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ClimateEU: historical and projected climate data for Europe

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.

The reference climate grids are based on the Parameter Regression of Independent Slopes Model (PRISM) interpolation method for precipitation, and ANUSplin for temperature. Historical data since 1901-2009 are based on the CRU-TS 3.1 dataset (Mitchell & Jones, 2005, Int J Climatol 25: 693-712) and has been updated to 2013 with CRU-TS 3.22. 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 the highest validation statistics in their CMIP3 equivalents.

 
Video tutorials
 

Get started with these two video-tutorials. This first video explains the basic functionality of the software (1) interactive query of climate for locations, (2) processing spreadsheets of locations, (3) generating time series of climate, and (4) basic processing of gridded data. The second video explains in detail how to generate continental scale climate grids in projected coordinate systems, and how to automate generation of multiple climate surfaces for a variety of climate variables, historical time periods and future projections.

Tutorial 1: Learn the basic operation of the software Tutorial 2: Learn how to mass-produce continental climate grids in a projection of your choice

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Refererence files used on the tutorial above: 2.5km digital elevation model, outline of countries, and coastline mask in Albers projection.

 
Software download Links
 

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).

Note that this is a beta version of ClimateEU that still undergoes some improvements to the code and model calibration. Please proceed with appropriate caution, and report any errors or suspicious estimates to help improve the work (andreas.hamann@ualberta.ca). Should we discover any major errors, a log of changes will be posted here.

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ClimateEU v4.63 - covers Europe west of ~63 degree longitude
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Legacy CMIP3 multimodel future projections corresponding to IPCC Assessment Report 4 (2007). To use them, unzip the archive and place the .gcm files into the GCMdat folder of ClimateEU.

 
Download links for CMIP5-based climate data (2.5km resolution)
 

This dataset was created with the ClimateEU software package by Maurizio Marchi from the Council for Agricultural Research and Economics - Forestry Research Centre (CRA-SEL) of Arezzo (Italy) under the Trees4Future project. 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) for future projections corresponding to the 5th IPCC Assessment Report (2013).

36 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.


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

This dataset was created with the ClimateEU software package by Maurizio Marchi from the Council for Agricultural Research and Economics - Forestry Research Centre (CRA-SEL) of Arezzo (Italy) under the Trees4Future project. 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) for future projections corresponding to the 5th IPCC Assessment Report (2013). Download and unzip with http://sourceforge.net/projects/sevenzip/

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

Average ensembles1
36 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
36 Bioclimate variables
48 Monthly variables
CCSM4, USA RCP4.5 2020s3: 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.



 
References
 

Note that the ClimateEU package has not undergone peer-review yet. In the interim, reference usage like this: "Climate data has been generated with the ClimateEU v4.63 softwarepackage, available at http://tinyurl.com/ClimateEU, based on methodology described by Hamann et al. (2013)."

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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 Society 94: 13071309.
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Wang, T., Hamann, A., Spittlehouse, D.L. and Murdock, T.Q. 2012. ClimateWNA - High-resolution spatial climate data for western North America. Journal of Applied Meteorology and Climatology 51: 16-29
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Mbogga, M., Hamann, A. and T. L. Wang. 2009. Historical and projected climate data for natural resource management in western Canada. Agricultural and Forest Meteorology 149: 881-890.
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Wang, T., Hamann, A. Spittlehouse, D. L., and Aitken, S. N. 2006. Development of scale-free climate data for western Canada for use in resource management. International Journal of Climatology 26: 383-397. .
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Hamann, A., and T. L. Wang. 2005. Models of climatic normals for genecology and climate change studies in British Columbia. Agricultural and Forest Meteorology 128: 211-221.

 
Acknowledgements
 

This research has, in part, been sponsored by the Alexander von Humboldt foundation.