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Pedro Sequera, Jorge E. González, Kyle McDonald, Steve LaDochy, and Daniel Comarazamy

experiment was the Weather Research and Forecasting (WRF, version 3.5) Model, a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational applications ( Skamarock et al. 2008 ). The simulations were conducted with three horizontal nested grids with grid spacing of 16, 4, and 1 km, respectively ( Figure 5 ). The coarser-resolution grid covers a good portion of the northern Pacific and western United States, and the finer-resolution grid covers the

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A L. Hirsch, A. J. Pitman, J. Kala, R. Lorenz, and M. G. Donat

al. (2014b) to undertake the first assessment of coupling strength for the austral summer [December–February (DJF)] over Australia. By applying the GLACE-1 methodology in the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), multiple simulations were conducted to understand the uncertainty associated with different model physics. Hirsch et al. (2014b) also considered the role of interannual variability of land–atmosphere coupling strength building on Northern Hemisphere

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Keith J. Harding, Tracy E. Twine, and Yaqiong Lu

contiguous United States; however, the impacts on precipitation were not investigated. Here, we expand on the work of Lu et al. (2015) and for the first time examine how dynamic crop growth impacts the simulated effect of irrigation on warm-season precipitation and its drivers. We used high-resolution (6.33-km model grid cell resolution) simulations of a version of the Weather Research and Forecasting (WRF) Model that is coupled to the Community Land Model version 4.0 with dynamic crop growth (WRF-CLM4

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Yaqian He and Eungul Lee

understanding of the influencing factors for the rainfall variability over the Sahel could improve the predictive skill in rainfall forecasting, which will benefit the local people. Figure 1. The land-use and land-cover types of Africa from the MODIS land-cover dataset in 2001. The Sahel region is outlined in red. Sahel rainfall is known to be strongly influenced by sea surface temperature (SST), both globally and in oceans adjacent to the African continent ( Martin and Thorncroft 2014 ; Mohino et al. 2011

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Andres Schmidt, Beverly E. Law, Mathias Göckede, Chad Hanson, Zhenlin Yang, and Stephen Conley

NEE over the derived tower footprints ( Lin et al. 2003 ; Nehrkorn et al. 2010 ). The 4D meteorological fields used in STILT were calculated with the Weather Research and Forecast Model (WRF) in the Advanced Research WRF (WRF-ARW, version 3.7 ( Michalakes et al. 2001 ). For the WRF boundary conditions, we used the NCEP Final (FNL) operational global analysis data with 1° horizontal resolution and a 6-hourly temporal resolution, respectively ( Kalnay et al. 1990 ) for 27 vertical levels. The NCEP

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Zhao Yang, Francina Dominguez, Hoshin Gupta, Xubin Zeng, and Laura Norman

model and concluded that increased urban heat island effect can decrease the time required for rainwater formation, while moving the horizontal location closer to the heating center. Craig and Bornstein (2002) found that the UHI can induce convergence and convection. Using the Weather Research and Forecast (WRF) Model, Lin et al. (2011) found that the UHI can affect the location of thunderstorms and precipitation in northern Taiwan. Veerbeek et al. (2011) found that extreme rainfall over the

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G. Strandberg and E. Kjellström

. Lett. , 36 , L14814 , . 10.1029/2009GL039076 Pongratz , J. , T. Raddatz , C. H. Reick , M. Esch , and M. Claussen , 2009 : Radiative forcing from anthropogenic land cover change since A.D. 800 . Geophys. Res. Lett. , 36 , L02709 , . 10.1029/2008GL036394 Quintanar , A. , and R. Mahmood , 2012 : Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid

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Soumaya Belmecheri, Flurin Babst, Amy R. Hudson, Julio Betancourt, and Valerie Trouet

Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR, 1948–present) and/or 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; 1958–present; e.g., Archer and Caldeira 2008 ; Archer and Caldeira 2009 ; Bals-Elsholz et al. 2001 ; Ellis and Barton 2012 ; Gallego et al. 2005 ; Kuang et al. 2014 ; Peña-Ortiz et al. 2013 ; Strong and Davis 2005 ; Woollings et al. 2010 ). From this reanalysis dataset, wind speed values at 200, 300, or

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