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MACA CMIP5 Statistically Downscaled Climate Projections

Screen capture from the MACA CMIP5 Statistically Downscaled Climate Projections tool

Access projection data produced through the Multivariate Adaptive Constructed Analogs (MACA) method. Variables available for the contiguous U.S. enable users to explore projections for hydrology, ecology, vegetation, fire, and wind.

Narrative

Important Notice for Using Climate Projections

Climate projections can be useful for making decisions about the future, but the limitations of climate models make it easy to misinterpret or misuse their results. Be aware that:

  • Climate projections are not predictions. Projections are based on assumptions about future human emissions of greenhouse gases and other policy choices.
  • Climate projections do not attempt to predict the timing of meteorological events such as storms, droughts, or El Niños. The location and timing of future extreme weather events cannot be deduced from climate model projections.
  • Projections vary from model to model: the best projection dataset for one location and purpose may not be the best for other situations. Considering a range of projections may help you gain a more complete picture of potential future risks.
  • The increased spatial resolution of statistically downscaled projections available for temperature and precipitation may not be available for all parameters. In addition, increased resolution does not necessarily equate to greater fidelity or reliability.

For decisions involving the use of climate model projections, you may want to consider seeking expertise.

The MACA archive contains output from 20 global climate models of the Coupled Model Inter-Comparison Project Phase 5 (CMIP5) for the contiguous United States. Downloaded tools provide users with access to projections for historical (observed) forcings  from 1950–2005, and for two future Representative Concentration Pathways (RCP 4.5 and RCP8.5) scenarios for 2006–2100.

CMIP5 output is downscaled from native resolutions to either 4-km or 6-km using the Multivariate Adaptive Constructed Analogs (MACA) method. MACA is a statistical downscaling method that utilizes a training dataset (i.e., meteorological observations) to remove historical biases and match spatial patterns in climate model output.

The MACA dataset offers 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 humidity 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

Users can download data directly from the archive using OPeNDAP, or use tools available on the site to download multiple files at once.

Difficulty Scale
Advanced
Tool URL
http://maca.northwestknowledge.net/
Partners
Climate Impacts Research Consortium
Northwest Knowledge Network
U.S. Department of the Interior | Northwest Climate Science Center
Regional Approaches to Climate Change
University of Idaho
Tool Function
Visualize Climate Projections
Climate Resilience Toolkit home link