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Kristina Trusilova, Barbara Früh, Susanne Brienen, Andreas Walter, Valéry Masson, Grégoire Pigeon, and Paul Becker

atmospheric models serve as an instrument for studying the climate on spatial scales below 50–80 km. Such models can resolve the atmospheric flow in detail to account for urban-specific processes. The increasing resolution of regional climate models in the last decade lead to the increase of their complexity. On the fine spatial scales of 1–10 km, the parameterization of different land uses requires more discretization between natural and human-made surfaces as they differ greatly in their thermal and

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Zhihong Jiang, Fei Huo, Hongyun Ma, Jie Song, and Aiguo Dai

in tropospheric heating and thus in atmospheric circulation patterns ( Pielke 2005 ). On the other hand, increased anthropogenic aerosols due to urbanization and industrialization can cool the surface by directly reflecting solar radiation and indirectly increasing the reflectivity of clouds ( Albrecht 1989 ). They can change precipitation even in regions far away from highly polluted areas ( Wang 2013 ). Therefore, urban land use and anthropogenic aerosols can have large impacts on regional

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Deming Zhao and Jian Wu

1. Introduction Land use and land cover (LULC) change can affect regional climate by altering energy and water exchange between the land and atmosphere ( Gibbard et al. 2005 ; Pielke et al. 1998 ; Weaver and Avissar 2001 ). Contributions from LULC changes, including urban heat islands, to the globally averaged land surface air temperature at 2 m (SAT) change are unlikely to exceed 10%; however, their impact at the regional or local scales in an area with rapid economic development and human

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Don Cline, Simon Yueh, Bruce Chapman, Boba Stankov, Al Gasiewski, Dallas Masters, Kelly Elder, Richard Kelly, Thomas H. Painter, Steve Miller, Steve Katzberg, and Larry Mahrt

1. Introduction Airborne sensors provide many unique observing capabilities to help understand cold land processes. Aircraft platforms provide flexibility in data collection not generally found with spaceborne systems, improving opportunities for coordinating remote sensing observations with ground observations and for adapting to changing conditions. Seven airborne sensors ( Table 1 ) were used to observe the surface and near-surface of the study areas of the Cold Land Processes Experiment

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Glen E. Liston and Christopher A. Hiemstra

1. Introduction It has long been recognized that snow plays a key role in influencing climate system elements (e.g., Wagner 1973 ; Dewey 1977 ; Namias 1985 ; Walsh et al. 1985 ; Baker et al. 1992 ; Karl et al. 1993 ; Ellis and Leathers 1999 ; Bamzai and Shukla 1999 ), including atmospheric, hydrological, and ecosystem processes. Given snow’s function in governing atmospheric and land surface processes, it is imperative that local, regional, and global models used to simulate weather

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Tosiyuki Nakaegawa

use of land cover monitoring in land surface sciences. As a result, several 1-km global land cover datasets have been produced by multiple research institutions, generally with moderate-resolution optical sensor data. These global land cover datasets have been used for both global and regional studies because they cover all continents and islands with sufficient spatial resolution (1 km). However, comparative analysis of different land cover datasets showed per-pixel agreement of only about 0.6 (e

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Paul A. Dirmeyer, Sanjiv Kumar, Michael J. Fennessy, Eric L. Altshuler, Timothy DelSole, Zhichang Guo, Benjamin A. Cash, and David Straus

the time scales of interest. For intraseasonal to seasonal-scale predictability, which reaches beyond the range of deterministic prediction for the evolution of weather systems, there is a scientific basis to predict anomalies in the mean state and the variability of fluctuations at time scales longer than detailed weather variations ( Shukla 1998 ). Such predictions use models that incorporate a complete global ocean and land surface interacting with the atmosphere. The land, ocean and sea ice

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Ellen Jasinski, Douglas Morton, Ruth DeFries, Yosio Shimabukuro, Liana Anderson, and Matthew Hansen

the spatial distribution of cultivated land. Previous studies have demonstrated the impact of road access on land-use change ( Stone et al. 1991 ; Wilkie et al. 2000 ; Laurance et al. 2002 ; Alves 2002 ). How much land in Mato Grosso will ultimately be converted to mechanized agriculture depends on future economic, political, biophysical, and climatic conditions. However, current assessment of the likelihood of conversion to mechanized agriculture for different landscapes in Mato Grosso can

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

1. Introduction There is growing evidence that land-use change has an important impact on regional and global climate ( Cox et al. 2000 ; Cramer et al. 2001 ; Pielke et al. 2002 ; Xue et al. 2004 ; Feddema et al. 2005a ; Bonan 2008 ; Pielke and Niyogi 2010 ; Mahmood et al. 2010 ). Current efforts in developing strategies for adapting to and mitigating future climate change, however, have focused primarily on reducing greenhouse gases related to radiative forcings ( Pielke et al. 2002

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Justin E. Bagley, Ankur R. Desai, Paul C. West, and Jonathan A. Foley

drawbacks that make assessing near-surface climate regulation by land-cover change problematic. One restriction of these models is that they are computationally expensive to run and complex to interpret. An implication of this expense is that computational requirements prohibit experiments where large numbers of land-use scenarios are investigated. Although the physics of the land surface and atmosphere in most GCMs, RCMs, and LESs are detailed, this comes at the cost of significantly increasing the

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