MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models (IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY)

Version 2

The MACAv2-METDATA dataset is a collection of 20 global
climate models covering the coterminous USA. The Multivariate Adaptive
Constructed Analogs (MACA) 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.

The MACA method was used 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 RCP 8.5 scenarios (2006-2100) from the native resolution of
the GCMS to 4km.

This version contains monthly summaries.

tasmaxMonthly average of maximum daily temperature near surfaceK
tasminMonthly average of minimum daily temperature near surfaceK
hussMonthly average of mean daily specific humidity near surfacekg/kg
prTotal monthly precipitation amount at surfacemm
rsdsMonthly average of mean daily downward shortwave radiation at surfaceW/m^2
wasMonthly average of mean daily near surface wind speedm/s


University of Idaho (producer, licensor)
Google Earth Engine (host)
STAC Version 0.6.2
Keywords climate, conus, geophysical, idaho, maca, monthly
License proprietary
Temporal Extent 12/31/1899, 4:00:00 PM - 12/30/2099, 4:00:00 PM
Citation Abatzoglou J.T. and Brown T.J., A comparison of statistical downscaling methods suited for wildfire applications, International Journal of Climatology(2012) doi: [](
Type image_collection
GSD arc minutesm
Cadence month
cube:dimensions {"x":{"type":"spatial","axis":"x","extent":[-124.9,-67]},"y":{"type":"spatial","axis":"y","extent":[24.9,49.6]},"temporal":{"type":"temporal","extent":["1900-01-01T00:00:00Z","2099-12-31T00:00:00Z"]},"bands":{"type":"bands","values":["tasmax","tasmin","huss","pr","rsds","was"]}}