The Multivariate Adaptive Constructed Analogs(MACA)(Abatzoglou, Brown, 2011)
method is a statistical downscaling method which utilizes a training dataset
(i.e. a meteorological observation dataset) 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 (RCPs) RCP 4.5 and RCP8.5 scenarios (2006-2100) from the native resolution of the GCMS to either 4-km or ~6-km.
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, wind). We currently have data for the following variables:
We are currently dispensing 3 data products: MACAv1-METDATA, MACAv2-METDATA and MACAv2-LIVNEH.
- tasmax - Maximum daily temperature near surface (2 m)
- tasmin - Minimum daily temperature near surface (2 m)
- rhsmax - Maximum daily relative humidity near surface (2 m)
- rhsmin - Minimum daily relative humdity near surface (2 m)
- huss - Average daily specific humidity near surface (2 m)
- pr - Average daily precipitation amount at surface
- rsds- Average daily downward shortwave radiation at surface
- was - Average daily wind speed near surface (10 m)
- uas - Average daily eastward component of wind near surface (10 m)
- vas - Average daily northward component of wind near surface (10 m)
- MACAv1-METDATA is available for the Western USA, while MACAv2-LIVNEH/MACAv2-METDATA are available over the entire coterminous USA.
- MACAv2-LIVNEH/MACAv2-METDATA both use the newest version of the MACA method (version 2), while MACAv1-METDATA uses version 1. Both methods are very similar to that described by Abatzoglou and Brown, 2011.