MACA Information


The Multivariate Adaptive Constructed Analogs(MACA)(Abatzoglou, Brown, 2011) 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.

CMIP5 Experiments

In this section, the CMIP5 historical and future RCP experiments are described briefly.

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CMIP5 GCMs used with MACA

In this section, some details on the global climate models (GCMs) used by MACA from the CMIP5 project are given.

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.

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Training Datasets used in the MACA Products

In this section, the training datasets used in the MACA products are described. Specifically, we have used gridMET (Abatzoglou) and the Livneh historical meteorological datasets.

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MACA Statistical Downscaling of Climate Data

In this section, the details of the MACA statistical downscaling method are described through 1) short videos, 2) step-by-step descriptions and 3) comparisons of MACA version 1 and 2.

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MACA Statistical Downscaling of Climate Data

In this section, the different MACA products are compared in a table to look at 1) resolution, 2) training datasets used, 3) spatial extent, 4) dataset size.

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MACA Licenses and References

In this section, the license to use the MACA data is stated and references for the MACA data or other publications of interest are given.

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