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. The term “poor man” refers to the method of dynamical downscaling from a larger-scale reanalysis. Berg and Christensen (2008) used the Danish RCM HIRHAM5 [the model name HIRHAM (version 5) comes from combining the High-Resolution Limited-Area Model (HIRLAM) and the German “ECHAM” Model], which was nested into the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ERA Interim reanalysis data ( Uppala et al. 2005 ) for a domain covering most of Europe at a
. The term “poor man” refers to the method of dynamical downscaling from a larger-scale reanalysis. Berg and Christensen (2008) used the Danish RCM HIRHAM5 [the model name HIRHAM (version 5) comes from combining the High-Resolution Limited-Area Model (HIRLAM) and the German “ECHAM” Model], which was nested into the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ERA Interim reanalysis data ( Uppala et al. 2005 ) for a domain covering most of Europe at a
. (2009) showed that the implementation of an irrigation scheme reduces the warm bias of the Max Planck Institute Regional Model (REMO) for the north of India and improves the precipitation distribution. Recently, Dobler and Ahrens (2010) performed RCM simulations with the Consortium for Small-Scale Modelling (COSMO) Climate Local Model (CLM) RCM over South Asia, using the ECHAM5–Max Planck Institute Ocean Model (MPIOM) GCM and the 45-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re
. (2009) showed that the implementation of an irrigation scheme reduces the warm bias of the Max Planck Institute Regional Model (REMO) for the north of India and improves the precipitation distribution. Recently, Dobler and Ahrens (2010) performed RCM simulations with the Consortium for Small-Scale Modelling (COSMO) Climate Local Model (CLM) RCM over South Asia, using the ECHAM5–Max Planck Institute Ocean Model (MPIOM) GCM and the 45-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re
) to provide the mean climate and global reanalysis products to distribute the mean monthly climate to daily and hourly estimates. The WFD has improved on previously published meteorological forcing data, providing half-degree rather than one-degree resolution and covering the whole of the twentieth century (1901–2001). In addition, key differences in processing ( Weedon et al. 2011 , this collection) involved (i) the use of 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re
) to provide the mean climate and global reanalysis products to distribute the mean monthly climate to daily and hourly estimates. The WFD has improved on previously published meteorological forcing data, providing half-degree rather than one-degree resolution and covering the whole of the twentieth century (1901–2001). In addition, key differences in processing ( Weedon et al. 2011 , this collection) involved (i) the use of 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re
Research Unit (CRU) of the University of East Anglia global land mask. No effort was made to harmonize model parameters, but the models were forced by the same meteorological data—the so-called WATCH Forcing Data (WFD; Weedon et al. 2010 , 2011 ). The WFD are based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ) interpolated to the 0.5° grid defined by the CRU land mask and then adjusted for elevation differences. Air temperature is
Research Unit (CRU) of the University of East Anglia global land mask. No effort was made to harmonize model parameters, but the models were forced by the same meteorological data—the so-called WATCH Forcing Data (WFD; Weedon et al. 2010 , 2011 ). The WFD are based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ) interpolated to the 0.5° grid defined by the CRU land mask and then adjusted for elevation differences. Air temperature is
forcing dataset named the Water and Global Change (WATCH) Forcing Data (WFD; WATCH is a European Union–funded research project — for details on WFD see Weedon et al. 2010 ) was used. The data were derived from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) product as described by Uppala et al. (2005) via sequential interpolation to 0.5° resolution, elevation correction, and monthly-scale adjustments (corrected temperature, diurnal temperature range) based
forcing dataset named the Water and Global Change (WATCH) Forcing Data (WFD; WATCH is a European Union–funded research project — for details on WFD see Weedon et al. 2010 ) was used. The data were derived from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) product as described by Uppala et al. (2005) via sequential interpolation to 0.5° resolution, elevation correction, and monthly-scale adjustments (corrected temperature, diurnal temperature range) based
precipitation analysis over Crete where chosen based on several criteria such as the correspondence of RCMs mesh, the forecast period, and the performance on their ability to simulate the present climate, calibrated for the 1973–2000 observed climate over Crete. RCM-specific weights were extracted in order to construct the optimal ensemble output for precipitation at a monthly time step and watershed level ( Christensen et al. 2010 ). The ENSEMBLES RCM weights were based on a set of metrics that were
precipitation analysis over Crete where chosen based on several criteria such as the correspondence of RCMs mesh, the forecast period, and the performance on their ability to simulate the present climate, calibrated for the 1973–2000 observed climate over Crete. RCM-specific weights were extracted in order to construct the optimal ensemble output for precipitation at a monthly time step and watershed level ( Christensen et al. 2010 ). The ENSEMBLES RCM weights were based on a set of metrics that were
fractional coverage calculations in the optional canopy model; a new implicit numerical scheme for updating temperatures and moisture contents of soil layers; and inclusion of increments due to snowmelt or limited moisture availability within the implicit calculation of surface heat and moisture fluxes. A direct comparison of MOSES1 and MOSES2 has not been performed, but the performance of MOSES 2 is discussed in climate simulations by Essery et al. (2003) and in mesoscale forecasts by Best et al
fractional coverage calculations in the optional canopy model; a new implicit numerical scheme for updating temperatures and moisture contents of soil layers; and inclusion of increments due to snowmelt or limited moisture availability within the implicit calculation of surface heat and moisture fluxes. A direct comparison of MOSES1 and MOSES2 has not been performed, but the performance of MOSES 2 is discussed in climate simulations by Essery et al. (2003) and in mesoscale forecasts by Best et al
dataset covers the period 1958–2001 and is based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ). The ERA-40 data were interpolated to 0.5° and only considered over land points using the land–sea mask from the Climate Research Unit dataset TS2.1 (CRU; Mitchell and Jones 2005 ). A correction for elevation differences between ERA-40 and CRU was applied. For 2-m temperatures, a correction of the monthly means with CRU data was performed
dataset covers the period 1958–2001 and is based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ). The ERA-40 data were interpolated to 0.5° and only considered over land points using the land–sea mask from the Climate Research Unit dataset TS2.1 (CRU; Mitchell and Jones 2005 ). A correction for elevation differences between ERA-40 and CRU was applied. For 2-m temperatures, a correction of the monthly means with CRU data was performed
steps. The WATCH forcing variables are taken from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as described by Uppala et al. (2005) . The 1° ERA-40 product was interpolated to ½° resolution on the CRU land mask, adjusted for elevation changes where needed, and bias corrected using monthly observations. Temperature, surface pressure, specific humidity, and downward longwave radiation were adjusted sequentially in that order because they are interdependent
steps. The WATCH forcing variables are taken from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as described by Uppala et al. (2005) . The 1° ERA-40 product was interpolated to ½° resolution on the CRU land mask, adjusted for elevation changes where needed, and bias corrected using monthly observations. Temperature, surface pressure, specific humidity, and downward longwave radiation were adjusted sequentially in that order because they are interdependent
calculate changes in hydrologically important variables such as evaporation, soil moisture, and runoff ( Haddeland et al. 2011 ). For both types of model, meteorological “forcing” (or “driving”) data (air temperature, rainfall/snowfall, etc.) are required at subdaily time steps for the LSMs and daily time steps for the GHMs. The 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) product, which provided the basis data used in the derivation of the WFD, was derived from
calculate changes in hydrologically important variables such as evaporation, soil moisture, and runoff ( Haddeland et al. 2011 ). For both types of model, meteorological “forcing” (or “driving”) data (air temperature, rainfall/snowfall, etc.) are required at subdaily time steps for the LSMs and daily time steps for the GHMs. The 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) product, which provided the basis data used in the derivation of the WFD, was derived from
