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Liming Zhou, Yuhong Tian, Haishan Chen, Yongjiu Dai, and Ronald A. Harris

energy currently amounts to ~3% of U.S. electricity generation ( AWEA 2012 ; U.S. DOE 2012 ) and could supply up to 20% of the total U.S. electricity by 2030 ( U.S. DOE 2008 ). To generate this substantial amount of energy, wind farms would have to install a huge number of wind turbines over a continental-scale area ( Wang and Prinn 2010 ; Fiedler and Bukovsky 2011 ). Wind power depends on weather and climate, and wind farms, if large enough, might also modify the weather and climate, at least in

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Chang Liao and Qianlai Zhuang

grasslands, the maximum correlation between NDVI and water deficits is found to be around 1 month during the growing season, which means the time lag might be 1 month ( Wang et al. 2001 ). For woodlands, the time lag is approximately 2 months in summer. The time lag for agricultural ecosystems is from 3 to 6 months ( Rouault and Richard 2003 ). In summary, different PFTs have different time lags in response to droughts. However, despite the rich knowledge of time-lag effects, current ecosystem models

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Marc L. Fischer, David P. Billesbach, Joseph A. Berry, William J. Riley, and Margaret S. Torn

1. Introduction Land surface exchanges of energy, water, and CO 2 are the dominant factors affecting near-surface air temperatures, boundary layer CO 2 concentrations, boundary layer development and structure, cloud development, and precipitation. In the case of energy budgets and surface climate, previous work has shown that spatial complexity and temporal variations in land cover generate variations in climate at the regional scale ( Song et al. 1997 ; Doran et al. 1998 ; Cooley et al

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In-Young Yeo, Steven I. Gordon, and Jean-Michel Guldmann

hydrological impacts of land-use changes under such a scenario. This ad hoc scenario method is widely used to determine the best management plans or to prioritize environmentally sensitive areas for preservation. Another limitation of the current approach is related to the shortcomings of hydrological models. Although fully distributed physical models are available, runoff simulation modeling based on the SCS curve number method is the most widely used tool because of its simplicity and good accuracy A

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Marcello Gugliotta, Jonathan G. Fairman Jr., David M. Schultz, and Stephen S. Flint

plausible reconstructions of past geographies (i.e., land–sea distribution, ocean bathymetry, continental topography, land cover, and vegetation), atmospheric composition, solar input, and Earth’s orbital parameters as boundary conditions. For time-slice simulations, the model is run to an equilibrium state to determine the climate achieved under these specific boundary conditions and other forcings. Paleoclimate modeling studies have become an increasingly important and popular way to understand Earth

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Robert Paul d'Entremont and Gary B. Gustafson

, plans call for the SERCAA model to replace the current U.S. Air Force (USAF) operational cloud analysis model, the Real-Time Nephanalysis (RTNEPH), in the summer of 2002. In the coming years, archived cloud analyses from this model will offer an alternative global cloud record for climate studies. As such, it is important to document and describe the retrieval technique in order to better understand and use its cloud products. The RTNEPH operates on two-channel data, generally obtained from Defense

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L. S. Rose, J. A. Stallins, and M. L. Bentley

–atmosphere characteristics can initiate and enhance convection and precipitation. Nonlinear interactions among surface friction, momentum drag, and urban heating can induce downwind convergence ( Rozoff et al. 2003 ). Urban heating may lead to the formation of a downwind updraft cell that strengthens as boundary layer stability or wind speed decreases ( Baik et al. 2001 ; Baik et al. 2007 ). As urban heating intensifies, precipitation may move closer to the source of heating ( Thielen et al. 2000 ). Aerosol size and

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Lauren E. Hay, Steven L. Markstrom, and Christian Ward-Garrison

1. Introduction In recognition of the vulnerability of freshwater resources to changing climate, many studies have sought to examine the effects of climate change on components of the hydrologic budget. The most common approach has been to combine basin-scale hydrologic models with climate-change scenarios derived from general circulation model (GCM) output ( Buytaert et al. 2009 ). GCMs are considered the most advanced tool currently available for simulating the effects of increasing

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Wondmagegn Yigzaw, Faisal Hossain, and Alfred Kalyanapu

river basins. The scale at which LULC changes impact atmospheric patterns varies based on the process that is affected. In general, such impacts are less observed in larger scales ( Blöschl et al. 2007 ). The atmospheric process can be affected to a larger extent than hydrological processes as these hydrological processes are bounded by physical watershed boundary unlike atmospheric processes ( Woldemichael et al. 2012 ; Yigzaw et al. 2012 ). The worst case is when both the atmospheric and

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Vivek K. Arora and George J. Boer

1. Introduction Vegetation affects climate by modifying the energy, momentum, and hydrologic balance of the land surface. Boundary layer exchanges of heat and momentum, evapotranspiration, and the absorption of solar radiation, are all influenced by vegetation and have important feedbacks on the global and regional climate. Vegetation influences climate via its physiological (stomatal conductance) and structural (leaf area index, root depth and distribution, height, and albedo) properties

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