Introduction to CAUSES: Description of Weather and Climate Models and Their Near‐Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains.

Autor: Morcrette, C. J.1 cyril.morcrette@metoffice.gov.uk, Van Weverberg, K.1, Ma, H.‐Y.2, Ahlgrimm, M.3, Bazile, E.4, Berg, L. K.5, Cheng, A.6, Cheruy, F.7, Cole, J.8, Forbes, R.3, Gustafson, Jr, W. I.5, Huang, M.5, Lee, W.‐S.8, Liu, Y.5, Mellul, L.7, Merryfield, W. J.8, Qian, Y.5, Roehrig, R.4, Wang, Y.‐C.9, Xie, S.2
Zdroj: Journal of Geophysical Research. Atmospheres. Mar2018, Vol. 123 Issue 5, p2655-2683. 29p.
Abstrakt: Abstract: We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale. [ABSTRACT FROM AUTHOR]
Databáze: GreenFILE
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