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

succession; and from residual sensor calibration and aerosol effects on LAI and FPAR data. 2.2. Classification of primary seasonal thaw events The Special Sensor Microwave Imager (SSM/I) is a multifrequency, linearly polarized passive microwave radiometer operating with a constant incidence angle of 53.1° and has flown on the Defense Meteorological Satellite Program (DMSP) platform series. Coverage is global and began in August 1987. We utilized the 19-GHz horizontally polarized channel, which has a

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Wanli Wu, Amanda H. Lynch, Sheldon Drobot, James Maslanik, A. David McGuire, and Ute Herzfeld

study by Drobot et al. ( Drobot et al. 2006 ) conducted a comprehensive intercomparison for precipitation and surface air temperature (SAT) among a number of data sources including the RCM simulation presented in this paper. Thus, the validation and intercomparison in this paper will focus on spatial patterns of mean states, variations, and corresponding seasonality. We also examine trends in the regional climate, with an emphasis on trends in the seasonality of surface climate. This paper is

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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

largely due to the predominance of air temperature as a major control on photosynthetic and respiration rates. The somewhat lower correspondence between TEM and PEM results reflects differences between mean monthly and daily meteorological inputs used as the major drivers of the biophysical process simulations. PEM and BIOME–BGC simulations reflect the cumulative effects of daily variations in meteorological conditions and discrete events, while TEM primarily reflects seasonal patterns captured by a

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

seasonality of photosynthetic carbon uptake in the spring, a result that is consistent with the analysis by Kimball et al. ( Kimball et al. 2006 ) that shows that annual anomalies in NPP of the WALE region are highly correlated with the timing in spring thaw. Other studies (e.g., see Euskirchen et al. 2006 ) have also identified that the timing of spring thaw is an important factor in the uptake of carbon by vegetation across the pan-arctic/pan-boreal region. In contrast to the effects of different

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

to the statistical differences observed in the time series analysis? Analysis methods used in this study include analysis of variance (ANOVA) with post hoc means comparisons to determine significant differences in the datasets, anomaly correlations to examine seasonal cycles, and similarity maps to highlight spatial regions where datasets differ. 2. Datasets Each of the datasets analyzed here is used to validate or force hydrological models in the WALE project. The NCEP1 and ERA-40 are data

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

function of geographical location? How large are seasonal effects, and how are they distributed regionally? What are the potential causes and remedies of discrepancies between data and models? Hence the objectives of our study are 1) quantitative assessment of similarity between datasets and climate model fields, for temperature and precipitation, in a spatial domain; 2) identification of geographic areas that are problematic in modeling; 3) investigation of seasonal differences in model–data agreement

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

vegetation imply that fire’s sensitivity to global change could be more important than the direct effects of climatic warming on terrestrial ecosystems ( Rupp et al. 2000a ; Houghton 2001 ; Lavorel et al. 2005 ). Studies of future responses of fire weather severity ( Flannigan and Van Wagner 1991 ; Flannigan et al. 1998 ; Stocks et al. 1998 ) and area burned ( Price and Rind 1994 ; Flannigan et al. 2005 ) indicate strong increases for many regions of the boreal forest but are characterized with

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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

al. 2007 ; Rupp et al. 2007 ; Wu et al. 2007 ). Drobot et al. ( Drobot et al. 2006 ) compared the temporal and spatial variability among the temperature and precipitation variables in the NCEP1, ERA-40, CRU, WM, and MM5 datasets from 1992 to 2000 for the southern portion of the WALE region. Analysis of the climatological means of seasonal variability revealed that the largest number of differences in temperature (precipitation) occur in October (July). In contrast, interannual variability of

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M. A. Rawlins, S. Frolking, R. B. Lammers, and C. J. Vörösmarty

. Precipitation simulated in GCMs tends to be overestimated and seasonal dynamics are often inaccurate ( Kite and Haberlandt 1999 ; Töyrä et al. 2005 ). Hydrological models that account for phase changes in soil water ( Rawlins et al. 2003 ; Su et al. 2005 ) have recently been adopted in hopes of improving simulated water budgets across high-latitude regions. Water budget models are dependent on accurate inputs of air temperature and especially precipitation in order to adequately depict the spatial and

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