CMIP5 Statistically Downscaled for Western USA
The Multivariate Adaptive Constructed Analogs(MACA)(Abatzoglou, Brown, 2011)
method is a statistical downscaling method which utilizes a training dataset ( the meteorological observation dataset (Abatzoglou, 2012)) to remove historical biases and match spatial patterns in climate model output.
We have used MACA to downscale the model output from 20 global climate models (GCMs) of the Coupled Model Inter-Comparison Project 5 (CMIP5) for the historical GCM forcings (1950-2005) and the future Representative Concentration Pathways (rcp's) rcp 45/85 scenarios (2006-2100).
The MACA dataset is unique in that it downscales a large set of variables making it ideal for different kinds of modeling of future climate (i.e. hydrology, ecology, vegetation, fire). We currently have data for the following variables: