Multivariate Adaptive Constructed Analogs(MACA)
Statistical Downscaling Method
University of Idaho


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:
  • tasmax - Maximum daily temperature near surface
  • tasmin - Minimum daily temperature near surface
  • rhsmax - Maximum daily relative humidity near surface
  • rhsmin - Minimum daily relative humdity near surface
  • huss - Average daily specific humidity near surface
  • pr - Average daily precipitation amount at surface
  • rsds- Average daily downward shortwave radiation at surface
  • was - Average daily wind speed near surface
  • uas - Average daily eastward component of wind near surface
  • vas - Average daily northward component of wind near surface
We currently have three MACA data products: MACAv1-METDATA(daily/monthly for WUSA), MACAv2-LIVNEH(daily for CONUS)and MACAv2-METDATA(daily for CONUS). Only MACAv1-METDATA is currently available for download, but the others are coming soon (mid-March, early April). See 'About the Data'... 'Data Products' for more information on these.


UofI reacch CIRC CSC