Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at ARM's SGP Site

Klein, S., Lawrence Livermore National Laboratory

Radiation Processes

Warm Boundary Layer Processes

Zhang C, S Xie, S Klein, H Ma, S Tang, K Van Weverberg, C Morcrette, and J Petch. 2018. "CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site." Journal of Geophysical Research: Atmospheres, 123(6), doi:10.1002/2017JD027200.


Department of Energy scientists, along with collaborators from the U.K. Met Office, organized an international multi-model intercomparison project, named CAUSES (Clouds Above the United States and Errors at the Surface), to identify possible causes for the large warm surface air temperature bias seen in many weather forecast and climate model simulations over the mid-latitude continent.


As one of a series of papers to address several relevant contributors to surface air temperature bias from the project, this study quantifies and better explains the biases in various surface and atmospheric fields associated with the warm bias in the CMIP simulations. We achieve this by analyzing the surface energy budget, water budget, and large-scale circulation with ARM detailed field observations and other supplemental observations. This paper also provides perspectives as to relevance of the hindcast biases studied in the other CAUSES paper to the climate bias. The assessed ARM measurements are useful for routinely benchmarking model performance.


By using the measurements collected from the U.S. DOE ARM's SGP sites and other available sources, this study shows that the systematic warm season bias is characterized by an overestimation of T2m and underestimation of surface humidity, precipitation, and precipitable water. Accompanying the warm bias is an overestimation of absorbed solar radiation at the surface, which is due to a combination of insufficient cloud reflection and clear-sky shortwave absorption by water vapor and an underestimation in surface albedo. The bias in cloud is shown to contribute most to the radiation bias. The surface layer soil moisture impacts T2m through its control on evaporative fraction (EF). The error in EF is another important contributor to T2m. Similar sources of error are found in hindcast from other CAUSES studies. Biases in meridional wind velocity associated with the low-level jet and the 500-hPa vertical velocity may also relate to T2m bias through their control on the surface energy and water budget.