Using climate scenarios from only 1 or a small number of global models (GCMs) in change impact studies may lead to biased assessment due large uncertainty projections. Ensemble means projections derived multi-GCM ensemble are often used as best estimates reduce bias. However, it is time consuming run process-based (e.g. hydrological and crop models) using numerous scenarios. It would be interes...

this study aims to evaluate effects of two downscaling methods; change-factor and statistical downscaling on therunoff of the azam-harat river located at yazd province (with an arid climate) of iran, under the a2 emission scenariofor the period of 2010-2039. for this purpose, cgcm3-ar4 model; a rainfall-runoff conceptual model, ihacres; twodownscaling models, change factor and lars-wg were appl...

prediction of climatic variables on a local scale by general circulation models of the atmosphere is impossible because the models have large-scale network of resolution. therefore, downscaling methods are used to solve this problem. since the climate change phenomenon can affect different systems such as, water resources, agriculture, environment, industry and economy as well, selection of the...

[1] A mesoscale model (MM5)–based regional climate model (CMM5) integration driven by the Parallel Climate Model (PCM), a fully coupled atmosphere-ocean-land-ice general circulation model (GCM), for the present (1986–1995) summer season climate is first compared with observations to study the CMM5’s downscaling skill and uncertainty over the United States. The results indicate that the CMM5, wi...

This study aims to evaluate effects of two downscaling methods; change-factor and statistical downscaling on therunoff of the Azam-Harat River located at Yazd province (with an arid climate) of Iran, under the A2 emission scenariofor the period of 2010-2039. For this purpose, CGCM3-AR4 model; a rainfall-runoff conceptual model, IHACRES; twodownscaling models, Change Factor and LARS-WG were appl...

Downscaling of climate model data is essential to local and regional impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km2 per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Mod...

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling--nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output--to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly ...