Data Spotlight: New Statistically Downscaled Climate Data Available for the Conterminous U.S.

Release Date:

A new statistically downscaled climate model dataset covering the conterminous U.S. is now available for download in the USGS GeoData Portal.

Person walking through tall grasses in marsh landscape, carrying field equipment of their shoulder

Doing fieldwork in a Pacific Northwest marsh

(Public domain.)

A new statistically downscaled climate model dataset covering the conterminous U.S. is now available for download in the USGS GeoData Portal. This dataset is called MACAv2-METDATA and it contains daily downscaled meteorological and hydrological projections for the conterminous U.S. at 4-km resolution. The dataset includes the following variables:

  1. Maximum & minimum temperature
  2. Precipitation amount
  3. Maximum & minimum relative humidity- the amount of moisture in the air compared to what the air can ‘hold’ at that temperature.
  4. Specific humidity- the ratio of the mass of water vapor in the air to the total mass of air
  5. Downward shortwave solar radiation- shortwave energy from the Sun that reaches the land-surface
  6. Eastward & northward wind

Here’s what you need to know:

What is Statistical Downscaling?

Statistical downscaling is one of two methods (the other is dynamical downscaling) that uses climate data produced at a large scale (such as global) to make predictions about future climate at a smaller scale (such as a particular watershed).  The downscaling process generates information that is useful for making decisions and adapting to the impacts of climate change on a local or regional scale. A number of statistical downscaling methods exist, one of which is MACA.

What is MACA?

MACA stands for ‘Multivariate Adaptive Constructed Analogs’ (Abatzoglou and Brown, 2012) and is a new method for downscaling Global Climate Models (GCMs). There are several types of GCMs, and MACA used model outputs from the Coupled Model Inter-comparison project (CMIP5). The method also requires the use of a training dataset— an observational dataset of the variables, downscaled to a smaller resolution. This product used METDATA (Abatzoglou, 2011) as a training dataset, a meteorological dataset at 4-km resolution. The benefits of MACA include the fact that it provides a number of key meteorological variables and that it allows for the consideration of extreme climate events.

How can this data be used?

This dataset can be used to predict future climate conditions at local and regional scales throughout the conterminous United States. Once conditions are predicted, vulnerable areas can be identified and prioritized for adaptation efforts. This dataset represents an important step towards predicting future climate scenarios in the U.S. at scales that are important to resource managers.

How can this data be accessed?

This dataset can be downloaded from the USGS GeoData Portal (GDP). The GDP houses large datasets, often the products of large-scale modeling efforts such as climate downscaling, and makes these datasets easier for scientists, managers, and the public to access and process the information for additional analyses.

Additional Documentation:
Documentation Home:
Tutorial on how to access the data in the USGS GDP
Tutorial on how to access the data with Python and R

The development of this dataset was funded in part by the Northwest Climate Science Center and the Southeast Climate Science Center, both managed by the USGS National Climate Change and Wildlife Science Center.