CAUSES: On the role of surface energy budget errors to the warm surface air temperature error over the Central U.S.

 

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
Frederique Cheruy — Laboratory of Dynamic Meteorology
Jason N. S. Cole — Canadian Centre for Climate Modelling and Analysis
Richard M Forbes — European Centre for Medium-Range Weather Forecasts
William I. Gustafson — Pacific Northwest National Laboratory
Maoyi Huang — National Oceanic and Atmospheric Administration (NOAA)
Ying Liu — Pacific Northwest National Laboratory
William Merryfield — Canadian Centre for Climate Modelling and Analysis
Yun Qian — Pacific Northwest National Laboratory
Romain Roehrig — National Center for Meteorological Research
Yi-Chi Wang — Research Center for Environmental Change Academia Sinica T

Category

Boundary layer structure, including land-atmosphere interactions and turbulence

Description

Many weather forecast and climate models simulate warm surface air temperature (T2m) biases over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) sites. The present study examines the contributions of surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central U.S. and SGP. Nevertheless, biases in the net shortwave and downward longwave fluxes, as well as surface evaporative fraction (EF) are contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias. (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)