CAUSES (Clouds Above the United States and Errors at the Surface): Error contribution from precipitation and surface energy budget

 

Authors

Hsi-Yen Ma — Lawrence Livermore National Laboratory
Stephen Klein — Lawrence Livermore National Laboratory
Shaocheng Xie — Lawrence Livermore National Laboratory
Chengzhu Zhang — Lawrence Livermore National Laboratory
Shuaiqi Tang — Pacific Northwest National Laboratory
Qi Tang — Lawrence Livermore National Laboratory
Cyril Julien Morcrette — Met Office - UK
Kwinten Van Weverberg — Met Office - UK
Jon Petch — UK Meteorological Office
Maike Ahlgrimm — Deutscher Wetterdienst
Larry Berg — Pacific Northwest National Laboratory
Jason N. S. Cole — Canadian Centre for Climate Modelling and Analysis
Richard M Forbes — European Centre for Medium-Range Weather Forecasts
Maoyi Huang — National Oceanic and Atmospheric Administration (NOAA)
William I. Gustafson — Pacific Northwest National Laboratory
Ying Liu — Pacific Northwest National Laboratory
William Merryfield — Canadian Centre for Climate Modelling and Analysis
Yun Qian — Pacific Northwest National Laboratory
Yi-Chi Wang — Research Center for Environmental Change Academia Sinica T

Category

General topics

Description

The CAUSES (Clouds Above the United States and Errors at the Surface) is a joint GASS/RGCM/ASR model intercomparison project with an observational focus (data from the U.S. DOE ARM SGP site and other observations). The goal of this project is to evaluate the role of clouds, radiation, and precipitation processes in contributing to the surface air temperature bias in the region of the central U.S., which is seen in several weather and climate models. In this project, we use a short-term hindcast approach and examine the error growth due to cloud-associated processes while the large-scale state remains close to observations. The study period is from April 1 to August 31, 2011, which also covers the entire Mid-latitude Continental Convective Clouds Experiment (MC3E) campaign that provides very frequent radiosondes (8 per day) and many extensive cloud and precipitation radar observations. Our analysis indicates that the warm surface air temperature bias in the mean diurnal cycle of the whole study period is very robust across all the participating models over the ARM SGP site. During the spring season (April-May), the daytime warm bias in most models is mostly associated with excessive net surface shortwave flux resulting from insufficient deep convective cloud fraction or too optically thin clouds. The nighttime warm bias is mostly associated with the excessive downwelling longwave flux warming. During the summer season (June-August), bias contribution from precipitation bias becomes important. The insufficient seasonal accumulated precipitation from the propagating convective systems originated from the Rockies contributes to lower soil moisture. Such condition drives the land surface to a dry state whereby radiative input can only be balanced by sensible heat loss through an increased surface air temperature. (This study is funded by the RGCM and ASR programs of the U.S. Department of Energy as part of the Cloud-Associated Parameterizations Testbed. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-688818)