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Agustín Robles-Morua, Enrique R. Vivoni, and Alex S. Mayer

in the NAM region ( Brito-Castillo et al. 2003 ; Gochis et al. 2006 ). However, understanding the spatiotemporal variability of streamflow remains an elusive challenge for several reasons. First, most regional river basins, particularly within Mexico, remain ungauged (or poorly gauged) because of inadequate rainfall, weather, and/or streamflow records. Sparse data limit the ability to observe hydrologic variables of interest at the appropriate spatiotemporal scales for streamflow forecasting

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Joseph A. Santanello Jr., Christa D. Peters-Lidard, Aaron Kennedy, and Sujay V. Kumar

Forecasting Model (ARW-WRF; Michalakes et al. 2001 ) is a state-of-the-art mesoscale numerical weather prediction system. Derived from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Anthes and Warner 1978 ), ARW-WRF has been designated as the community model for atmospheric research and operational prediction and is ideal for high-resolution (e.g., 1 km) regional simulations on the order of 1–10 days. ARW-WRF has an Eulerian mass

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Xubin Zeng, Zhuo Wang, and Aihui Wang

temperature diurnal cycle over arid regions by addressing these three questions. Two community land models will be used: the Noah land model ( Ek et al. 2003 ; Chen and Dudhia 2001 ) as used in the National Centers for Environmental Prediction (NCEP) regional and global weather forecasting models as well as in the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model, and the Community Land Model (CLM3.5) ( Oleson et al. 2008 ) as used in the NCAR Earth System Model

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Keith J. Harding and Peter K. Snyder

minimize model edge effects. The goal of this study is to explore the impact that irrigation has on the hydrologic cycle using a high-resolution coupled land–atmosphere model. Simulations using the Weather Research and Forecasting Model (WRF; Skamarock et al. 2008 ) were performed both with and without irrigation for a suite of years for different precipitation regimes. This includes El Niño–Southern Oscillation (ENSO) years that have a marked influence on Great Plains precipitation ( Twine et al

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Keith J. Harding and Peter K. Snyder

. (2006) using data from the North American Regional Reanalysis (NARR) to show that evapotranspired water from an area over the Ogallala Aquifer falls as precipitation primarily over the Great Plains and upper Midwest. This study uses a backward trajectory method based on the approach of Brubaker et al. (2001) with data generated from simulations of the Weather Research and Forecasting Model (WRF) for an array of antecedent soil moisture conditions. Performing a backward trajectory analysis on

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Craig R. Ferguson, Eric F. Wood, and Raghuveer K. Vinukollu

/or variability of coupling may play a large role in the predictability of regional climate (i.e., temperature and precipitation). For example, potential rainfall forecast skill from land may be high where coupling is strong and invariable and low where coupling is either consistently weak or strong but highly variable. In the past, there have been many attempts to evaluate coupling with models (e.g., Beljaars et al. 1996 ; Betts 2004 ; Cook et al. 2006 ; Delworth and Manabe 1989 ; Dirmeyer 2011

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Ruth E. Comer and Martin J. Best

land–atmosphere coupling signals were largely consistent, the strength varied considerably across the models, with a few displaying very weak coupling. The weakest were the Australian Bureau of Meteorology Research Centre’s Atmospheric Model (labeled BMRC), the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model coupled to the Oregon State University (OSU) land surface scheme (LSS), and the Met Office Hadley Centre’s Atmospheric Model, version 3 (HadAM3). Guo et

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