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Xiaoyan Jiang, Sara A. Rauscher, Todd D. Ringler, David M. Lawrence, A. Park Williams, Craig D. Allen, Allison L. Steiner, D. Michael Cai, and Nate G. McDowell

projected vegetation, and climate characteristics from the ensemble simulations. Finally, discussion and conclusions are presented in section 4 . 2. Methods a. Model description The model utilized here is the atmosphere and land components of the global CESM, which was previously known as the Community Climate System Model (CCSM). To allow for interactions between climate and vegetation, the model was run in a configuration in which the atmosphere model (Community Atmosphere Model) and CLM4.0 are

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Sebastian Bathiany, Martin Claussen, and Victor Brovkin

ice dynamics, terrestrial hydrology, and a terrestrial and marine carbon cycle. The horizontal resolution is approximately 1.25° latitude × 1.875° longitude. The interaction between land surface and atmosphere is calculated by the Met Office Surface Exchange Scheme, version 2 (MOSES2) ( Essery et al. 2003 ). Leaf-level photosynthesis is based on Collatz’s models for C3 ( Collatz et al. 1991 ) and C4 plants ( Collatz et al. 1992 ). Vegetation distribution and composition are calculated by the Top

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Tirtha Banerjee, Frederik De Roo, and Matthias Mauder

1. Introduction It is a standard practice in modeling land surface–atmosphere interaction that momentum or scalar fluxes can be parameterized by relating them to the mean velocity gradient or scalar concentration gradient by means of a turbulent diffusion coefficient, called gradient-diffusion parameterization or K theory ( Raupach and Thom 1981 ; Katul et al. 2013 ). The K theory has enjoyed a high degree of popularity over the years, especially because of the ease of usage. It has been

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Weile Wang, Bruce T. Anderson, Dara Entekhabi, Dong Huang, Robert K. Kaufmann, Christopher Potter, and Ranga B. Myneni

similar magnitude range as the observed values ( Figure 9 , dark solid line), while the spectra of the random inputs are essentially “flat” ( Figure 9 , gray dash line). When the model uses the full value of θ , the output precipitation has larger magnitude differences between high and low frequencies (not shown). The overestimated red spectra of the model simulations may be due to the fact that the strength of land–atmosphere interactions in the observed system has monthly/seasonal variability ( W1

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D. Lauwaet, K. De Ridder, and N. P. M. van Lipzig

Shukla 1993 ; Zeng et al. 1999 ). Therefore, there has been a large effort to develop highly complex land surface models to represent more realistically the physical processes at the atmosphere–surface interface. A problem faced by these land surface models in the Sahel is the lack of measurements of soil and vegetation parameters in the area. Bad estimations for these parameters can lead to unrealistic simulated fluxes with feedback to other meteorological parameters. In this study, our focus is on

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C. Kendra Gotangco Castillo, Samuel Levis, and Peter Thornton

1. Introduction The Community Land Model, version 4.0 (CLM4) was released as a component of the Community Climate System Model, version 4.0 (CCSM4), which was updated to become the Community Earth System Model, version 1.0 (CESM1) with the option to run with interactive atmosphere–ocean–land carbon cycles. CLM4 contains several notable improvements over previous releases (e.g., Lawrence et al. 2011 ; Kluzek 2011 ; Oleson et al. 2010 ). Earlier versions of the CLM (since CLM2) included

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Taikan Oki and Y. C. Sud

1. Introduction General circulation models (GCMs) are now becoming sufficiently realistic in simulating the rainfall, biosphere–atmosphere interactions, and land hydrology. This has been accomplished because of modern state-of-the-art land surface models are able to generate realistic evaporation and precipitation, provided the soil moisture initialization and rainfall forcing (inputs) are realistic (e.g., Oki et al., 1997 ). Chen et al. ( Chen et al.,1997 ) estimated (Project for

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Hua Yuan, Robert E. Dickinson, Yongjiu Dai, Muhammad J. Shaikh, Liming Zhou, Wei Shangguan, and Duoying Ji

elements of the 3D model are modularized. A one-layer model was constructed by combining single bush models taking into account effects of their shadows, intercanopy interactions, and low sun angles. The one-layer model was used to build up the three-layer model by considering the shadow overlapping between layers. The 3D model so developed is simple and effective and is in good agreement with numerical simulations. We implemented the 3D model into the land component of a climate model, CLM4.0, and

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Mengmeng Li, Zhichun Mao, Yu Song, Mingxu Liu, and Xin Huang

(b) locations of the meteorological stations (red dots) in the inner domain. The shaded contour represents topography height (m), the black labels represent the major provinces, and the blue labels represent the names of the observational sites. In this study, the main physical-parameterization schemes contain the Noah land surface scheme to describe the detailed thermodynamic and hydrological processes of land–atmosphere interactions ( Chen et al. 2006 ; Ek et al. 2003 ), the Lin microphysics

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Douglas A. Miller and Richard A. White

1. Climate and hydrology model requirements for soil information Over the past several decades the climate and hydrology modeling communities have been developing increasingly sophisticated parameterizations of the interaction between the land surface and the atmosphere in so-called soil–vegetation–atmosphere transfer schemes (SVATS). A major requirement of these process descriptions is an understanding of the surface and subsurface nature of the soil environment. The soil controls the downward

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