DOI: 10.5281/zenodo.14843328
title: FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution

description: Overview

This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened. 

Resolution: 30x30 arcsecProjection: EPSG 4326Extent for historical data: 10.67°W – 47.67°E, 33.68°N – 71.33°NExtent for scenario data: 10.67°W – 39.33°E, 33.68°N – 71.33°NPeriods for historical data: 1961-1990 and 1991-2020Periods for scenario data: 2036-2065 and 2071-2100Format: GeoTIFF

List of climatologies (climate indicators)  






#



Short name



Name



Description



Unit



Yearly (Y) or monthly (M)





1



tasmin



Average daily minimum temperature



Arithmetic mean



°C



Y, M





2



tasmax



Average daily maximum temperature



Arithmetic mean



°C



Y, M





3



tas



Average temperature



Arithmetic mean



°C



Y, M





4



tas_warmest_month



Average temperature mean in the warmest month



Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month



°C



Y





5



tas_coldest_month



Average temperature mean in the coldest month



Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month



°C



Y





6



tasmin_coldest_month



Average temperature minimum in the coldest month



Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month



°C



Y





7



tasmax_warmest_month



Average temperature maximum in the warmest month



Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month



°C



Y





10



GSL



Average length of the growing season



The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days



days



Y





12



GDD



Average Growing Degree Days per year above 5°C



Σ(Tmean – 5°C) per year. 



°C



Y





13



FD_first



Average date of the first frost occurrence



Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate



day of year



Y





14



FD_last



Average date of the last frost occurrence



Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate



day of year



Y





20



GLO_hori



Average sum of global radiation



 



kWh



Y, M





33



pr



Average precipitation sum



 



mm



Y, M





39



ET0



Average annual potential evapotranspiration



Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm



mm



Y, M





40



WBAL



Average climatic water balance



Precipitation minus potential evapotranspiration



mm



Y, M





Data sources

The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020. 



CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016

CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)

ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020

v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.


Climate models from EURO-CORDEX (doi.org/10.1007/s10113-013-0499-2):



MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17

MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17

ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4
description: The following daily meteorological data were generated in order to calculate the 30-year averaged climate indicators: 

a) Temperature, Precipitation and Global Radiation

For variables included in CHELSA-W5E5 v1.1, a statistical bias adjustment of ERA5-Land was carried out in order to obtain a homogeneous data set. The correction was made using 1981-2010 as the observation period with the CHELSA-W5E5 v1.1 data using the "Quantile Delta Mapping" (QDM) methodology (doi.org/10.1175/JCLI-D-14-00754.1). Update for v2.0: To further reduce the mean error, especially in mountainous regions, differences in monthly climatological minimum, maximum, and average temperature to WorldClim version 2.1 were added. Thus, the WorldClim reference period 1971-2000 temperature climatologies are perfectly matched. 

b) Wind Speed

Only climatological monthly averages of wind speed are available in CHELSA. An approach was chosen in which the temporal variability and trends of ERA5-Land are preserved and the climatological means of CHELSA V2.1 are reproduced:



Calculation of climatological monthly means for the period 1981-2010 for ERA5-Land

Calculation of the ratio of CHELSA climatology to ERA5-Land climatology for each of the 12 months. 

Multiplication of the daily values with the climatological ratio of the CHELSA climatology to the ERA5-Land climatology.


c) Vapor Pressure Deficit

Only climatological monthly averages are available in CHELSA. However, validation of the vapor pressure deficit of CHELSA V2.1 in Austria showed that the values deviate significantly from the measured values. Therefore, an approach had to be chosen in which not only the trends of ERA5-Land are retained, but also spatial mean values, as these are closer to the measured values in Austria. This means that only the small-scale spatial variability of CHELSA is largely retained. This required the following steps:



Conversion of the dew point of ERA5-Land into the vapor pressure deficit using temperature

Calculation of climatological monthly means of the vapor pressure deficit for the period 1981-2010 for ERA5-Land

Calculation of the ratio of CHELSA climatology to ERA5-Land climatology for each of the 12 months. 

Smoothing of the ratio with a running mean in both spatial dimensions with a centered window of size 0.1°. This brings the ratio of the two climatologies approximately to the real resolution of ERA5-Land. 

Multiplication of the daily values by the climatological ratio of the CHELSA climatology to the ERA5 land climatology, divided by the smoothed ratio.


d) Scenario Data 

Regional climate models were selected from EURO-CORDEX. The scenario data was bias adjusted and downscaled using the approaches described in doi.org/10.5194/ascmo-9-29-2023. The historical 30-year climate period 1991-2020 was selected for bias adjustment. The temperature (minimum, mean, maximum) and humidity (dew point) are adjusted additively. For all other variables (wind speed, global radiation, precipitation) the correction was carried out multiplicatively.  
description: "yr" in the file names refers to climate indicators for the whole year

["01"..."12"] in the file names refers to climate indicators for months
description: Changes in v2.0:

 - Shift of monthly climatological minimum, maximum, and average temperature (delta approach) to WorldClim version 2.1, which reduced the mean errors, especially in mountainous regions. The previous version had a pronounced warm bias in some Alpine valleys. (see also https://doi.org/10.5194/ems2024-582, 2024.)