MACA Statistically Downscaled Climate Data from CMIP5
A new statistically downscaled climate model dataset covering the conterminous U.S. 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.
The Multivariate Adaptive Constructed Analogs (MACA) (Abatzoglou & Brown 2012) 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). The dataset includes the following variables:
- Maximum & minimum temperature
- Precipitation amount
- Maximum & minimum relative humidity - the amount of moisture in the air compared to what the air can ‘hold’ at that temperature.
- Specific humidity - the ratio of the mass of water vapor in the air to the total mass of air
- Downward shortwave solar radiation - shortwave energy from the Sun that reaches the land-surface
- Eastward & northward wind
We are currently dispensing 3 data products: MACAv1-METDATA, MACAv2-METDATA and MACAv2-LIVNEH.
- 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 2012.
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.