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Historical and projected climate data for North America (ClimateNA)

The software, downloadable from this web page, can be used to estimate more than 50 monthly, seasonal, and annual variables, including many economically or biologically relevant variables such as growing and chilling degree days, heating and cooling degree days, Hargrave's moisture deficit and reference evaporation, beginning and end of the frost-free period, etc.

Click on the thumbnails below and use the zoom tool Zoom to see high resolution images of mean annual temperature (MAT), mean winter temperature with inversions in northern mountain valleys (MWT), mean annual preciptiation with leeward rainshadows (MAP), and a climate moisture index (CMI):

MAT TaveDJF MAP CMI

The reference climate grids are based on the Parameter Regression of Independent Slopes Model (PRISM) interpolation method for current climate. Historical data since 1901 are based on the CRU-TS 3.22 dataset (Mitchell & Jones, 2005). Future projections are based on 15 AOGCMs of the CMIP5 multimodel dataset corresponding to the IPCC Assessment Report 5 (2013) x 2 Emission Scenarios (RCP4.5 and RCP8.5) x 3 standard time-slices (2020s, 2050s, 2080s). Average projected global warming increase (and likely range) for RCP4.5 are: +1.4°C (±0.5) by the 2050s; +1.8°C (±0.7) by the 2080s. For RCP8.5 they are: +2.0°C (±0.6) by the 2050s; +3.7°C (±0.9) by the 2080s.

The 15 AOGCMs are CanESM2, ACCESS1.0, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, CCSM4, HadGEM2-ES, CNRM-CM5, CSIRO Mk 3.6, GFDL-CM3, INM-CM4, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, GISS-E2R and were chosen to represent all major clusters of similar AOGCMs by Knutti et al (2013). Within clusters, we selected models that had high validation statistics in their CMIP3 equivalents.

 
Downloads and References
 

This program does not require installation. Download, unzip, and double click the executable file ClimateAB.exe. The program should run on all versions of Windows. If you receive the error message "COMCTL32.OSX missing", you have to install these libraryfiles. The program also runs on Linux, Unix and Mac systems with the free software Wine or MacPorts/Wine).

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ClimateNA v5.21 - covers North America and includes AR5/CMIP5 future scenarios and CRU-TS3.22 historical data.

To receive updates on major corrections or new releases, you can add yourself to the ClimateNA Mailing List.

We suggest the following wording to reference data usage: "Climate data has been generated with the ClimateNA v5.10 softwarepackage, available at http://tinyurl.com/ClimateNA, based on methodology described by Wang et al. (2016)."

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Wang, T., Hamann, A. Spittlehouse, D.L. and Carroll, C. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One 11: e0156720.

 
Download links for CMIP5-based climate data (1km resolution)
 

This dataset was created by Carlos Carroll, David Roberts, and Andreas Hamann under the AdaptWest project, funded by the Wilburforce Foundation. It is based on the Parameter Regression of Independent Slopes Model (PRISM) interpolation method for current climate, and the Coupled Model Intercomparison Project phase 5 (CMIP5) database corresponding to the 5th IPCC Assessment Report (2013). Download and unzip with http://sourceforge.net/projects/sevenzip/

27 Bioclimate variables
(1961-1990 normal period)
48 Monthly variables
(1961-1990 normal period)
Reference files:
Elev, ID, Boundary
Meta data:
Projection, Variables
ASCII: ASCII format ASCII: ASCII format ASCII, Shape: ASCII format Readme: CSV format, ESRI: ASCII format

Average ensembles1
27 Bioclimatic variables
48 Monthly variables
RCP4.5 emission scenario2 2020s3: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
RCP8.5 emission scenario 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
   1) The ensemble projections are averages across 15 CMIP5 models (CanESM2, ACCESS1.0, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, CCSM4,
      HadGEM2-ES, CNRM-CM5, CSIRO Mk 3.6, GFDL-CM3, INM-CM4, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, GISS-E2R) that were chosen
      to represent all major clusters of similar AOGCMs (Knutti et al 2013), and that had high validation statistics in their CMIP3 equivalents.
   2) Average projected global warming increase and likely range for RCP4.5: +1.4°C (±0.5) by the 2050s; +1.8°C (±0.7) by the 2080s; RCP8.5: +2.0°C (±0.6)
      by the 2050s; +3.7°C (±0.9) by the 2080s.
   3) 2020s: average for years 2011-2040, 2050s: 2041-2070, 2080s: 2071-2100.


AOGCM name, country of origin1
Emission scenario2
27 Bioclimate variables
48 Monthly variables
CCSM4, USA RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
CNRM-CM5, France RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
CanESM2, Canada RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
GFDL-CM3, USA RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
HadGEM2-ES, UK RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
INM-CM4, Russia RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
IPSL-CM5A-MR, European Union RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
MPI-ESM-LR, Germany RCP4.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
  RCP8.5 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
   1) This is a selection of eight individual models to represent all major clusters of similar AOGCMs (Knutti et al 2013), and that had high validation statistics
      in their CMIP3 equivalents.
   2) Average projected global warming increase and likely range for RCP4.5: +1.4°C (±0.5) by the 2050s; +1.8°C (±0.7) by the 2080s; RCP8.5: +2.0°C (±0.6)
      by the 2050s; +3.7°C (±0.9) by the 2080s.
   3) 2020s: average for years 2011-2040, 2050s: 2041-2070, 2080s: 2071-2100.



 
Model selection
 

Selecting scenarios for climate change impact and adaptation research is a complex task. We selected eight individual models to represent all major clusters of similar AOGCMs (Knutti et al 2013) with high validation statistics in their CMIP3 equivalents. To further whittle the number of AOGCMs under consideration down, researchers often select "worst case", "best case", and "median" climate change projections. However, what constitutes a worst case or best case scenario, differs by region and by the climate variable of interest. The table below may help with scenario selection for states and provinces within western North America. We also provide ensemble scenarios for download above, but they may have unrealistic combinations of individual climate variables.

Mean annual temperature change for states and provinces of North America projected for the 2050s under the RCP4.5 scenario. States and provinces are alphabetically sorted from left to right, AOGCMs are sorted by magnitude of projection for North America from top to bottom. MAT Selection

Mean annual precipitation change for states and provinces of North America projected for the 2050s under the RCP4.5 scenario. States and provinces are alphabetically sorted from left to right, AOGCMs are sorted by magnitude of projection for North America from top to bottom. MAP Selection



 
Previous CMIP3-based climate data (4km resolution)
 

For a 4km resolution dataset for North America based on the Coupled Model Intercomparison Project phase 3 (CMIP3) database corresponding to the 4th IPCC Assessment Report (2009), follow this link.