G3) and Zhang-McFarlane-Liang cumulus parameterization (ZML) show clear advantages over others in reproducing both floods location and amount. In particular, the ECP scheme with the moisture convergence closure over land and cloud-base vertical velocity closure over oceans not only reduces the wet biases in the G3 and ZML schemes along the US coastal oceans, but also accurately reproduces the Central US daily precipitation variation and frequency distribution. The Grell (GR) scheme shows superiority in reproducing the Central US nocturnal rainfall maxima, but others generally fail. This advantage of GR scheme is primarily due to its closure assumption in which the convection is determined by the tendency of large-scale instability. Future study will attempt to incorporate the large-scale tendency assumption as a trigger function in the ECP scheme to improve its prediction of Central US rainfall diurnal cycle.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available