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. 1996 ), 15-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analyses (ERA-15; Gibson et al. 1997 ), and the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU; Jones et al. 2001 ) datasets were analyzed. The results indicated that temperature differences between the NCEP1 and CRU datasets were largest in winter and smallest in summer, with NCEP1 being warmer over North America; comparisons for NCEP1 and ERA-15 were similar, whereas ERA-15 was noticeably warmer than CRU
. 1996 ), 15-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analyses (ERA-15; Gibson et al. 1997 ), and the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU; Jones et al. 2001 ) datasets were analyzed. The results indicated that temperature differences between the NCEP1 and CRU datasets were largest in winter and smallest in summer, with NCEP1 being warmer over North America; comparisons for NCEP1 and ERA-15 were similar, whereas ERA-15 was noticeably warmer than CRU
Grell deep convective parameterization ( Grell et al. 1994 ), the Medium-Range Forecasting (MRF) planetary boundary layer scheme ( Hong and Pan 1996 ), and the Reisner explicit cloud microphysics parameterization ( Reisner et al. 1998 ). This latter parameterization predicts the mixing ratio of cloud water and ice crystals as well as the rain and snow water mixing ratios. The Rapid Radiative Transfer Model (RRTM; Mlawer et al. 1997 ) is chosen for longwave radiation, and the delta
Grell deep convective parameterization ( Grell et al. 1994 ), the Medium-Range Forecasting (MRF) planetary boundary layer scheme ( Hong and Pan 1996 ), and the Reisner explicit cloud microphysics parameterization ( Reisner et al. 1998 ). This latter parameterization predicts the mixing ratio of cloud water and ice crystals as well as the rain and snow water mixing ratios. The Rapid Radiative Transfer Model (RRTM; Mlawer et al. 1997 ) is chosen for longwave radiation, and the delta
studies presented in this paper, we focus on MM5 temperature and precipitation grids for the model years 1992–2000 ( Wu et al. 2007 ) and compare them to several compiled or reanalyzed datasets that are frequently used in the climate research community. These include temperature and precipitation datasets from 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; see Gibson et al. 1997 ); 2) University of Delaware climate datasets [UDEL (MW); see Willmott and
studies presented in this paper, we focus on MM5 temperature and precipitation grids for the model years 1992–2000 ( Wu et al. 2007 ) and compare them to several compiled or reanalyzed datasets that are frequently used in the climate research community. These include temperature and precipitation datasets from 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; see Gibson et al. 1997 ); 2) University of Delaware climate datasets [UDEL (MW); see Willmott and
1. Introduction Understanding fire’s linkage to climate and influence on land cover, as well as associated feedbacks, is critical for accurate forecasts of global change impacts. Fire is the keystone disturbance in terrestrial ecosystems globally ( Clark et al. 1997 ; Pyne 2001 ; Lavorel et al. 2005 ), burning 200–500 × 10 6 hectares (Mha) annually ( Goldammer and Mutch 2001 ). Fire represents the primary reinitiation mechanism throughout much of the boreal biome and is responsible for the
1. Introduction Understanding fire’s linkage to climate and influence on land cover, as well as associated feedbacks, is critical for accurate forecasts of global change impacts. Fire is the keystone disturbance in terrestrial ecosystems globally ( Clark et al. 1997 ; Pyne 2001 ; Lavorel et al. 2005 ), burning 200–500 × 10 6 hectares (Mha) annually ( Goldammer and Mutch 2001 ). Fire represents the primary reinitiation mechanism throughout much of the boreal biome and is responsible for the
to as NCEP1; 2) a similar dataset for which temperature has been modified by elevation and precipitation has been modified with a statistical rescaling approach ( Serreze et al. 2003 ), which we refer to as NCEP2 (not to be confused with NCEP II sensu; Kistler et al. 2001 ); and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis ( Uppala et al. 2005 ), which we refer to as ERA-40. We established the NCEP1 dataset as the standard for comparing the applications of different
to as NCEP1; 2) a similar dataset for which temperature has been modified by elevation and precipitation has been modified with a statistical rescaling approach ( Serreze et al. 2003 ), which we refer to as NCEP2 (not to be confused with NCEP II sensu; Kistler et al. 2001 ); and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis ( Uppala et al. 2005 ), which we refer to as ERA-40. We established the NCEP1 dataset as the standard for comparing the applications of different
runoff is noted for NCEP2 and WM climate with the adjusted PM PET method, the accuracy of these precipitation data—the most important variable for arctic hydrological models—cannot be verified. Adjustment for biases such as gauge undercatch would likely significantly change simulated water fluxes. Other sources of climate data such as the new 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) may prove useful in improving closure of water budgets across the WALE
runoff is noted for NCEP2 and WM climate with the adjusted PM PET method, the accuracy of these precipitation data—the most important variable for arctic hydrological models—cannot be verified. Adjustment for biases such as gauge undercatch would likely significantly change simulated water fluxes. Other sources of climate data such as the new 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) may prove useful in improving closure of water budgets across the WALE
