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Wanli Wu, Amanda H. Lynch, Sheldon Drobot, James Maslanik, A. David McGuire, and Ute Herzfeld

to the parameterization of cloud physics. Nevertheless, the studies of Murphy ( Murphy 1999 ), Wu et al. ( Wu et al. 2005 ), and Liang et al. ( Liang et al. 2006 ) along with this present study have each recognized the limitations of an RCM in downscaling regional or local climate (e.g., biases that might be associated with forcing data used at the lateral boundary). In light of biases in the regional climate modeling, one computationally intense approach to reduce biases is to employ the four

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Sheldon Drobot, James Maslanik, Ute Christina Herzfeld, Charles Fowler, and Wanli Wu

to the statistical differences observed in the time series analysis? Analysis methods used in this study include analysis of variance (ANOVA) with post hoc means comparisons to determine significant differences in the datasets, anomaly correlations to examine seasonal cycles, and similarity maps to highlight spatial regions where datasets differ. 2. Datasets Each of the datasets analyzed here is used to validate or force hydrological models in the WALE project. The NCEP1 and ERA-40 are data

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A. D. McGuire, J. E. Walsh, J. S. Kimball, J. S. Clein, S. E. Euskirchen, S. Drobot, U. C. Herzfeld, J. Maslanik, R. B. Lammers, M. A. Rawlins, C. J. Vorosmarty, T. S. Rupp, W. Wu, and M. Calef

driven with either the NCEP2 or WM climate (temperature and precipitation) data. The use of NCEP1 data resulted in basinwide runoff estimates that were approximately twice the observed estimates of runoff. Thus, the accurate simulation of regional water balance is limited by biases in the forcing data. Uncertainties in simulating regional ecosystem dynamics Several studies in WALE examined uncertainties in simulating carbon dynamics of the region ( Kimball et al. 2006 ; Kimball et al. 2007 ; Clein

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J. S. Kimball, K. C. McDonald, and M. Zhao

daily LAI and FPAR information, to compute GPP and NPP. Monthly LAI and FPAR data were obtained from the NOAA AVHRR Pathfinder dataset, which has an approximate 16-km spatial resolution and extends over the entire domain from 1982 to 2000 ( James and Kalluri 1994 ; Myneni et al. 1997b ). The LAI and FPAR data are based on a monthly maximum value compositing of AVHRR spectral reflectance data to mitigate cloud cover, smoke, and other atmospheric aerosol contamination effects ( Myneni et al. 1997b

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