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Current and projected climate data for western North America (ClimateWNA)
Current and projected climate data for western North America (ClimateWNA)
ClimateWNA - Current and projected climate data for western North America
This dataset has been prepared for the Climate Adaptation Conservation Planning Database for Western North America (CACPD) 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 3 (CMIP3) database corresponding to the 4th IPCC Assessment Report for future projections. For further information and citation refer to:
Hamann, A. and Wang, T., Spittlehouse, D.L., and Murdock, T.Q. 2013. A comprehensive, high-resolution database of historical and projected climate surfaces for western North America. Bulletin of the American Meteorological Society94: 1307–1309.
As an alternative to accessing interpolated climate data in gridded data formats from the table below, we also provide a software solution to query climate data for a series of sample points of interest. The programs should run on Windows 9x/NT/2000/XP/Vista/7 without an installation on most systems.
The programs also runs on Linux, Unix and Mac systems with the free software Wine or MacPorts/Wine)
ClimateWNA v4.62 - covers North America west of -100 degree longitude (includes AR4/CMIP3 future scenarios and CRU-TS3.1 historical data)
Click on the thumbnails below and use the zoom tool to see high resolution images of mean annual temperature (MAT), mean minimum January temperature with inversions in northern mountain valleys (Tmin01), mean summer preciptiation with leeward rainshadows (MSP), precipitation as snow (PAS), and Hargrave's climate moisture deficit (CMD):
Data coverage and variables
The gridded climate layers downloadable below are in Lambert Conformal Conic projection, at 1km resolution, and covering North America, west of 100° longitude. The database consists of 11 million grid cells and is designed to capture climate gradients, temperature inversions, and rain shadows in the mountenous landscape of western North America.
There are two data formats available: All variables in a single comma seprated value file, where rows represent grid cell locations, and columns represent variables (CSV). CSV files can be read, for example, by R, SAS, or Excel. The other format are ESRI grids, a native format of ArcGIS software, but compatible with many other GIS applications (ASCII).
Two sets of variables are available for download. One comprises 24 biologically relevant variables, including seasonal and annual means, extremes, growing and chilling degree days, snow fall, potential evapotranspiration, and a number of drought indices. The second dataset consists of 36 monthly temperature and precipitation variables.
Download links for climate data (1km resolution)
24 Bioclimate variables (1961-1990 normal period)
36 Monthly variables (1961-1990 normal period)
Reference files:
Elev, ID, Boundary
Meta data: Projection, Variables
CSV: , ASCII:
CSV: , ASCII:
CSV: , ASCII:
Readme: , ESRI:
AOGCM name (country of origin, original resolution,
AOGCM code or ΔTemp/ΔPrec for ensembles)
Emission scenario2
Future period3
24 Bioclimate variables
36 Monthly variables
CSIRO Mk3.0 (Australia, 1.87°x1.87°)
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
CGCM3.1 T47 (Canada, 3.75°x3.75°, CCCMA_CGCM_3_1)
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
ECHAM5 (Germany, 1.87°x1.87°, MPI_ECHAM5)
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
IPSL-CM4 (France, 3.75°x2.50°)
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
NCAR-CCSM3 (USA, 1.40°x1.40°)
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
UKMO-HadGEM3.1 (UK, 1.87°x1.24°)
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
MIROC3.2 hires (Japan, 1.12°x1.12°)
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
CSIRO Mk3.0 (Australia, 1.87°x1.87°)
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
CGCM3.1 T47 (Canada, 3.75°x3.75°, CCCMA_CGCM_3_1)
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
ECHAM5 (Germany, 1.87°x1.87°, MPI_ECHAM5)
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
IPSL-CM4 (France, 3.75°x2.50°)
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
NCAR-CCSM3 (USA, 1.40°x1.40°)
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
UKMO-HadGEM3.1 (UK, 1.87°x1.24°)
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
MIROC3.2 hires (Japan, 1.12°x1.12°)
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
CSIRO Mk3.0 (Australia, 1.87°x1.87°)
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
CGCM3.1 T47 (Canada, 3.75°x3.75°, CCCMA_CGCM_3_1)
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
ECHAM5 (Germany, 1.87°x1.87°, MPI_ECHAM5)
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
IPSL-CM4 (France, 3.75°x2.50°)
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
NCAR-CCSM3 (USA, 1.40°x1.40°)
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
UKMO-HadGEM3.1 (UK, 1.87°x1.24°)
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
MIROC3.2 hires (Japan, 1.12°x1.12°)
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
B1
2020s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
B1
2050s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
B1
2080s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
A1B
2020s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
A1B
2050s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
A1B
2080s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
A2
2020s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
A2
2050s
CSV: , ASCII:
CSV: , ASCII:
Ensemble of all 23 CMIP3 AOGCMs
A2
2080s
CSV: , ASCII:
CSV: , ASCII:
1) Validation rank according to Strahlberg et al. (2013) Unpublished manuscript 2) B1: low emssion scenario, A1B: moderate emission scenario, A2: high emssion scenario 3) 2020s: average for years 2011-2040, 2050s: 2041-2070, 2080s: 2071-2100,
How to select future climate scenarios
Selecting scenarios for climate change impact and adaptation research is a complex task. Not all future projections have equal resolution, number of runs, and validation statistics against past climate. We selected a representative set of 7 AOGCMs from 14 high-quality models, from a total of 23 CMIP3 AOGCMs.
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 plot on the right may help with scenario selection for the entire study area, and the table below for scenario selection for regions within western North America. We also provide ensemble scenarios for download above, but they may have unrealistic combinations of individual climate variables.