|Resource type:||Composite Resource|
|Created:||Jul 06, 2017 at 4:48 p.m.|
|Last updated:||Jul 10, 2017 at 7:24 p.m. by Donghui Xu|
Climate change will affect global temperatures and the distribution and amount of precipitation, which are expected to impact regional hydrology and water resources in many parts of the world. It is therefore vital to quantify characteristics of the change and the corresponding uncertainty. A substantial amount of recent research has relied on climate projections obtained with General Circulation Models (GCMs) to assess climate change. However, such modeling results typically carry biases that must be reduced in some optimal fashion before any conclusions about robustness of climate change can be drawn. To minimize model- and scenario-specific biases, we combined information provided by the 5th phase of the Coupled Model Intercomparison Project database with a Bayesian Weighted Averaging method. Specifically, the results of 18 GCMs for two emission scenarios RCP45 and RCP85 were downscaled for mid- (2041–2070) and end-century (2071–2100) intervals, at six WebMET locations that represent a hydroclimatic transect of Michigan. Furthermore, hourly results of future climate are generated by an advanced weather generator using the information from the combine GCMs ensemble.
precipitation projections,stochastic downscaling,climate change,uncertainty,CMIP5,weather generator,factor of change
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