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Gregory R. Quetin and Abigail L. S. Swann

there is no analog ecological community to base predictions on, in order to project vegetation response to novel environments, process-based models must correctly reproduce ecosystem functioning: the sensitivity of vegetation to the physical environment. Observational constraints are critical for testing the fidelity of our ability to simulate ecosystem functioning in the present-day climate and to provide confidence in simulations under a changing climate. Here we evaluate the ecosystem functioning

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David Medvigy, Robert L. Walko, and Roni Avissar

affect evapotranspiration ( Marani et al. 1997 ; Silva and Rodriguez 2001 ; Owens et al. 2006 ), with possible consequences for regional and global climate. Terrestrial ecosystems are sensitive to both precipitation averages ( Gnandesikan and Stouffer 2006 ) and other statistics ( Ruel and Ayres 1999 ). One mechanism by which high-frequency precipitation variability affects vegetation is through the interception of water by the plant canopy ( Wang and Eltahir 2000 ; Bagnoud et al. 2005

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Makoto Saito, Akihiko Ito, and Shamil Maksyutov

in a variety of studies (e.g., Annamalai et al. 1999 ; Rödenbeck et al. 2003 ; Tourigny and Jones 2009 ). However, care is required when using the above reanalysis products despite their obvious benefits because many investigations have identified biases in the reanalyses (e.g., Randel et al. 2000 ; Berg et al. 2003 ), resulting in poor model simulations of the surface water balance ( Lenters et al. 2000 ), climatology ( Serreze and Hurst 2000 ), and terrestrial ecosystem productivity

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Christopher Potter, Steven Klooster, Alfredo Huete, and Vanessa Genovese

implications for carbon cycle research. Direct input of satellite vegetation index “greenness” data from the MODIS sensor into ecosystem simulation models is now used to estimate spatial variability in monthly net primary production (NPP), biomass accumulation, and litter fall inputs to soil carbon pools. Global NPP of vegetation can be predicted using the relationship between leaf reflectance properties and the absorption of photosynthetically active radiation (PAR), assuming that net conversion

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Min Chen and Qianlai Zhuang

biological, physical, and chemical processes of ecosystems and use mathematical equations to represent these processes. These mathematical equations are usually parameterized for representative vegetation types and then extrapolated to regional scales. For example, the Terrestrial Ecosystem Model (TEM) has been widely used to study ecosystem carbon and nitrogen dynamics at different scales since the early 1990s ( Kicklighter et al. 1999 ; McGuire et al. 1992 ; McGuire et al. 2001 ; Melillo et al. 1993

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A. D. McGuire, J. E. Walsh, J. S. Kimball, J. S. Clein, S. E. Euskirchen, S. Drobot, U. C. Herzfeld, J. Maslanik, R. B. Lammers, M. A. Rawlins, C. J. Vorosmarty, T. S. Rupp, W. Wu, and M. Calef

( McGuire et al. 2006 ). To predict the role of high-latitude terrestrial ecosystems in the response of the Earth system to global change requires the integration of climate dynamics, ecosystem dynamics, and large-scale hydrology in high-latitude regions. The Western Arctic Linkage Experiment (WALE) Project was designed to assess the ability of models to simulate water/energy and CO 2 exchange with the atmosphere, and freshwater delivery to the ocean for the Alaskan region in the 1980s and 1990s. The

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Gretchen Keppel-Aleks, Samantha J. Basile, and Forrest M. Hoffman

evaluate models against observations. When evaluating coupled ESMs, simulated ecosystem properties may disagree with observational metrics due either to misparameterization of the relevant biogeochemical or biogeophysical processes or to biases in the physical climate drivers thereof. Model development and improvement, therefore, requires evaluating simulations using metrics that constrain functional responses—in other words, the relationships between driver and response variables—rather than simply

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Mustapha El Maayar, Navin Ramankutty, and Christopher J. Kucharik

atmospheric chemistry, through its exchanges of energy, momentum, water vapor, and various trace gases such as CO 2 and CH 4 with the atmosphere ( Schlesinger 1991 ; Pielke et al. 1998 ). For that reason, considerable efforts have been made over the last three decades to develop terrestrial ecosystem models that describe the multiple interactions that occur at the land surface, and between the surface and the atmosphere at varying spatiotemporal scales ( Foley 1995 ; Hurtt et al. 1998 ). For instance

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Michael A. White, Peter E. Thornton, Steven W. Running, and Ramakrishna R. Nemani

carbon dynamics ( Motovalli et al. 1994 ), effects of nitrogen saturation ( Aber et al. 1997 ), and the location of global carbon sources and sinks ( Houghton et al. 1998 ; Randerson et al. 1997 ). Models can also be used to develop basic theoretical understandings of ecosystem function that cannot be tested with field methods ( Churkina and Running 1998 ; Schimel et al. 1996 ). Perhaps most importantly, models are used to address the political and management need for estimates of ecosystem

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Melanie Brown and Dominique Bachelet

finescale features and provide relevant climate drivers to drive hydrologic- and ecosystem-response models. Statistical downscaling derives relationships between observations (from weather stations) and simulation results from a general circulation model. These statistical relationships are applied to the climate model’s future climate projections to generate finer-scale projections. This method assumes that correlations between coarse- and finescale climate remain the same (stationary) in the future

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