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-parameter regression analysis similar to that used by Kasischke et al. ( Kasischke et al. 2002) . We stratified interior Alaska by ecoregion ( Gallant et al. 1995 ), growing-season climate ( Fleming et al. 2000 ), and fire frequency ( Kasischke et al. 2002 ). We assumed the Canadian portion of our domain to be similar and therefore did not incorporate region-specific data for the analysis. Fire frequencies and climate variables were computed directly (and respectively) from the Bureau of Land Management, Alaska
-parameter regression analysis similar to that used by Kasischke et al. ( Kasischke et al. 2002) . We stratified interior Alaska by ecoregion ( Gallant et al. 1995 ), growing-season climate ( Fleming et al. 2000 ), and fire frequency ( Kasischke et al. 2002 ). We assumed the Canadian portion of our domain to be similar and therefore did not incorporate region-specific data for the analysis. Fire frequencies and climate variables were computed directly (and respectively) from the Bureau of Land Management, Alaska
. Thompson , and A. D. McGuire , 2005 . Surface energy exchanges along a tundra-forest transition and feedbacks to climate. Agric. For. Meteor. 131 : 143 – 161 . Calef , M. P. , A. D. McGuire , H. E. Epstein , T. S. Rupp , and H. H. Shugart , 2005 . Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach. J. Biogeogr. 32 . doi:10.1111/j.1365-2699.2004.01185.x . Cihlar , J. , and J. Beaubien , 1998
. Thompson , and A. D. McGuire , 2005 . Surface energy exchanges along a tundra-forest transition and feedbacks to climate. Agric. For. Meteor. 131 : 143 – 161 . Calef , M. P. , A. D. McGuire , H. E. Epstein , T. S. Rupp , and H. H. Shugart , 2005 . Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach. J. Biogeogr. 32 . doi:10.1111/j.1365-2699.2004.01185.x . Cihlar , J. , and J. Beaubien , 1998
) Advanced Very High Resolution Radiometer (AVHRR)-based global land cover classification to define major biomes for PEM calculations within the study region ( Myneni et al. 1997b ; DeFries et al. 1998 ). Boreal forests and tundra are the major biomes within the region and represent approximately 52% and 30% of the region, respectively. The rest of the domain is composed of permanent ice and snow, barren land, and inland water bodies. These nonvegetated areas were masked from further analysis to isolate
) Advanced Very High Resolution Radiometer (AVHRR)-based global land cover classification to define major biomes for PEM calculations within the study region ( Myneni et al. 1997b ; DeFries et al. 1998 ). Boreal forests and tundra are the major biomes within the region and represent approximately 52% and 30% of the region, respectively. The rest of the domain is composed of permanent ice and snow, barren land, and inland water bodies. These nonvegetated areas were masked from further analysis to isolate
-term means or linear least squares regression results where significant ( P ≤ 0.1) secular trends were observed. We then conducted a statistical correlation analysis of the relationships between springtime thaw, LAI and annual productivity calculations, and mean NCEP surface meteorological parameters. The statistical significance of these relationships was assessed at the 90% confidence level. 3. Results 3.1. Spatial variability in spring thaw timing A map of the average (1988–2000) timing of the
-term means or linear least squares regression results where significant ( P ≤ 0.1) secular trends were observed. We then conducted a statistical correlation analysis of the relationships between springtime thaw, LAI and annual productivity calculations, and mean NCEP surface meteorological parameters. The statistical significance of these relationships was assessed at the 90% confidence level. 3. Results 3.1. Spatial variability in spring thaw timing A map of the average (1988–2000) timing of the
of mass or energy within a specified grid cell or between adjacent grid cells. 3. Results 3.1. Differences in input climate data Our analysis of absolute differences in mean annual temperature over the entire WALE region indicates that CRU is warmer than NCEP1 on the order of about 1°C ( Figure 1a ). During the 1990s, mean annual temperature from the MM5 simulations is approximately 3°C cooler than CRU and 2°C cooler than NCEP1. In agreement with Drobot et al. ( Drobot et al. 2006 ), comparison
of mass or energy within a specified grid cell or between adjacent grid cells. 3. Results 3.1. Differences in input climate data Our analysis of absolute differences in mean annual temperature over the entire WALE region indicates that CRU is warmer than NCEP1 on the order of about 1°C ( Figure 1a ). During the 1990s, mean annual temperature from the MM5 simulations is approximately 3°C cooler than CRU and 2°C cooler than NCEP1. In agreement with Drobot et al. ( Drobot et al. 2006 ), comparison
