Last modified on Sept 16, 2026 15:25

Contributors: B-Open and TCDF

Issued by: B-Open

Issued Date: 19 May 2025 

Ref: C3S3_430a – ECDE maintenance and development

Official reference number service contract: 2021/C3S2_430a_BOPEN

Acronyms

1. Introduction

1.1. Executive summary

This dataset responds to the European Environmental Agency's (EEA) and other user's needs to provide access to the derived (aggregated and multi-model) products that underpin the European Climate Data Explorer. This dataset provides a series of climate indicators derived products (anomalies, climatology, ensemble statistics etc...) that correspond to the post processed data displayed in the European Climate Data Explorer (ECDE) visualisation application hosted on the European Environment Agency’s Climate-ADAPT platform. It allows the download of the application's data for direct access and reproduction of the plots. This catalogue entry complements the Climate indicators for Europe from 1940 to 2100 derived from reanalysis and climate projections dataset which contains single ensemble members and full time series by providing pre-calculated ensemble statistics and derived products such anomalies and climatologies.   

The ECDE climate indicators describe how climate variability and change can impact key sectors such as health, agriculture, forestry, energy, tourism, or water management. These indicators are grouped by hazard categories introduced by the IPCC (Heat and cold, Wet and dry, Wind, Snow and ice) and are displayed in the ECDE visualisation application as maps and plots. 

The dataset includes several products corresponding to each plot of the ECDE visualisation application that show key indicators including model uncertainty envelopes time series, 30-year averages, anomalies, Global Warming Levels (GWLs), projected time series, and climatology. These products are available at monthly, seasonal or annual temporal aggregation depending on the specific indicator. The spatial aggregations cover gridded or aggregated regions such as NUTS regions, transnational regions and European zones.

This dataset was produced on behalf of the Copernicus Climate Change Service. This document describes version 2.0, which represents the first public release of the datasets. Regular updates will follow subsequent releases of the ECDE visualisation application.

1.2. Scope of documentation

This documentation provides a comprehensive overview of the aggregated climate indicators for Europe from 1940 to 2100, including their requirements, definitions, structure, and methodology. It first presents the requirements for the climate hazard indicators (2.1. Requirements), followed by a description of the aggregated dataset (2.2. Data Description). The definitions of the ECDE climate indicators are detailed in (2.3. Climate indicators definition), and the specific derived statistics derived from these indicators are outlined in (2.4. Derived statistics). Finally, the methodology and input datasets used for product generation are described in (2.5. Method).

1.3. Version history 

This is the first release of the dataset, labelled as version v2.0 to align with version v2.0 of the ECDE indicators. Future updates will align with subsequent releases. 

2. Product description

2.1. Requirements 

The development of the ECDE climate indicators and derived statistics was driven by the European Environment Agency (EEA) to meet the informational needs of national climate change adaptation initiatives across the EU and partner countries. These requirements were identified through user consultations and stakeholder engagement.

The indicators support adaptation planning at European and national levels and cover the hazard categories defined by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA, [1]). They are made available interactively through public visualisation applications on the European Climate Data Explorer hosted  on the EEA’s Climate-ADAPT platform.

2.2. Data Description

Table 1 presents the main characteristics of the aggregated dataset, including its spatial and temporal coverage, resolution, and file format.

Table 1: List of key specifications of the ECDE climate hazard derived statistics.

2.3. Climate indicators definition

Table 2 lists and defines the climate indicators included in the aggregated dataset.

Table 2: Definition of the ECDE indicators.

Note on Hydrological models:

Most variables are derived directly from temperature and precipitation. However,  Flood recurrence, Mean river discharge, Aridity actual and Mean soil moisture are brokered directly through the C3S Operational Water Service. These hydrological indicators are calculated using EURO-CORDEX regional climate model forcing and hydrological impact models. The water service uses two different hydrological impact models (E-HYPE and VIC-WUR) and are described in detail in the linked document.

For completeness, hydrological indicators in this catalogue entry are available for the E-HYPE and VIC-WUR models as well as in a "combined" version: for each regional climate model, the average of the two hydrological impact models has been computed. All derived statistics have then been calculated using the same methodology applied to the individual hydrological models.

Note on Variable-specific parameters:

Some variables depend on an additional parameter that must be selected in the download form. The variables requiring this extra selection are:

• Hot days: A temperature threshold must be selected. The available thresholds to define a hot day are 30 °C, 35 °C, or 40 °C.

• Snowfall amount: An elevation must be selected. Snowfall data is available for the following altitudes: 0 m, 500 m, 1,000 m, 1,500 m, 2,000 m, 2,500 m, and 3,000 m.

• Flood recurrence: A return period must be selected. The available options are 1-in-2, 1-in-5, 1-in-10, or 1-in-50 years.

• Daily maximum and minimum temperature: To access the absolute highest, lowest, or average values of daily maximum and minimum temperatures over monthly, seasonal, or yearly intervals, the user must select the corresponding statistic: maximum, minimum, or mean.

2.4. Derived statistics

The derived statistics refer to the aggregated and multi-model product that can be dowloaded through the catalogue entry. Instead of downloading row time series of an indicator for a single model, the derived statistics provide multi-model climatologies, anomalies, uncertainties etc. The following sections present the categories in which the products have been grouped and the definition of every single derived statistic.

2.4.1. Categories

The products documented here originate from the interactive visualisation application of the European Climate Data Explorer (ECDE), hosted on the European Environment Agency’s (EEA) Climate-ADAPT platform. In the ECDE, indicators are presented and visualised in five main ways:

• Maps of 30 years averages;
• Maps of Global warming levels;
• Anomalies;
• Projected time series;
• Climatology.

The derived statistics provided in the present catalogue entry correspond to the data underlying these visualisations and have been grouped into five categories which correspond to the different ways of presenting and analysing the data through the ECDE application. The categories are described below, with illustrative maps and plots taken from the ECDE application.

30 year statistics and GWLs

The products in the 30-year statistics and GWL categories contain data expressed as absolute values, absolute changes, or relative (%) changes. Ensemble means and quantiles (17th, 50th, 83rd percentiles) are provided to characterise uncertainty. Both products categories can be represented as maps showing climatological averages or projected changes in the indicators. For 30-year statistics, values are calculated over fixed 30-year periods (e.g. 2011–2040, 2041–2070, 2071–2100) and compared with reference periods (1981–2010 or 1991–2020). For GWLs, values correspond to the average conditions in a world at specific warming levels (+1.5 °C, +2 °C, +3 °C, +4 °C) relative to pre-industrial times.

Figure 1: Example map showing country aggregated temperature values averaged over a 30 year period.

Anomaly

The products in the anomaly category contain data expressed as deviations from a 30-year reference period (1981–2010 or 1991–2020). To characterise uncertainty, ensemble means and quantiles (17th, 50th, 83rd percentiles) are provided. These data can be represented in a plot showing yearly values and their deviations relative to a reference period.

Figure 2: Example plot showing time series of yearly temperature anomalies (i.e. differences from the mean) relative to the 1991–2020 reference period.

Projected time series

The products in the projected time series category contain data expressed as time series of quantiles. These data can be represented in a plot showing observed yearly values together with the ensemble median and the likely range (66% probability of occurrence) from climate model simulations, displayed for different emission scenarios.

Figure 3: Example plot showing time series of the median and likely range of temperature as an envelope based on quantile values.

Climatology

The products in the climatology category contain data expressed as monthly climatologies. These data can be represented in a plot showing 30-year averages and standard deviations of observed and projected monthly values for three time horizons. Likely values (66% probability of occurrence) are represented as an uncertainty envelope derived from an ensemble of climate models.

Figure 4: Example plot showing averaged monthly temperature values (climatologies) over the 2011-240, 2041-2070, and 2071–2100 period.

2.4.2. Definitions

Table 3: Derived statistics naming convention and definition.

2.4.3. File structure

Each derived statistic is distributed as a multidimensional NetCDF file. Table 4 specifies the available dimensions for each product type, facilitating consistent interpretation and use of the datasets. The possible dimensions are:

• avg_period: The period over which the average was calculated (i.e., 1981–2010/1991-2020  for historical periods and 2011-2040/2041-2070/2071-2100 for future periods).

• ref_period: The historical reference period used to calculate changes or anomalies (i.e., 1981–2010 or 1991-2020).

• scenario: Emission scenario (typically RCP4.5, RCP8.5).

• clim_period: The period for climatological statistics covering both historical and futur periods. 

• quantile: Ensemble quantile (typically 17th, 50th, 83rd).

• gwl: Global warming level (e.g., +1.5°C, +2°C, +3°C, and +4°C above preindustrial).

• time: Timestamps for time-series products.

• month: Month index for climatology products.

Table 4 lists the dimensions present in the NetCDF files for each derived statistic, distinguishing between reanalysis and projections. This helps to understand the organization and access relevant information within each product file.

Table 4: Dimensions of each derived statistic. 

avg_period

avg_period
scenario

ref_period
avg_period

ref_period
avg_period
scenario

ref_period
avg_period

ref_period
avg_period
scenario

avg_period

avg_period
scenario
quantile

ref_period
avg_period

ref_period
avg_period
scenario
quantile

ref_period
avg_period

ref_period
avg_period
scenario
quantile

gwl

gwl
ref_period

gwl
ref_period

ref_period
time

ref_period
scenario
quantile
time

scenario
quantile
time

clim_period
stat
month

clim_period
stat
quantile
month

2.5. Method

2.5.1. Input dataset

The hazard products are based on the indicators published in the Climate indicators for Europe from 1940 to 2100 derived from reanalysis and climate projections dataset, available in the Copernicus Climate Data Store (CDS). Detailed descriptions of these indicators are provided in the corresponding Product User Guide, while documentation of the underlying datasets can be found here. 

2.5.2. Calculation

Computing hazard products primarily involves temporal aggregations, simple calculations and statistics (e.g., anomalies, relative changes, ensemble quantiles). The most complex component concerns the estimation of GWLs. The methodology and time frames used to compute these levels are described in Appendix 1.

3. Version changes

Table 5: List of the ECDE indicators dataset versions.

4. Concluding remarks

The derived statistics presented in this documentation, derived from the underlying climate indicators dataset, offer harmonised user-ready information on key climate-related hazards across Europe covering the 1940-2100 period. These products are available for exploration and analysis through the interactive visualisation application on the European Climate Data Explorer hosted on the EEA’s Climate-ADAPT site. They are provided as a dataset to support further analysis by climate change adaptation practitioners that wish to have access to the underlying data of the ECDE.  Designed to support adaptation planning and risk assessment, the products provide regionally aggregated data on hazards such as heat, cold, drought, flooding, and wind, building on established climate indicators. As climate data and scientific understanding evolve, these hazard products will continue to be updated and expanded, ensuring ongoing support for climate resilience planning across Europe.

5. Appendix 1: Global Warming Levels 

This appendix briefly describes the concept of Global Warming Levels (GWLs) and the methodology used by the IPCC in the IPCC Atlas to represent them. 

GWLs, as defined by the IPCC, represent specific global mean temperature increases—namely +1.5 °C, +2 °C, +3 °C, or +4 °C—relative to the preindustrial baseline (1850–1900). These levels serve as standard reference points for comparing climate projections and assessing hazards across models and scenarios regardless of when those levels will be reached. 

For each ensemble member in the CMIP5 and CMIP6 datasets, a 20-year moving window is applied to the annual global mean temperature series; the GWL is considered reached when the central year of the window first meets or exceeds the specified threshold, and the corresponding 20-year period is recorded as [n–9, n+10] (with n as the central year). For each warming level and scenario, an ensemble mean period is then calculated by averaging the periods obtained from all ensemble members.

For EURO-CORDEX regional simulations, the GWL periods are assigned according to the driving global climate mode. This is because all regional simulations forced by the same global model share identical GWL periods. These driving global climate models of the underlying datasets are listed in Table 6.

The tables below provide the GWL periods for CMIP5 (Table 7) and CMIP6 models (Table 8) Earth System Models. In both tables, ‘NA’ indicates that a given GWL is not reached before 2100, and ‘9999’ denotes missing data for that model or scenario.

The tables focus on RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5 scenarios, as these are used in the underlying climate indicators and derived statistics datasets. The +4°C warming level appears exclusively in the RCP8.5/SSP5-8.5 scenario because this represents the only emission pathway that reaches this threshold—an approach consistent with the "Simple" version of the IPCC Atlas. The "Extended" Atlas provides GWLs for each SSP, but not all warming levels are available for every scenario (e.g., SSP1-2.6 attains only +1.5 °C, SSP2-4.5 reaches +1.5 °C and +2.0 °C, SSP3-7.0 goes up to +3.0 °C, and SSP5-8.5 up to +4 °C).

Table 6: List of CMIP5 driving Earth System Models of the EURO-CORDEX experiment used by each project and corresponding dataset. 

Table 7: Central year of GWLs in CMIP5 simulations under RCP4.5 and RCP8.5. ESM's used in SIS Energy are highlighted in yellow.  

Source https://github.com/IPCC-WG1/Atlas/blob/main/warming-levels/CMIP5_Atlas_WarmingLevels.csv.

Table 8: Central year of GWLs for CMIP6 simulations under SSP2_4.5 and SSP5-8.5.   

Source https://github.com/IPCC-WG1/Atlas/blob/main/warming-levels/CMIP6_Atlas_WarmingLevels.csv.

6. Appendix 2: Regional layers

The table below lists all the regional layers available and the corresponding code to use in the file naming conventions.

7. References

[1] Crespi A., Terzi S., Cocuccioni S., Zebisch M., Berckmans J., Füssel H-M (2020) “Climate-related hazard indices for Europe”. European Topic Centre on Climate Change impacts, Vulnerability and Adaptation (ETC/CCA) Technical Paper 2020/1. DOI: https://doi.org/10.25424/cmcc/climate_related_hazard_indices_europe_2020

[2] Van Wagner, C.E., 1987. Development and structure of the Canadian Forest Fire Weather Index System, Forestry Technical Report (CFS - Ottawa). Canadian Forestry Service, Headquarters, Ottawa. http://cfs.nrcan.gc.ca/publications?id=19927. 

8. Related articles

• Page:
  ECDE indicators catalogue entry v2.0: Product User Guide
• Page:
  European Climate Data Explorer (ECDE) indicators