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

variability driven by large-scale modes of variability ( Risbey et al. 2009 ). Land-use change (LUC) also affects the mean climate ( Pitman et al. 2009 ; Pielke et al. 2011 ; de Noblet-Ducoudré et al. 2012 ) and climate extremes (e.g., Pitman et al. 2012 ), particularly at regional scales ( Deo et al. 2009 ; Kala et al. 2011 ; Nair et al. 2011 ; Avila et al. 2012 ). The persistence of droughts and heat waves has also been linked to land processes, mostly through the soil moisture limitation of

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

that can respond to variations in temperature and moisture stress ( Lu et al. 2015 ). The Great Plains have been previously identified as one of three global maxima in land–atmosphere coupling ( Koster et al. 2004 ), as variations in soil moisture are positively correlated with precipitation in the region ( Koster et al. 2003 ). The Great Plains low-level jet (GPLLJ), a nocturnal southerly wind maximum, is the primary driver of summertime convective rainfall in the region ( Higgins et al. 1997

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Edward Armstrong, Paul Valdes, Jo House, and Joy Singarayer

1. Introduction Human-induced land-use change (LUC), such as the conversion of natural land cover to agriculture, transforms the land surface, altering its structure and influencing biogeophysical processes such as albedo, leaf area index (LAI), seasonality, surface roughness, and moisture fluxes. This has implications for the surface energy balance, altering shortwave radiation (SW) and the partitioning of latent and sensible heat (e.g., Brovkin et al. 2009 ; Bala et al. 2007 ; Boisier et

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

). Initial soil moisture and temperature conditions were also derived from NARR data. NARR provides similar soil conditions when compared with the North American Land Data Assimilation System (NLDAS) soil moisture products (not shown). We simulated each of the years from 1991 to 2000, beginning on 1200 UTC 15 June and running through the end of August (1200 UTC 31 August) to cover the peak of the monsoon season in Arizona. 2.5. Observational temperature data In section 3.4 , we correlate urban

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G. Strandberg and E. Kjellström

ice extent within the model domain. Surface albedo in RCA4 is a function of leaf area index (LAI). LAI is calculated as a function of the soil temperature with a lower limit set to 0.4 and upper limits to 2.3 (forest free) and 4.0 (deciduous forest). If deep soil moisture reduces to the wilting point, the LAI is set to its lower limit. LAI in coniferous forests is set constant to 4.0 regardless of soil moisture. For snow in forest-free areas, RCA4 has a prognostic albedo that varies between 0

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Yaqian He and Eungul Lee

biophysical properties over the land through modifying the surface albedo, partitioning the surface energy between sensible and latent heat fluxes, and altering the roughness of the land surface, which subsequently can influence the climate ( Foley et al. 2003b ; Mahmood et al. 2014 ; McPherson 2007 ; Dirmeyer et al. 2010 ; Pielke 2005 ; Wang et al. 2006 ). Van Noorden (2006) stated that more vegetation can transfer more moisture into the atmosphere by evapotranspiration, and the darker surface of

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Weiyue Zhang, Zhongfeng Xu, and Weidong Guo

studies have investigated the potential impact of LULCC on the troposphere. For example, a GCM simulation with a prescribed SST suggested that the impacts of terrestrial quantities (e.g., soil moisture and sensible and latent heat fluxes) resulting from LULCC over South Asia and Southeast Asia can rarely propagate into the atmosphere in a significant way in summer ( Findell et al. 2009 ). However, Narisma and Pitman (2003) investigated the influence of LULCC on regional climates using an RCM and

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W. L. Ellenburg, R. T. McNider, J. F. Cruise, and John R. Christy

different than those associated with the midwestern and south-central trends ( Portmann et al. 2009 ; Meehl et al. 2012 ; Rogers 2013 ). While no definitive cause has been identified for the persistence of this phenomenon, several theories have been advanced by climate scientists. These include “dimming” due to aerosols ( Saxena and Yu 1998 ; Portmann et al. 2009 ; Leibensperger et al. 2012 ); increased cloudiness, precipitation, and soil moisture variability ( Pan et al. 2004 ); variability of SST

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Jean-Sébastien Landry, Navin Ramankutty, and Lael Parrott

coexistence of two PFT; for the simulations performed in boreal or temperate locations, the grid-level differences in the major energy fluxes between the two approaches were all below 10%, yet the differences in the equilibrium values of carbon-related variables ( , biomass, and soil carbon) were mostly ~20%–40%. These major changes arose from the minor differences in the physical environment (mainly net radiation, temperature, and moisture) experienced by the PFT between the two modeling approaches. In

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

(Noah LSM). Noah LSM uses four soil layers (for temperature, water + ice, and water), one vegetation type in each grid cell without dynamic vegetation and carbon budget ( Jin et al. 2010 ), and predicts soil moisture and temperature in four layers. The ground heat budget in the Noah LSM is calculated using a diffusion equation for soil temperature, and the surface skin temperature is determined using a single, linearized surface energy balance equation ( Chen and Dudhia 2001 ). The radiation

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