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Ute C. Herzfeld, Sheldon Drobot, Wanli Wu, Charles Fowler, and James Maslanik

. 2007 ) are taken a step further into a multidimensional spatiotemporal domain. To achieve this, we apply algebraic similarity mapping, a method for quantitative comparison of any number of input datasets, maps, or models, which was first developed for resource exploration ( Herzfeld and Merriam 1990 ) and is adapted here for climate data analysis and the WALE experiment. Similarity mapping, or algebraic map comparison, utilizes a multidimensional algebraic algorithm to compare any number of input

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J. S. Kimball, K. C. McDonald, and M. Zhao

2000 onward ( Running et al. 2004 ; Zhao et al. 2005 ). A detailed description of these algorithms and associated BPLUT properties can be found in the MODIS MOD17 User’s Guide ( Heinsch et al. 2003 ). For this investigation, we applied the PEM described above to assess spatial and temporal variability in annual vegetation productivity for the study region over a 13-yr period from 1988 to 2000. The PEM requires spatially explicit and temporally contiguous inputs of daily surface meteorology, and

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Joy Clein, A. David McGuire, Eugenie S. Euskirchen, and Monika Calef

thermal model (with an updated soil freeze–thaw algorithm) and considers the effects of freeze–thaw dynamics on gross primary production. The most detailed descriptions can be found in Raich et al. ( Raich et al. 1991 ), McGuire et al. ( McGuire et al. 1992 ), and Tian et al. ( Tian et al. 1999 ). The following is a short summary of model components related to the simulated fluxes analyzed in this study. The flux NPP, which represents the net amount of CO 2 taken up by vegetation, is calculated as

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Sheldon Drobot, James Maslanik, Ute Christina Herzfeld, Charles Fowler, and Wanli Wu

on station data, the MW dataset is compiled from numerous other datasets, including the Global Historical Climatology Network ( Peterson et al. 1998 ), the Global Synoptic Climatology Network (National Climatic Data Center Dataset 9290c), and the Global Surface Summary of Day ( http://www.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html ). Monthly mean surface air temperature and precipitation were regridded onto 0.5° × 0.5° grids through the spherical version of Shepard’s algorithm, which employs

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J. S. Kimball, M. Zhao, A. D. McGuire, F. A. Heinsch, J. Clein, M. Calef, W. M. Jolly, S. Kang, S. E. Euskirchen, K. C. McDonald, and S. W. Running

(MODIS) sensor on board the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite from 2000 onward ( Running et al. 2004 ; Zhao et al. 2005 ). A detailed description of these algorithms and associated BPLUT properties can be found in the MODIS MOD17 User’s Guide ( Heinsch et al. 2003 ). 2.2. Ecosystem process model simulations 2.2.1. BIOME–BGC BIOME–BGC is a general ecosystem process model designed to simulate fluxes and storage of carbon, water, and

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T. Scott Rupp, Xi Chen, Mark Olson, and A. David McGuire

. 2006 ). Because ALFRESCO operates at a resolution of 1 km to simulate interactions between fire and vegetation heterogeneity across the landscape, it requires driving data at 1-km resolution. For this analysis we used a simple resampling algorithm to process each dataset and generate climate data for a grid of 1 km × 1 km pixels. No interpolation (i.e., downscaling) methodology was employed in this analysis as we simply populated the six hundred twenty-five 1 km × 1 km pixels within an EASE pixel

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