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ClimateEU: historical and projected climate data for Europe

Prec12On this page you can download the ClimateEU software and approximately 3,000 climate grids at 1 km resolution (Albers Equal Area projected) or ~2km resolution (75 arcsecond, unprojected). The software can further generate time series data for historical climate (1901-2020) in monthly, annual, decadal, and 30-year steps and projected future climate (2020s, 2050s, 2080s) based on CMIP6 multi-model projections.

The database includes monthly base variables (Tmin, Tmax, Tave & Prec) as well as economically or biologically relevant (bioclim) variables such as growing and chilling degree days, beginning and end of frost free period, heating and cooling degree days, Hargrave's climate moisture deficit and reference evaporation, precipitation as snow, and seasonal variables.

The climate grids were developed with a deep neural network that uses geographic and atmospheric information to model local weather patterns. Click on image on the right ( Zoom tool ) to see a higher resolution version, with the inset showing precipitation induced by orographic lift and rain shadows in the Alps region.

Subsequently, the ClimateEU software downscales the grids to higher resolutions with a digital elevation model in conjunction with local environmental lapse-rates. The software can also provide scale-free point estimates of climate variables for user-provided latitude, longitude and elevation coordinates.

 
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 and references
 

This software can be used to query climate variables for any location, elevation and time period across Europe. The program does not require installation. Download, unzip, and double click the executable file ClimateEU.exe. The program should run on all versions of Windows. If you receive the error message "COMCTL32.OCX 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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ClimateEU v5.01a - covers Europe west of ~44 degree longitude and includes CMIP6 projections corresponding to IPCC Assessment Report 6 (2021) and CRU-TS 4.05 historical data from 1901 to 2020 (use if you need historical coverage prior to 1950).
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ClimateEU v5.01b - covers Europe west of ~44 degree longitude and includes CMIP6 projections corresponding to IPCC Assessment Report 6 (2021) and ERA5-Land historical data from 1950 to 2024 (use if you want up-to-date historical data).
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Legacy download: ClimateEU v4.63 - includes CMIP5 projections corresponding to IPCC Assessment Report 5 (2013) and CRU-TS 4.05 historical data from 1901 to 2020, with the corresponding CMIP5 gridded dataset available here.
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Legacy download: 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.

We are working on publishing a research note that explains new methods and datasets underlying ClimateEU v5. Meanwhile you can cite as follows: "Climate data has been generated with the ClimateEU v5.00 softwarepackage, available at http://tinyurl.com/ClimateEU, based on methodology similar to Mahoney et al. (2022) and Marchi et al. (2020)."

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Mahoney, C.R., Wang, T., Hamann, A., Cannon, A.J. 2022. A CMIP6 ensemble for downscaled monthly climate normals over North America. International Journal of Climatology 42: 5871-5891.
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Marchi, M., Castellanos-Acuna, D., Hamann, A., Wang, T., Ray, D. Menzel, A. 2020. ClimateEU, scale-free climate normals, historical time series, and future projections for Europe. Scientific Data 7: 428. doi: 10.1038/s41597-020-00763-0

 

 


Gridded data at ~2km resolution (75 arcsec, unprojected)


Help file
ClimateEU input file
Elevation, ID reference
Area covered
Usage, variables, grid generator: CSV format 75 arcsec CSV: CSV format 75 arcsec geoTIFF: ASCII format Shapefile: ASCII format

75 arcsec resolution historical 30-year climate normal periods
1940s (1931-1960) Bioclim: CSV format, Monthly: CSV format 1950s (1941-1970) Bioclim: CSV format, Monthly: CSV format 1960s (1951-1980) Bioclim: CSV format, Monthly: CSV format
1970s (1961-1990) Bioclim: CSV format, Monthly: CSV format 1980s (1971-2000) Bioclim: CSV format, Monthly: CSV format 1990s (1981-2010) Bioclim: CSV format, Monthly: CSV format
2000s (1991-2020) Bioclim: CSV format, Monthly: CSV format (as a representation of current climate use the SSP2-2020s* projection below)

Average ensemble scenarios1
27 Bioclimatic variables
48 Monthly variables
SSP1 (+2.6 W/m2) - Sustainability focus2 2020s3: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
SSP2 (+4.5 W/m2) - Middle of the road 2020s*: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s*: CSV format, 2050s: ASCII format, 2080s: ASCII format
SSP3 (+7.0 W/m2) - Regional rivalry 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
SSP5 (+8.5 W/m2) - Fossil-fuel focus 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 8 CMIP6 models (ACC, CNRM, EC, GFDL, GISS, MIR, MPI and MRI) that were chosen
     based on a variety of quality criteria and representativeness. For details on GCM selection, see Mahoney et al. (2022).
   2) Average projected global warming by the 2080s for different Shared Socioeconomic Pathways (SSP) scenarios:
       SSP1-2.6: 1.5-2.0°C; SSP2-4.5: 2.5-3.0°C; SSP3-7.0: 3.5-4.0°C; SSP5-5.8: 4.0-5.0°C.
   3) Projections for 30-year normal periods: 2020s: 2011-2040, 2050s: 2041-2070, 2080s: 2071-2100.
    *) SSP2-2020s is a good choice to represent current climate (midpoint of "middle of the road" climate normal estimate).

 


Gridded data at 1km resolution (Albers Equal Area Conic projection)


Help file
ClimateEU input file
Elevation, ID reference
Area covered
Usage, variables, grid generator: CSV format 1 km CSV: CSV format 1 km geoTIFF: ASCII format Shapefile: ASCII format

1km resolution historical 30-year climate normal periods
1940s (1931-1960) Bioclim: CSV format, Monthly: CSV format 1950s (1941-1970) Bioclim: CSV format, Monthly: CSV format 1960s (1951-1980) Bioclim: CSV format, Monthly: CSV format
1970s (1961-1990) Bioclim: CSV format, Monthly: CSV format 1980s (1971-2000) Bioclim: CSV format, Monthly: CSV format 1990s (1981-2010) Bioclim: CSV format, Monthly: CSV format
2000s (1991-2020) Bioclim: CSV format, Monthly: CSV format (as a representation of current climate use the SSP2-2020s* projection below)

Average ensemble scenarios1
27 Bioclimatic variables
48 Monthly variables
SSP1 (+2.6 W/m2) - Sustainability focus2 2020s3: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
SSP2 (+4.5 W/m2) - Middle of the road 2020s*: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s*: CSV format, 2050s: ASCII format, 2080s: ASCII format
SSP3 (+7.0 W/m2) - Regional rivalry 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
SSP5 (+8.5 W/m2) - Fossil-fuel focus 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format 2020s: CSV format, 2050s: ASCII format, 2080s: ASCII format
   For notes, see table above.

 

 
Scenario selection to quantify uncertainty for different Counties and IPCC regions of Europe

If you want to quantify uncertainty in future projections, you need to work with a selection of multiple, individual models. This is easily done with the provided software for locations of interest (see video tutorial above), the pre-selected grids downloadable below, or it is also fairly straight-forward to generate custom grids with the help of some R code (included in the help file) if your data needs are different.

To help with the selection of a representative set of models for different regions of Africa (or for the entire continent), we used the Katsavounidis-Kuo-Zhang (KKZ) algorithm which selects an optimally representative set of future projections for different IPCC climate reference regions considering multiple climate variables (click on left panel dendrogram to visualize similarity among models):

aogcm clusters     IPCCregions

A common approach is to select a median, a pessimistic and an optimistic projection, i.e., a subset size of 3. For example, to assess uncertainty in the United Kingdom and Irlend region (uk/ie) with a 3-model ensemble, you would choose the models: CNRM (median), ACC (pessimistic), and MPI (optimistic) from the table below (compare with the dendrogram above). Adding additional models (rows 4 to 8) will provide increasingly better representation of uncertainty in future predictions in multivariate space.

To include the sensitive "outlier" model UKES in the model selection process, use the lower portion of the table. If UKES is included, this scenario will almost always be picked second, as the most "pessimistic" model of global warming. UKES is not a very likely outcome, but if you work with a larger ensemble, you may include it as one possible outcome. For details on GCM selection process, see Mahoney et al. (2022).

Subset size
IPCC climate reference region (upper case) / groups of countries (lower case)
Europe
NEU
WCE
MED
uk/ie
pt/es
fr/bl/lu
nl/dk/de
no/se/fi
ch/at
eu-ne
eu-se
Excluding UKESM1-0-LL (recommended for smaller ensembles with up to 5 models)
1 CNRM GISS MRI CNRM CNRM GISS GISS CNRM MRI MRI CNRM CNRM
2 EC EC ACC ACC ACC ACC EC EC ACC EC EC EC
3 MPI ACC MPI MPI EC EC ACC MPI MPI ACC ACC MPI
4 GFDL MPI EC EC MPI GFDL GFDL GFDL EC MPI MPI ACC
5 MIROC GFDL GFDL GISS MRI MIROC MPI MRI GFDL MIROC GFDL GFDL
6 ACC CNRM CNRM MRI MIROC MPI CNRM MIROC GISS GFDL MRI MIROC
7 MRI MIROC MIROC GFDL GFDL MRI MIROC ACC CNRM CNRM GISS GISS
8 GISS MRI GISS MIROC GISS CNRM MRI GISS MIROC GISS MIROC MRI
Including UKESM1-0-LL (recommended when using 6 to 13 models)
1 CNRM GISS MRI CNRM CNRM GISS GISS CNRM MRI CNRM MRI CNRM
2 UKES UKES UKES UKES UKES UKES UKES UKES UKES UKES UKES UKES
3 EC EC MPI MPI EC EC EC MPI MPI EC MPI EC
4 MPI ACC ACC EC MRI ACC ACC EC ACC MPI EC MPI
5 MIROC GFDL EC GISS MPI MIROC GFDL GFDL EC ACC GISS ACC
6 GFDL MPI GFDL ACC ACC GFDL MPI MIROC GISS MIROC ACC MIROC
7 ACC MIROC CNRM MRI MIROC MRI CNRM MRI GFDL GFDL MIROC GFDL
8 MRI CNRM MIROC MIROC GFDL MPI MIROC ACC MIROC MRI CNRM GISS
9 GISS MRI GISS GFDL GISS CNRM MRI GISS CNRM GISS GFDL MRI
   1) IPCC region abbreviations are: NEU, Northern Europe; WCE, West-Central Europe, MED, Mediteranean. Other groups: uk/ie, UK, Ireland;
       fr/bl/lu: France, Belgium, Luxembourg; pt/es, Portugal, Spain; nl/dk/de, Netherlands, Denmark, Germany; no/sw/fi, Norway, Sweden, Finland;
       ch/at, Switzerland/Austria; eu-ne, northeastern European contries - Baltic to Romania; eu-se, southeastern European countries - Italy to Bulgaria;
   2) Model abbreviations are: ACC, ACCESS-ESM1-5; CNRM, CNRM-ESM2-1; EC, EC-Earth3; GFDL, GFDL-ESM4; GISS, GISS-E2-1-G; MIR, MIROC6;
       MPI, MPI-ESM1-2-HR; MRI, MRI-ESM2-0; and UKES, UKESM1-0-LL.



Below, you can find a table with individual scenario downloads for uncertainty analysis. This is not the full set of SSPs is not typically necessary for a meaningful uncertainty analysis. The selection below will be suitable to quantify medium term (2050s) and long term (2080s) uncertainty in projections, as well as the dependence of outcomes on two SSPs.

 
Individual GCM downloads for uncertainty analysis

[currently under development - check back in July]

AOGCM
SSP2 (+4.5 W/m2) - Middle of the road
SSP5 (+8.5 W/m2) - Fossil-fuel focus
27 Bioclimate variables
48 Monthly variables
27 Bioclimate variables
48 Monthly variables
ACCESS-ESM1-5 (ACC) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
CNRM-ESM2-1 (CNRM) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
EC-Earth3 (EC) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
GFDL-ESM4 (GFDL) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
GISS-E2-1-G (GISS) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
MIROC6 (MIR) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
MPI-ESM1-2-HR (MPI) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
MRI-ESM2-0 (MRI) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format
UKESM1-0-LL (UKES) 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format 2050s: ASCII format, 2080s: ASCII format

 
Acknowledgements
 

This research has, in part, been sponsored by the Alexander von Humboldt foundation and an NSERC Discovery Grant.