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Y. Xia, A. J. Pitman, H. V. Gupta, M. Leplastrier, A. Henderson-Sellers, and L. A. Bastidas

1. Introduction Developments in the parameterization of land surface processes over the last two decades have tended to add complexity to the land surface schemes or models used to represent the surface energy and water balance in climate and weather prediction simulations. The intercomparison of existing land surface models has led to the identification of large differences in the partitioning of available water between runoff and evaporation and in the partitioning of available energy between

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Phil P. Harris, Sonja S. Folwell, Belen Gallego-Elvira, José Rodríguez, Sean Milton, and Christopher M. Taylor

; Mueller et al. 2011 ) and were found to contain significant mutual differences, limiting their use for model evaluation. Moreover, the hybrid models can also share approaches with the GCM land surface schemes that their outputs are used to assess. In a related effort to derive an evaporation benchmark from these estimates, Mueller et al. (2013) produced synthesis datasets based on combinations of EO-derived data, offline land surface models, and reanalysis outputs. Alternatively, we can use the well

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Megan S. Mallard, Tanya L. Spero, and Stephany M. Taylor

1. Introduction Accurate representation of air–surface exchanges of heat, moisture, and momentum is critical for simulating regional climate and meteorological conditions. In the WRF Model, which is commonly used for both regional climate and meteorological simulations, many of the physical processes that affect air–surface exchanges are a function of land use or land cover (hereinafter LU), which is a prescribed field in WRF. Within each WRF grid cell, LU affects radiative properties

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Eleanor Blyth, John Gash, Amanda Lloyd, Matthew Pryor, Graham P. Weedon, and Jim Shuttleworth

time scale. Such characteristics are attractive to land surface model developers, and Stöckli et al. (2008) , for example, used FLUXNET data to inform development of the Community Land Model (CLM) land surface model. The earth science community is therefore interested in the potential use of FLUXNET observations as benchmark data to evaluate the performance of the land surface models used in GCMs ( Stöckli et al. 2008 ). This paper describes a pathfinder study whose goal is to determine if and how

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Andrew D. Jones, William D. Collins, James Edmonds, Margaret S. Torn, Anthony Janetos, Katherine V. Calvin, Allison Thomson, Louise P. Chini, Jiafu Mao, Xiaoying Shi, Peter Thornton, George C. Hurtt, and Marshall Wise

1. Introduction Land-use changes (LUCs) exert multiple influences on climate through direct biophysical effects on surface energy and water budgets as well as through changes in net greenhouse gas fluxes ( Bonan 2008 ; Foley et al. 2005 ). Climate change mitigation activities to date, however, have focused almost exclusively on the greenhouse gas consequences of land-use change ( Marland et al. 2003 ). None of the proposed regulations or programs, including the United Nations (UN

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Kevin P. Gallo, Timothy W. Owen, David R. Easterling, and Paul F. Jamason

1. Introduction The influence of land use/land cover (LULC) on several meteorological variables has been well documented (e.g., Landsberg 1981 ; Kukla et al. 1986 ; Karl et al. 1988 ; Changnon 1992 ; Gallo et al. 1993 ; Gallo et al. 1996 ). Gallo et al. (1996) suggested that the LULC associated with climate observation stations needed to be periodically monitored. Gallo et al. (1996) speculated that the transition of the LULC from predominantly rural to a more urban setting can have

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Menglin S. Jin

rural temperature for different types of land cover. Simply put, the magnitude of UHI 2m depends on the locations of the city and rural observations, complicating the assessment of the spatiotemporal attributes of UHI and making it impossible to compare the UHI of one city to another. The UHI has also been detected using relatively high-resolution satellite observations of the land surface skin temperature T skin ( Voogt and Oke 1997 ; Jin et al. 2005 , 2011 ), which is generally reported as

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Daniel E. Comarazamy, Jorge E. González, Jeffrey C. Luvall, Douglas L. Rickman, and Robert D. Bornstein

1. Introduction Anthropogenic land-cover and land-use (LCLU) changes have profound climate and environmental impacts. One of the most extreme cases of LCLU change is urbanization, with its clearest indicator as the urban–rural thermal phenomenon known as the urban heat island (UHI). The UHI is defined as a dome of high temperatures observed over urban centers, as compared to the relatively cooler rural surroundings ( Landsberg 1981 ; Oke 1987 ). One factor that leads to UHI formation is the

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Yu-Heng Tseng, Shou-Hung Chien, Jiming Jin, and Norman L. Miller

components are introduced briefly in the following subsections. Fig . 1. The structure of the I-RMS. a. Weather Research and Forecasting Model 3.1 with Community Land Model 3.0 (WRF-CLM) WRF-CLM is designed as a regional weather and climate model ( Skamarock et al. 2005 ; Skamarock and Klemp 2008 ) coupled with the Community Land Model ( Oleson et al. 2004 ), a land surface model used in the Community Climate System Model (CCSM). The CLM includes a sophisticated subgrid representation, advanced snow

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Bart Nijssen, Reiner Schnur, and Dennis P. Lettenmaier

° for the period 1979–88 using a version of the Simplified Simple Biosphere Model. Observed average monthly precipitation was disaggregated into daily data by scaling the monthly time series by the frequency of observed 1979 daily precipitation events. Both Schemm et al. and Liston et al. treated all precipitation as rainfall and consequently did not take snow into account. The past two decades have seen a rapid growth in the development of land surface schemes that model the interaction between the

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