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Haishan Chen, Bo Yu, Botao Zhou, Wanxin Zhang, and Jie Zhang

radiation effects and the feedback of land–atmosphere interaction on the warm amplification over arid/semiarid regions. In addition, various studies have explored the possible reasons for the abnormal warming of the Eurasian continent from aspects of changes in cloud amount ( Dai et al. 1997 , 1999 ; Tang and Leng 2012 ; Tang et al. 2012 ) and precipitation ( Dai et al. 1997 , 1999 ; Trenberth and Shea 2005 ). Dai et al. (1997 , 1999) pointed out that increased cloud amount can reduce solar

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Garry L. Schaefer, Michael H. Cosh, and Thomas J. Jackson

information on critical areas of the state that require monitoring. They guide the selection process to ensure that a site represents a predominant climate regime. Station locations are selected based on several criteria, including NRCS National Benchmark Soils, land ownership (federal, state, county, or university land), whether nonirrigated, whether agricultural in nature, and station security. The first stations to be installed were located in areas that were susceptible to drought. When installed

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Bart J. J. M. van den Hurk and Erik van Meijgaard

1. Introduction Land–atmosphere interaction is manifest at a wide range of spatial and temporal scales ( Van den Hurk and Blyth 2008 ). The planetary boundary layer is affected by the evaporation from the surface by changing the atmospheric humidity, which changes surface evaporation by a modified humidity gradient within a couple of hours. The lifting condensation level (LCL) is strongly coupled to the surface relative humidity and soil moisture ( Betts 2004 ). The ability of the atmosphere to

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Joshua K. Roundy and Eric F. Wood

interactions ( Koster et al. 2000 ). There has been a great deal of work over the last decade to quantify land–atmosphere interactions and feedbacks over a variety of scales that utilize observations and prediction models. Working groups as part of the Global Energy and Water Cycle Experiment (GEWEX) initiative have done much of this work. One such effort focuses on the local land–atmosphere coupling through diagnosing the interactions between the land surface and the planetary boundary layer for models

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Dara Entekhabi, Ignacio Rodriguez-Iturbe, and Rafael L. Bras

multiplicative-noise stochastic differential equation for the waterbalance dynamics of continental-type climates that includes land surface-atmosphere interaction.The solution to this differential equation exhibits a bimodal probability distribution function for soil moistureand precipitation. Extended periods of anomalous dry conditions (drought) or alternatively wet conditions(pluvial), with abrupt transitions between them, are present in the model. The statistics of persistent anomalousconditions are

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Toshihisa Matsui, Venkataraman Lakshmi, and Eric E. Small

-scale atmospheric circulation pattern; therefore, it may be difficult to find statistically significant relationships using only observed data ( Matsui et al. 2003 ). A limited-area regional climate model (RCM) can be used to physically study the importance of complex landatmosphere interactions ( Giorgi and Mearns 1999 ). The climate of the NAMS region is unique: land surface conditions, sea breezes, orographic lifting, and large-scale monsoon circulation may affect the rainfall pattern ( Adams and Comrie

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Gordon B. Bonan, David Pollard, and Starley L. Thompson

_~af area index was the most important parameter; stomatal resistances were onlyimportant on wet soils. Interactions among parameters increased the nonlinearity of land-atmOsphere energyexchange. When considered separately, six to ten values of each parameter greafiy reduced the deviation betweenthe two flux estimates. However, this approach became cumbersome when all four parameters varied independently. These analyses suggest that the debate over how to best parameterize the nonlinear effects of

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

1. Introduction The inherent coupled nature of earth’s energy and water cycles places significant importance on the proper representation and diagnosis of land–atmosphere (LA) interactions in hydrometeorological prediction models ( Entekhabi et al. 1999 ; Betts and Silva Dias 2010 ). Unfortunately, the disparate resolutions and complexities of the governing processes have made it difficult to quantify these interactions in models or observations ( Angevine 1999 ; Betts 2000 ; Cheng and

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L. R. Vargas Zeppetello, Étienne Tétreault-Pinard, D. S. Battisti, and M. B. Baker

shown in Figs. 2 and 3 are due to errors in the model representations of the connection between surface turbulent energy fluxes and soil moisture. In this paper, we take a first step toward addressing this hypothesis with the aid of our “toy model” of land–atmosphere interaction on monthly time scales, developed in sections 2 and 3 . For more details on model development, see Tétreault-Pinard (2013) . The model quantitatively links the variance in 2-m air temperature T and soil moisture m

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Binayak P. Mohanty and Jianting Zhu

-based lookup tables and/or point-scale pedotransfer functions to estimate soil hydraulic properties is one of the weakest links in current National Aeronautics and Space Administration (NASA) land surface modeling efforts (C. D. Peters-Lidard 2005, personal communication). From land–atmosphere interaction perspective with model grid/pixel scale ranging from hundreds of meters to several kilometers, soil hydraulic properties’ variability may include overlapping small to large (nested) spatial structures due

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