Multivariate Adaptive Constructed Analogs(MACA)
Statistical Downscaling Method


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.

Most recently, MACA has been used to downscaled the model output from 14 global climate models (GCMs) of the Coupled Intermodel Comparison Project 5 (CMIP5) to 4-km resolution for the historical GCM forcings (1950-2005) and the future Representative Concentration Pathways (rcp's) rcp 45/85 scenarios (2006-2100).

This Dataset has the following features:

  • Spatial Resolution: 4-km grid (1/24 deg)
  • Spatial Extent: Western United States(31.02 to 49.1 N, -124.77 to -103.02 W)
  • Temporal Resolution: Daily(downscaled) and Monthly(aggregated)
  • Temporal Extent: 1950-2100
  • Variables:
    • Precipitation
    • Temperature (maximum and minimum)
    • Humidity (maximum and minimum relative humidity and specific humidity)
    • Surface downward shortwave radiation (mean)
    • Wind components (mean)
  • Format: netCDF adhering to Climate and Forecasting Metadata standards

UofI reacch CIRC CSC