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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
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
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
used for intercomparison include those derived from 1) large-scale global reanalysis: NCEP–NCAR reanalysis and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40); and 2) gridded observations: Willmott–Matsuura (WM) climatology, Global Precipitation Climatology Project (GPCP), and Xie–Arkin global precipitation. A global reanalysis is a retrospective global analysis of atmospheric and surface fields. Available observations are assimilated into a global model. The
used for intercomparison include those derived from 1) large-scale global reanalysis: NCEP–NCAR reanalysis and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40); and 2) gridded observations: Willmott–Matsuura (WM) climatology, Global Precipitation Climatology Project (GPCP), and Xie–Arkin global precipitation. A global reanalysis is a retrospective global analysis of atmospheric and surface fields. Available observations are assimilated into a global model. The
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
Canada and Alaska. Land Change Science: Observing, Monitoring, and Understanding Trajectories of Change on the Earth’s Surface, G. Gutman et al. Eds., Kluwer Academic, 139–161 . Mitchell , T. D. , T. R. Carter , P. D. Jones , M. Hulme , and M. New . 2004 . A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: The observed record (1901–2000) and 16 scenarios (2001–2100). Tyndall Centre for Climate Change Research Working Paper 55, University of
Canada and Alaska. Land Change Science: Observing, Monitoring, and Understanding Trajectories of Change on the Earth’s Surface, G. Gutman et al. Eds., Kluwer Academic, 139–161 . Mitchell , T. D. , T. R. Carter , P. D. Jones , M. Hulme , and M. New . 2004 . A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: The observed record (1901–2000) and 16 scenarios (2001–2100). Tyndall Centre for Climate Change Research Working Paper 55, University of
