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Xia Zhang, Shu Fen Sun, and Yongkang Xue

convergence is reached even when drastic phase change occurs. b. Boundary conditions The upper boundary condition for Eq. (1) on a bare soil surface is given by the vertical moisture flux q s θ (m s −1 ) and is defined as where E is evaporation rate (m s −1 ), U p is rainfall rate (m s −1 ) on the soil surface, and R s is surface runoff. If there is snow falling, the snow will create a snow cover over bare soil and a snow cover model, such as the snow–atmosphere–soil transfer model (SAST

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J. Li, X. Gao, and S. Sorooshian

/02 but severely underestimated the snow-rich winter of 2000/01. Figure 5a shows that the model overestimated the winter precipitation for both the snow-deficient and snow-rich winters. Therefore, the MM5–Noah model shows a severe deficiency in that it systematically underestimates the snow cover on the land surface. The Noah land surface model ( Chen and Dudhia 2001 ) simplifies snowpack on the land surface as a single layer of ice particles without liquid water. The physical properties of snowpack

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Richard G. Lawford, John Roads, Dennis P. Lettenmaier, and Phillip Arkin

moisture, vegetation) become more important. Through the GEWEX Continental-scale International Project (GCIP) and its follow-on project, GAPP, a number of regional land surface and atmospheric products have been developed to support the forecast systems at NOAA/National Centers for Environmental Prediction (NCEP). In particular, the snow cover fields derived from Advanced Very High Resolution Radiometer (AVHRR) data and the solar radiation fields derived from Geostationary Operational Environmental

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Ana M. B. Nunes and John O. Roads

focused on improving the precipitation, we knew that our many assumptions about the vertical distribution could lead to a change in cloud cover, which could, in turn, affect the surface radiation fluxes. These surface radiation changes, which we believe were also beneficial to the model simulation, will be discussed in section 3c . b. Model The version of the Experimental Climate Prediction Center (ECPC)-Regional Spectral Model (RSM; Juang and Kanamitsu 1994 ) used here is a primitive equation model

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Xubin Zeng and Aihui Wang

1. Introduction Vegetation can be characterized by its type, horizontal coverage, and vertical distribution. While vegetation types are considered by almost all land models, the treatment of vegetation horizontal coverage and vertical thickness is quite different in different land models. For instance, an annually maximum fractional vegetation cover (FVC) along with seasonally variable leaf area index (LAI) is used in the Community Land Model version 3 (CLM3; Oleson et al. 2004 ), while

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Kevin E. Trenberth, Lesley Smith, Taotao Qian, Aiguo Dai, and John Fasullo

temperature, snow cover, and runoff ( Bonan et al. 2002 ; Dickinson et al. 2006 ). In the stand-alone integration used here, the CLM3 was forced with observed monthly precipitation and other fields blended with high-frequency weather information from the National Centers for Environmental Prediction (NCEP)–NCAR reanalysis ( Qian et al. 2006 ). Values are reported on a T42 grid (∼2.8°), on a monthly basis from 1948 to 2004. The precipitation dataset is a blend of PREC/L and GPCP to ensure complete

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Song Yang, S-H. Yoo, R. Yang, K. E. Mitchell, H. van den Dool, and R. W. Higgins

connection between summer monsoons and the preceding snow cover in Asia as well as in North America ( Hahn and Shukla 1976 ; Barnett et al. 1989 ; Meehl 1994 ; Yang et al. 1996 ; Gutzler and Preston 1997 ; Higgins et al. 1998 ; Yang and Lau 1998 ; Hu and Feng 2002 ). Even in the summer season itself, the monthly persistence of surface temperature cannot be well explained without soil–atmosphere interactions ( Huang and Van den Dool 1993 ; Huang et al. 1996 ). Previous studies have also

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Jinwon Kim and Hyun-Suk Kang

; Soong and Kim 1996 ; Kim 1997 ; Chung et al. 1998 ; Neiman et al. 2002 ; Kim and Lee 2003 ; Grubišić et al. 2005 ). Extreme elevation changes in the Sierra Nevada further complicate the regional water cycle through their influence on the local snow budget and cloud formation ( Kim 2001 ; Kim et al. 2006 ). Among these orographic effects, the low-level wind disturbances induced by these mountain ranges play the most fundamental role in determining the precipitation distribution in California

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