MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models

IDAHO_EPSCOR/MACAv2_METDATA
Dataset Availability
1900-01-01T00:00:00 - 2100-12-31T00:00:00
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("IDAHO_EPSCOR/MACAv2_METDATA")
Tags
maca climate conus monthly geophysical idaho

Description

The MACAv2-METDATA dataset is a collection of 20 global climate models covering the conterminous 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.

The full list of models can be found at: http://climate.nkn.uidaho.edu/MACA/GCMs.php

Bands

Resolution
2.5 arc minutes

Bands

Name Units Min Max Description
tasmax K 222.4* 334.92*

Maximum daily temperature near surface

tasmin K 215.33* 315.61*

Minimum daily temperature near surface

rhsmax % 1* 100*

Maximum daily relative humidity near surface, not present in models CCSM4 or NorESM1-M

rhsmin % 1* 100*

Minimum daily relative humidity near surface, not present in models CCSM4 or NorESM1-M

huss kg/kg 0* 0.05*

Average daily specific humidity near surface

pr mm 0* 1609.77*

Average daily precipitation amount at surface

rsds W/m^2 9.06* 455.61*

Average daily downward shortwave radiation at surface

uas m/s -29.74* 25.96*

Average daily eastward component of wind near surface

vas m/s -29.26* 33.06*

Average daily northward component of wind near surface

* estimated min or max value

Image Properties

Image Properties

Name Type Description
scenario STRING

Name of the CMIP5 scenario, one of 'rcp85', 'rcp45', or 'historical'

model STRING

Name of the CMIP5 model, eg 'inmcm4'

ensemble STRING

Either 'r1i1p1' or 'r6i1p1'

Terms of Use

Terms of Use

The MACA datasets were created with funding from the US government and are in the public domain in the United States. For further clarity, unless otherwise noted, the MACA datasets are made available with a Creative Commons CC0 1.0 Universal dedication. In short, John Abatzoglou waives all rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute, and perform the work, even for commercial purposes, all without asking permission. John Abatzoglou makes no warranties about the work, and disclaims liability for all uses of the work, to the fullest extent permitted by applicable law. Users should properly cite the source used in the creation of any reports and publications resulting from the use of this dataset and note the date when the data was acquired. For more information refer to the MACA References and License page.

Citations

Citations:
  • Abatzoglou J.T. and Brown T.J., A comparison of statistical downscaling methods suited for wildfire applications, International Journal of Climatology(2012) doi: https://doi.org/10.1002/joc.2312.

Explore in Earth Engine

var dataset = ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA')
                  .filter(ee.Filter.date('2018-08-01', '2018-08-15'));
var maximumTemperature = dataset.select('tasmax');
var maximumTemperatureVis = {
  min: 290.0,
  max: 314.0,
  palette: ['d8d8d8', '4addff', '5affa3', 'f2ff89', 'ff725c'],
};
Map.setCenter(-84.37, 33.5, 5);
Map.addLayer(maximumTemperature, maximumTemperatureVis, 'Maximum Temperature');