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Aiwen Lin, Hongji Zhu, Lunche Wang, Wei Gong, and Ling Zou

topographic factors, which will make the distribution of air temperature and precipitation more complex, especially in mountain areas. Thus, the cokriging geostatistical interpolation technique is used for the spatial interpolation of air temperature and precipitation in this study. By choosing the elevation of each meteorological station as the covariable and implementing repeated exploratory spatial data analysis, cross validation, error analysis, and parameter modification, the cokriging method will

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Jeff Chieppa, Austin Bush, and Chandana Mitra

studied differed in background climate, which is an important factor contributing to UHI ( Zhao et al. 2014 ). Smaller cities, in addition, may be subject to greater growth rates than are larger cities ( Rahman 2014 ). Therefore, more information on the relationship between temperature and urbanization in smaller cities will assist in developing adaptation and mitigation techniques ( Stone et al. 2010 ). The southeastern United States provides an ideal opportunity to utilize the LCZ system for the

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Martin-Pierre Lavigne, Alain N. Rousseau, Richard Turcotte, Anne-Marie Laroche, Jean-Pierre Fortin, and Jean-Pierre Villeneuve

-off characteristics. Agric. For. Meteorol 50 : (1–2), 125 – 138 . Monteith , J. L. 1965 . Evaporation and the environment, in The State and Movement of Water in Living Organisms: 19th Symposium of the Society for Experimental Biology, edited by G.E. Fogg and P.G. Kohn, Cambridge University Press, London, 205–234 . Nash , J. E. and J. V. Sutcliffe . 1970 . River flow forecasting through conceptual models. Part I—A discussion of principles. J. Hydrol 10 : (3), 282 – 290 . Nicolson , J. A

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Umarporn Charusombat and Dev Niyogi

/Great Lake region. The techniques and methodologies are indeed transferable to different regions and not study-domain specific. Our understanding of climatic change in Indiana continues to evolve; therefore, we recommend that the vulnerability of drought and the planning for drought be done on a frequent basis. In other words, rather than treating the assessment as a one-time exercise, it would be useful to reevaluate this every decade. For example, in the absence of the SPI, PHDI was not an appropriate

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Z. M. Subin, W. J. Riley, J. Jin, D. S. Christianson, M. S. Torn, and L. M. Kueppers

California-specific plant functional types (PFTs) within CLM3.5. We used a fine-resolution (20 km) regional climate model [Weather Research and Forecasting model version 3 (WRF3)–CLM3.5, which is described below] to evaluate the impact of vegetation change on the California regional climate. The use of several vegetation scenarios with both historical climate (HC) and future climate (FC) boundary conditions allowed us to separate the biogeophysical effects of local vegetation from the effects of large

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Peter A. Bieniek, Uma S. Bhatt, Donald A. Walker, Martha K. Raynolds, Josefino C. Comiso, Howard E. Epstein, Jorge E. Pinzon, Compton J. Tucker, Richard L. Thoman, Huy Tran, Nicole Mölders, Michael Steele, Jinlun Zhang, and Wendy Ermold

1982 to 2013. The area average sea ice concentration was calculated within a 100-km buffer of the coast of each tundra region. The AVHRR surface temperature data have been enhanced through more effective cloud masking techniques and calibration through the utilization of in situ surface temperature data ( Comiso 2003 ). Monthly AVHRR land surface temperatures served to calculate the SWI, which is the sum of May–September monthly average land surface temperatures greater than 0°C, while the weekly

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

-down perspectives. Both modeling techniques are affected by uncertainties that make direct comparisons challenging. Atmospheric inverse modeling uses measurements of atmospheric mixing ratios of a trace gas to constrain surface fluxes of the same gas to match those mixing ratio observations. This is achieved by linking the mixing ratios to the fluxes through a transport model ( Gerbig et al. 2003 ). While accounting for uncertainties associated with mixing ratio observations, the transport model, and the

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Rick Lader, John E. Walsh, Uma S. Bhatt, and Peter A. Bieniek

regional dynamically downscaled variables obtained using the Advanced Research core of the Weather Research and Forecasting Model (WRF; Skamarock et al. 2008 ) over the Alaska domain ( Figure 1a ). The model simulations were driven by ERA-Interim ( Dee et al. 2011 ) from 1981 to 2010; the Geophysical Fluid Dynamics Laboratory Climate Model version 3 (GFDL-CM3; Donner et al. 2011 ) from 1981 to 2100; and the National Center for Atmospheric Research Community Climate System Model, version 4 (CCSM4

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Bryan Pijanowski, Nathan Moore, Dasaraden Mauree, and Dev Niyogi

coded as 1) error and a false negative (FN; i.e., incorrectly placing a nonagriculture, or 0, coded cell) error. Correctly placing an agricultural cell or nonagricultural cell is referred to as true positive (TP) and true negative (TN). We used the percent positive correct (PPC) metric to determine the goodness of fit for the placement of rain-fed agricultural cells, To determine how location errors impacted the coarser 36-km grid spacing RAMS model, we employed the scalable window technique of

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D. M. Nover, J. W. Witt, J. B. Butcher, T. E. Johnson, and C. P. Weaver

Model (CRCM; http://www.ec.gc.ca/ccmac-cccma/default.asp?lang=En&n=4A642EDE-1 ); Regional Climate Model, version 3 (RCM3; http://users.ictp.it/~pubregcm/RegCM3/ ); Hadley Regional Model 3 (HRM3; http://precis.metoffice.com/ ); Weather Research and Forecasting Model, using the Grell convection scheme (WRFG; http://www.wrf-model.org/index.php ); Geophysical Fluid Dynamics Laboratory 50-km global atmospheric time slice (GFDLhires; http://www-pcmdi.llnl.gov/ipcc/model_documentation/GFDL-cm2.htm

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