Monitoring and management of several environmental and socioeconomic sectors require climate data that can be summarized using a set of standard and meaningful climate metrics. This study describes a newly developed gridded dataset for the whole of Europe, which employed a set of 125 climate indices spanning different periods based on data availability, but mainly 1950–2017 and 1979–2017. This dataset comprehensively summarizes climate variability in Europe for a wide range of climate variables and conditions, including air temperature, precipitation, biometeorology, aridity, continentality, drought, amongst others. Climate indices were computed at different temporal scales (i.e. monthly, seasonal and annual) and mapped at a grid interval of 0.25°. We intend to update these indices on an annual basis. This dataset is freely available to research and end-user communities.
computational modeling technique
air temperature • precipitation • sea level pressure • wind at 10 m • wind gust at 10 m • TOA radiation • radiation • insolation • snow density • snow depth • snowfall • total cloud cover • low cloud cover
Sample Characteristic - Environment
Sample Characteristic - Location
Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12029427
Background & Summary
Climate processes are complex and therefore hard to monitor and quantify.This is mainly due to the different environmental and socioeconomic impacts of climate, which pose further challenges to climate quantification and assessment1,2,3. Although climate observations usually correspond to quantitative variables with a comprehensible physical meaning, the environmental, societal and economic impacts, including a number of sectors, usually depend on specific climate conditions or event characteristics4,5,6,7. For example, the cumulative climate conditions over long periods, the extreme values recorded over a period, the frequency of days with specific characteristics, the frequency of events above or below specific thresholds; amongst others, could have a direct influence on environmental and socioeconomic systems8,9,10,11. In the same context, monitoring and assessing the impact of climate is a complicated process, given that conditions that trigger specific impacts are often a result of interactions between different climate covariates. For example, bioclimatic conditions that strongly determine human health and comfort depend on a wide range of climate variables, such as air temperature, relative humidity, wind speed, etc.12,13. Similarly, while drought severity is mainly controlled by precipitation, other climate variables (e.g. atmospheric evaporative demand, potential evaporation) could contribute significantly to drought dynamics14,15,16.
Within this context, much effort has been made to develop synthetic climate indices, which can be employed to determine climate impacts on different natural systems and socioeconomic sectors17,18,19,20,21. These indices are also useful for providing a comprehensive assessment of climate variability and change processes under current and future climate change scenarios22,23,24,25. The joint Ccl/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI)26 developed an array of standard climate indices, which have widely been used to determine recent trends in climate conditions, with a particular focus on precipitation and temperature metrics. These indices have been widely used over different world regions such as Brazil27, China28,29, Africa30,31, the Mediterranean32,33, Indonesia34, North America35, central America36, central Asia37, amongst others. In addition, these indices have also been analyzed to assess the consequences of projections for future climate change scenarios. For example, Aerenson et al.38 have recently analyzed a suite of climate indices based on daily precipitation and temperature projections at the global scale, with the aim of determining possible future changes under 1.5 °C and 2 °C warming scenarios. Their study indicated that temperature indices are likely to witness significant changes in the future. On the other hand, the behavior of precipitation indices is more complex and different scenarios may cause different changes in the intensity of precipitation. Other studies (e.g. Dong et al.39) employed climate indices to verify that the observed warming trends in Asia in the past six decades were inconsistent with the natural variability of the climate system, but agreed with climate responses to external forcing, as simulated by the models.
The availability of long-term, high spatial resolution and updated climate indices could be promising for the research community through a multidecadal assessment of climate change processes and their impacts. This climate information could also be useful for a wide variety of environmental and socioeconomic sectors (e.g. land and agricultural management, climate-based health impacts, insurance plans associated with weather extremes, etc). Currently, some climate indices datasets have been developed35,40, mostly with the purpose of analyzing trends in climate conditions, focusing more on extreme meteorological events41,42. There are some worldwide updated climate indices dataset, such as i) DegDays_0p25_1970_201843, which provides indices summarizing monthly and annual cooling and heating degree –days at the global scale, ii) GHCNDEX41, which includes a wide range of the ETCCDI indices calculated at the global scale, and iii) the global drought monitoring dataset based on the standardized precipitation evapotranspiration index44. Nevertheless, in Europe, there is no updated dataset of a wide variety of climate indices that can be useful not only for climate analysis, but also for a broad sectorial impact assessment. For this reason, the objective of this study is to provide an updated gridded dataset of a large variety of climate indices that summarize the temporal and spatial variability of climate in Europe during the past seven decades (from 1950 to 2017)45. Understanding climate services as “The transformation of climate-related data – together with other relevant information – into customized products such as projections, forecasts, information, trends, economic analyses, assessments (including technology assessments), counselling on best practices, development and evaluation of solutions and any other service in relation to climate that may be of use for the society at large”46 makes this dataset a potential asset from a climate service perspective. The target beneficiaries of this dataset may include climate services providers, civil society groups, farmers, amongst different stakeholders and end-users.
To calculate the different climate indices, we employed two information sources: the European Climate Assessment & Dataset (ECA&D) E-OBS gridded dataset (https://www.ecad.eu/) and the ERA5 dataset (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). E-OBS dataset provides updated high quality daily air temperature, precipitation and sea level pressure data for Europe at different grid intervals from January 1950 onwards, with regular updates. This dataset was created from quality-controlled meteorological records47, sourced from the European National Meteorological Services. Earlier versions of this dataset have been made available at a spatial resolution of 0.25°, being improved (0.1°) in the version v17.0e. Herein, we produced our indices at a spatial resolution of 0.25° to be consistent with the resolution of ERA5. In pursuit of the ERA4CS INDECIS project (European Union Grant 690462), daily station data from ECA&D were homogenized48 and the adjusted records will serve as a basis for the upcoming version of E-OBS datasets. In our gridded dataset, the E-OBS dataset acted as the main base for calculating climate indices related to air temperature and precipitation.
ERA5 is the fifth generation of the re-analysis dataset developed by the European Centre for Medium-Range Weather Forecasts (ECMWF)49. Albeit with the availability of daily air temperature and precipitation data within ERA5, our preference was made to use ERA5 as a supplementary data source to the observational datasets, with the aim of securing unavailable observational data for some climate variables. This included data for dewpoint temperature at 2 m, wind at 10 m, wind gust at 10 m, top-of-atmosphere (TOA) forcing, radiation, insolation, snow density, snow depth, snowfall, total cloud cover, and low cloud cover. Data from ERA5 were provided at a spatial resolution of 0.25° and hourly frequency. However, we aggregated the hourly data to daily scale to match the temporal resolution of E-OBS. To date, ERA5 is only available from February 1979; however, we will utilize the dataset for the early decades (1950–1978) once data is available.
Figure 1 depcits the spatial coverage of the study domain;our dataset included the entire European continent, apart from Russia, Turkey and Cyprus, comprising a total of 12280 series at a spatial resolution of 0.25°.
We calculated a set of 125 climate indices, based on Climlnd: a recently developed package within R platform (https://cran.r-project.org/web/packages/ClimInd/index.html). The climate indices were grouped into eight broad categories: (i) temperature-based (N = 42), (ii) precipitation-based (N = 21), (iii) bioclimatic (N = 21), (iv) wind-based (N = 5), v) aridity/continentality (N = 10), vi) snow-based (N = 12), vii) cloud/radiation-based (N = 6), and (viii) drought (N = 8). Online-only Table 1 lists these indices and their description for the eight categories. The specific formulation of each index can also be consulted via: https://cran.r-project.org/web/packages/ClimInd/ClimInd.pdf. Overall, the majority of the indices were computed on monthly, seasonal (winter: DJF, spring: MAM, summer: JJA, autumn: SON) and annual scales. However, in some instances, specific indices were calculated only on the annual scale (e.g. the growing season precipitation [GSR] and the modified Fournier index [MFI]). For those indices that require a base period for their calculation (e.g. percentile-based indices), we considered the entire period as a reference period. Herein, the 125 climate indices were computed for each one of the 12280 series covering the European continent.
Figure 2 illustrates the spatial variability of two selected indices (Rx1day and TXx) calculated on the annual scale for three specific years. Rx1day represents the maximum precipitation gauged within one day, whilst TXx refers to the highest daily maximum air temperature recorded during the year.
Figure 3 depicts the temporal evolution of two selected climate indices (heavy precipitation days [D50mm] and the maximum consecutive frost days [CFD]) for randomly selected grid points in Europe. The two indices were computed on the annual scale for the entire study period.
The climate index dataset has been archived in figshare (https://doi.org/10.6084/m9.figshare.c.4773491)45, including all climate indices spanning the periods from 1950 or 1979 to 2017, based on data availability (see Online-only Table 1). Given the high number of indices included in this dataset, we have stored the climate index data in different levels. The first level corresponds to the main eight categories: temperature-based, precipitation-based, bioclimatic, wind-based, aridity/continentality, snow-based, cloud/radiation-based, and drought. Each of the first level categories (folders) is divided into subcategories (sub-folders); each of them corresponds to an individual climate index. Finally, at the third level, there are the data of each index at the different available temporal scales (i.e. monthly, seasonal and annual). The data were stored in a 3-D netcdf4 format (https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_introduction.html). In specific, each file has an array of 464 (longitudes) × 201 (latitudes) × 68 (times for the annual resolution for the period 1950–2017). Indeed, the time dimension of the array varies as a function of the time resolution (i.e. monthly, seasonal, annual), as well as the period of time available (currently 1950 to 2017 for E-OBS indices and 1979 to 2017 for ERA5 indices). The geographical extent of the dataset is 25.37°N–75.37°N and 40.37°W–75.37°E. Netcdf4 files can be visualized and manipulated with several types of software, including open source software like R, Panoply, etc., and Geographic Information System software (ArcGIS©, QGIS). A complete list of the available software can be found at https://www.unidata.ucar.edu/software/netcdf/software.html. The entire dataset comprises almost 1 billion of data divided between the 125 indices and the different temporal scales (i.e. monthly, seasonal and annual).
The validation of our dataset depends largely on the reliability of climatic information used for calculating each index. The quality control and homogeneity of climatic data retrieved from the source datasets (i.e. E-OBS and ERA5) are comprehensively described via https://www.ecad.eu/download/ensembles/download.php for E-OBS and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 for ERA5. We have checked the spatial and temporal consistency of the obtained climate indices. Also, the monthly, seasonal and annual values of the different indices were visually inspected to detect any possible problems.
In addition to the stored dataset in the figshare repository, the climate indices are made also available through the project website (https://indecis.csic.es/), maintained by the Spanish National Research Council (Fig. 4). In addition to its role as a data warehouse, the project’s website can be used as a climate service, where end-user beneficiaries can select, visualize and download any index of interest for any grid point in Europe (Fig. 5). These series can be downloaded in a generic ASCII .txt format. The web site also allows for the navigation between the monthly, seasonal and annual archives and directly download any netcdf4 of the database. The entire dataset stored in this web site will be updated regularly depending on the availability and updates of the ECA&D and ERA5 data. This website is mirrored at the portal of the INDECIS project (http://www.indecis.eu/indices.php).
The code used to calculate the indices is available via: https://cran.r-project.org/web/packages/ClimInd/index.html. The R scripts necessary to calculate the different indices, from the ECA&D and ERA5 gridded datasets in a 3-D array format, can be accessed via: https://gitlab.com/fergusrg/indecis_example.
Sergio, F., Blas, J. & Hiraldo, F. Animal responses to natural disturbance and climate extremes: a review. Glob. Planet. Change 161, 28–40 (2018).
Eyshi Rezaei, E., Webber, H., Gaiser, T., Naab, J. & Ewert, F. Heat stress in cereals: Mechanisms and modelling. Eur. J. Agron. 64, 98–113 (2015).
Bachmair, S., Tanguy, M., Hannaford, J. & Stahl, K. How well do meteorological indicators represent agricultural and forest drought across Europe? Environ. Res. Lett. 13, 1–10 (2018).
Olesen, J. E. et al. Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34, 96–112 (2011).
Wolf, S. et al. Contrasting response of grassland versus forest carbon and water fluxes to spring drought in Switzerland. Environ. Res. Lett. 8, 1–12 (2013).
Van Dijk, A. I. J. M. et al. The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour. Res. 49, 1040–1057 (2013).
Crockett, J. L. & Leroy Westerling, A. Greater temperature and precipitation extremes intensify Western U.S. droughts, wildfire severity, and sierra Nevada tree mortality. J. Clim. 31, 341–354 (2018).
Porter, J. R. & Semenov, M. A. Crop responses to climatic variation. Philos. Trans. R. Soc. B Biol. Sci. 360, 2021–2035 (2005).
Prugh, L. R. et al. Ecological winners and losers of extreme drought in California. Nat. Clim. Chang. 8, 819–824 (2018).
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. USA 110, 52–7 (2013).
Ruffault, J., Curt, T., Martin-Stpaul, N. K., Moron, V. & Trigo, R. M. Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean. Nat. Hazards Earth Syst. Sci. 18, 847–856 (2018).
Di Napoli, C., Pappenberger, F. & Cloke, H. L. Assessing heat-related health risk in Europe via the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 62, 1155–1165 (2018).
Blazejczyk, K., Epstein, Y., Jendritzky, G., Staiger, H. & Tinz, B. Comparison of UTCI to selected thermal indices. Int. J. Biometeorol. 56, 515–535 (2012).
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).
Matiu, M., Ankerst, D. P. & Menzel, A. Interactions between temperature and drought in global and regional crop yield variability during 1961–2014. PLoS One 12, 1–23 (2017).
Lobell, D. B. et al. The shifting influence of drought and heat stress for crops in northeast Australia. Glob. Chang. Biol. 21, 4115–4127 (2015).
Scott, D., Gössling, S. & De Freitas, C. R. Preferred climates for tourism: case studies from Canada, New Zealand and Sweden. Clim. Res. 38, 61–73 (2008).
Easterling, D. R., Alexander, L. V., Mokssit, A. & Detemmerman, V. CCI/CLIVAR workshop to develop priority climate indices. Bull. Am. Meteorol. Soc. 84, 1403–1407+1329 (2003).
Yu, G., Schwartz, Z. & Walsh, J. E. A weather-resolving index for assessing the impact of climate change on tourism related climate resources. Clim. Change 95, 551–573 (2009).
Peña-Gallardo, M. et al. Drought sensitiveness on forest growth in peninsular Spain and the Balearic Islands. Forests 9, 524 (2018).
Peña-Gallardo, M. et al. Effectiveness of drought indices in identifying impacts on major crops across the USA. Clim. Res. 75, 221–240 (2018).
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W. & Bronaugh, D. Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res. Atmos. 118, 1716–1733 (2013).
Zhou, B., Wen, Q. H., Xu, Y., Song, L. & Zhang, X. Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Clim. 27, 6591–6611 (2014).
Dosio, A. Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models. J. Geophys. Res. Atmos. 121, 5488–5511 (2016).
Campozano, L. et al. Evaluating extreme climate indices from CMIP3&5 global climate models and reanalysis data sets: a case study for present climate in the Andes of Ecuador. Int. J. Climatol. 37, 363–379 (2017).
Klein Tank, A. M. G., Zwiers, F. & Zhang, X. Guidelines on Analysis of extremes in a changing climate in support of informed decisions for adaptation, WMO/TD-1500, Climate Data and Monitoring WCDMP-No. 72. (2009).
Bezerra, B. G., Silva, L. L., e Silva, C. M. & de Carvalho, G. G. Changes of precipitation extremes indices in São Francisco River Basin, Brazil from 1947 to 2012. Theor. Appl. Climatol. 135, 565–576 (2019).
Yin, H. & Sun, Y. Characteristics of extreme temperature and precipitation in China in 2017 based on ETCCDI indices. Adv. Clim. Chang. Res. 9, 218–226 (2018).
Wang, H., Pan, Y., Chen, Y. & Ye, Z. Linear trend and abrupt changes of climate indices in the arid region of northwestern China. Atmos. Res. 196, 108–118 (2017).
Abatan, A. A., Abiodun, B. J., Gutowski, W. J. & Rasaq-Balogun, S. O. Trends and variability in absolute indices of temperature extremes over Nigeria: linkage with NAO. Int. J. Climatol. 38, 593–612 (2018).
Touré Halimatou, A., Kalifa, T. & Kyei-Baffour, N. Assessment of changing trends of daily precipitation and temperature extremes in Bamako and Ségou in Mali from 1961–2014. Weather Clim. Extrem. 18, 8–16 (2017).
Mathbout, S. et al. Observed Changes in Daily Precipitation Extremes at Annual Timescale Over the Eastern Mediterranean During 1961–2012. Pure Appl. Geophys. 175, 3875–3890 (2018).
Turco, M., Zollo, A. L., Ronchi, C., De Luigi, C. & Mercogliano, P. Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy. Nat. Hazards Earth Syst. Sci. 13, 1457–1468 (2013).
Supari, Tangang, F., Juneng, L. & Aldrian, E. Observed changes in extreme temperature and precipitation over Indonesia. Int. J. Climatol. 37, 1979–1997 (2017).
Terando, A., Easterling, W. E., Keller, K. & Easterling, D. R. Observed and modeled twentieth-century spatial and temporal patterns of selected agro-climate indices in North America. J. Clim. 25, 473–490 (2012).
Beharry, S. L., Clarke, R. M. & Kumarsingh, K. Variations in extreme temperature and precipitation for a Caribbean island: Trinidad. Theor. Appl. Climatol. 122, 783–797 (2015).
Sajjad, H. & Ghaffar, A. Observed, simulated and projected extreme climate indices over Pakistan in changing climate. Theor. Appl. Climatol. 137, 255–281, https://doi.org/10.1007/s00704-018-2573-7 (2019).
Aerenson, T., Tebaldi, C., Sanderson, B. & Lamarque, J.-F. Changes in a suite of indicators of extreme temperature and precipitation under 1.5 and 2 degrees warming. Environ. Res. Lett. 13, 035009 (2018).
Dong, S. et al. Observed changes in temperature extremes over Asia and their attribution. Clim. Dyn. 51, 339–353 (2018).
Dietzsch, F. et al. A global ETCCDI-based precipitation climatology from satellite and rain gauge measurements. Climate 5, 9 (2017).
Donat, M. G. et al. Global Land-Based Datasets for Monitoring Climatic Extremes. Bull. Am. Meteorol. Soc. 94, 997–1006 (2013).
Lorenz, R. et al. Representation of climate extreme indices in the ACCESS1.3b coupled atmosphere-land surface model. Geosci. Model Dev. 7, 545–567 (2014).
Mistry, M. N. Historical global gridded degree-days: A high-spatial resolution database of CDD and HDD. Geosci. Data J. 6, 214–221 (2019).
Beguería, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).
Domínguez-Castro, F. et al. A multidecadal assessment of climate indices over Europe. figshare, https://doi.org/10.6084/m9.figshare.c.4773491 (2020).
Street, R. B. Towards a leading role on climate services in Europe: A research and innovation roadmap. Clim. Serv. 1, 2–5 (2016).
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. & Jones, P. D. An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
Squintu, A. A., van der Schrier, G., Brugnara, Y. & Klein Tank, A. Homogenization of daily temperature series in the European Climate Assessment & Dataset. Int. J. Climatol. 39, 1243–1261 (2019).
(C3S), C. C. C. S. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS) (2017).
This work was supported by the research projects PCIN-2015-220, CGL2017-82216-R and CGL2017-83866-C3-1-R financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by the AXIS (Assessment of Cross(X) - sectorial climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Dhais Peña-Angulo is supported by a “Juan de la Cierva” postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Domínguez-Castro, F., Reig, F., Vicente-Serrano, S.M. et al. A multidecadal assessment of climate indices over Europe. Sci Data 7, 125 (2020). https://doi.org/10.1038/s41597-020-0464-0
This article is cited by
Spatiotemporal projections of extreme precipitation over Algeria based on CMIP6 global climate models
Modeling Earth Systems and Environment (2023)
Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP)
Earth Systems and Environment (2023)
Climate Dynamics (2022)