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integrated approach to basin analysis and mineral exploration. Computerized Basin Analysis for the Prognosis of Energy and Mineral Resources, J. Harff and D. F. Merriam, Eds., Pergamon, 197–214 . Peterson , T. R. , R. Vose , R. Schmoyer , and V. Razuvaëv . 1998 . Global historical climatology network (GHCN) quality control of monthly temperature data. Int. J. Climatol. 18 : 1169 – 1179 . Serreze , M. C. and C. M. Hurst . 2000 . Representation of mean Arctic precipitation from
integrated approach to basin analysis and mineral exploration. Computerized Basin Analysis for the Prognosis of Energy and Mineral Resources, J. Harff and D. F. Merriam, Eds., Pergamon, 197–214 . Peterson , T. R. , R. Vose , R. Schmoyer , and V. Razuvaëv . 1998 . Global historical climatology network (GHCN) quality control of monthly temperature data. Int. J. Climatol. 18 : 1169 – 1179 . Serreze , M. C. and C. M. Hurst . 2000 . Representation of mean Arctic precipitation from
evapotranspiration and runoff are strongly dependent on the quality of time series data used to drive the model. These results suggest that closure of simulated water budgets across Arctic regions is strongly dependent on thorough evaluations of model requirements, potential biases in climatic datasets, and comparisons with observed data where available. Acknowledgments This work was supported by NSF Grants OPP-0094532, OPP-0230243, and OPP-9910264 and NASA Grant NNG-04GM19G. We wish to thank Alexander
evapotranspiration and runoff are strongly dependent on the quality of time series data used to drive the model. These results suggest that closure of simulated water budgets across Arctic regions is strongly dependent on thorough evaluations of model requirements, potential biases in climatic datasets, and comparisons with observed data where available. Acknowledgments This work was supported by NSF Grants OPP-0094532, OPP-0230243, and OPP-9910264 and NASA Grant NNG-04GM19G. We wish to thank Alexander
reanalysis data are model driven but provide self-consistent climatological mean states, fluxes, and variations. The reanalysis data used in this study have a horizontal resolution of T62 (∼240 km). The gridded observations are generally based on a merging of remote sensing products with in situ observations in which quality control was applied before interpolating to regular girds (1° × 1° for GPCP and Xie–Arkin; 0.5° × 0.5° for WM). For intercomparison, we take the MM5 simulation (referred to as RCM
reanalysis data are model driven but provide self-consistent climatological mean states, fluxes, and variations. The reanalysis data used in this study have a horizontal resolution of T62 (∼240 km). The gridded observations are generally based on a merging of remote sensing products with in situ observations in which quality control was applied before interpolating to regular girds (1° × 1° for GPCP and Xie–Arkin; 0.5° × 0.5° for WM). For intercomparison, we take the MM5 simulation (referred to as RCM
carbon dynamics will require both an understanding of the effects of different driving data as well as the consideration of projections of fire dynamics. In this paper we address the latter issue by assessing the sensitivity of simulated fire dynamics to different driving climate datasets. Our focus is the response of a transient vegetation dynamics model, Alaskan Frame-based Ecosystem Code (ALFRESCO) ( Rupp et al. 2000b ; Rupp et al. 2002 ), to the same driving datasets evaluated by Clein et al
carbon dynamics will require both an understanding of the effects of different driving data as well as the consideration of projections of fire dynamics. In this paper we address the latter issue by assessing the sensitivity of simulated fire dynamics to different driving climate datasets. Our focus is the response of a transient vegetation dynamics model, Alaskan Frame-based Ecosystem Code (ALFRESCO) ( Rupp et al. 2000b ; Rupp et al. 2002 ), to the same driving datasets evaluated by Clein et al
carbon dynamics, despite the relatively coarse (16 km) spatial scale and monthly temporal fidelity of the remote sensing data, and uncertainties associated with sensor stability and spectral quality of the NOAA AVHRR time series. BIOME–BGC, TEM, and PEM results occupied respective upper, lower, and intermediate levels of estimated regional C fluxes. BIOME–BGC simulations of annual vegetation productivity were 16%–21% greater than PEM results and 22% smaller than NOAA AVHRR–based observations of LAI
carbon dynamics, despite the relatively coarse (16 km) spatial scale and monthly temporal fidelity of the remote sensing data, and uncertainties associated with sensor stability and spectral quality of the NOAA AVHRR time series. BIOME–BGC, TEM, and PEM results occupied respective upper, lower, and intermediate levels of estimated regional C fluxes. BIOME–BGC simulations of annual vegetation productivity were 16%–21% greater than PEM results and 22% smaller than NOAA AVHRR–based observations of LAI
