Last modified on Jul 10, 2024 10:43

Contributors: B-Open and TCDF

Issued by: B-Open

Issued Date: 28/07/2023

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 provides a series of climate indices derived from reanalysis and model simulations data hosted on the CDS that are made available through interactive visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. These indices describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. 

The indices have either been calculated through a specific CDS Toolbox workflow using CDS dataset in input or directly retrieved From the CDS when already available. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate simulations data differ in their technical specifications (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as Ideally the indices should come from the same dataset with identical specifications. 

The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables from the same datasets: Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections and ERA5 single levels. The other indices are directly available from CDS dataset generated by sector specific projects.

1.2. Scope of documentation

This document provides a description of the indices included in the European Climate Data Explorer and details of the dataset and the methodology used to produce them. First, the product requirements (section 2.1) against which this dataset was developed is presented, followed by the definition of the indicators (2.2 product overview) and the input datasets (2.3 Input datasets) used to calculate them. Thereafter, the methodology (2.4 Method) to produce the indicators forming this catalogue entry is described.

1.3. Version history 

This is the first version of the dataset.

2. Product description

2.1. Product requirements 

The development of those indices was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultations. They are relevant for adaptation planning at the European and national level and cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. 

2.2. Product overview 

2.2.1. Data Description

Table 1: Overview of key characteristics of the ECDE indices.

2.2.2. Indices description

Table 2: Description of the ECDE indices.

2.2.3. Input datasets by indicator

The table below provides the input dataset used to derive each indicator. Each single dataset is described in more detail in section 2.3. For conciseness and clarity we have used a consistent set of short names for the datasets in the table below and in section 2.3. The Product User Guide (PUG) column provides a link to the specific indicator PUG for further information.

Table 3: List of the ECDE indices datasets.

2.3. Input datasets

2.3.1. ERA5 and derived products. 

The ERA5 reanalysis is regarded as a good proxy for observed atmospheric conditions and currently covers 01/01/1959 to near real time and is regularly extended as ERA5 data become available. 

The historical values of the indices were evaluated from hourly data from the ERA5 reanalysis (ERA5 single levels) whenever possible. This was the case for the indices related to temperature, precipitation and wind (20 out of 30). For the remaining indicators, values over the historical period (either simulated or from reanalysis when available) were used directly.

The historical values of Days with high fire danger and Fire Weather indices are from the Fire danger indices historical data from the Copernicus Emergency Management Service dataset, that is based on the ERA5 reanalysis and updated in near real time. It is produced by the Copernicus Emergency Management Service (CAMS) for the Global ECMWF Fire Forecasting model (GEFF) and the European Forest Fire Information System (EFFIS). More technical specifications can be found in the dataset documentation.

2.3.2. SIS Energy

The simulated indices related to temperature, precipitation and wind (20 out of 30) were calculated from daily atmospheric variables in the same climate projections dataset: Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections. It is a set of bias-adjusted EURO-CORDEX projections composed of 9 GCM-RCM simulations at 0.25° x 0.25° spatial resolution, 3-hourly temporal resolution and cover emission scenarios RCP4.5 and RCP8.5. The 9 combinations of the 5 GCMs with the 5 RCMs is given in Table 1. More technical specifications can be found in the dataset documentation. 

Table 4: The 9 GCM/RCM combinations of the Climate and energy indicators for Europe from 2005 to 2100 derived from climate projection dataset

2.3.3. ERA5 Heat

The High UTCI Day index data are from Thermal comfort indices derived from ERA5 reanalysis. As indicated, it is based on surface variables from the ERA5 reanalysis (ERA5 single levels) and inherits the same spatial (0.25° x 0.25°) and temporal resolution (hourly). There are no climate projections of UTCI at the moment. More technical details can be found in the dataset documentation. 

2.3.4. SIS Operational Water Service

The Flood recurrence, Mean river discharge, Aridity actual, and Duration of Soil moisture Draughts index data are from the Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections dataset. It is a set of 30-year statistics from two hydrological models forced by 8 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. The hydrological models are from the Swedish Meteorological and Hydrological Institute (SMHI, E-HYPEgrid model) and Wageningen University (VIC-WIR Model). The hydrological simulations are either gridded (5km x 5km) or at catchment scale and cover scenarios RCP4.5 and RCP8.5. The 8 combinations of the 5 GCMs with the 5 RCMs is given in Table 1. More technical details about the hydrological models can be found in the dataset documentation and in the following Hydrological model specification. 

Table 7: The 8 GCM/RCM combinations of the water related indices dataset.

2.3.5. SIS EU Tourism

The Days with high fire danger and Fire Weather index data are from the Fire danger indicators for Europe from 1970 to 2098 derived from climate projections dataset. It is a set of 6 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. These simulations have a daily temporal resolution, a spatial resolution of 0.1° x 0.1° and cover scenarios RCP4.5 and RCP8.5. The 5 combinations of the 5 GCMs with 1 RCM is given in Table 1. More technical specifications can be found in the dataset documentation. 

Table 8: The 5 GCM/RCM combinations of the Fire danger indicators dataset

Note: The Fire Weather Index values were bias-corrected in this project to be compatible with the CEMS reanalysis FWI. See subsection 2.4.2 Indicator calculation.

The Snowfall amount index data (both historical and simulated) are from the Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections dataset. The dataset is based on the UERRA reanalysis and a set of 9 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. The index data simulations have an annual temporal resolution, a spatial resolution over NUTS3 regions, a vertical resolution of 100m and cover scenarios RCP4.5 and RCP8.5. More technical specifications can be found in the dataset documentation. 

Table 9: The 9 GCM/RCM combinations of the snow indicators dataset.

2.3.6. SIS European Storm Surges

The Relative sea level rise and the Extreme sea level index data are from the Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections dataset. It is based on a set of simulations produced with the Global Tide and Surge Model (GTSM) of Deltares, a global 2D hydrodynamic model which incorporates tides, surges and mean sea-levels dynamically. Both the historical and future GTSM simulations include sea level rise data as input that are also available in the dataset. The Relative sea level rise field is annual and spatially-varying at 1° x 1° resolution and is relative to the 1986-2005 reference period based on RCP8.5 (not SSPs as for the GTSM simulations). The field is the median result of a probabilistic model that computes and combines processes affecting sea level. As such, it is model independent and only one field (the median) of SLR is provided in the dataset. More technical specifications can be found in the dataset documentation.

2.4. Method

2.4.1. Input dataset selection

The indices are either retrieved from datasets when available or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specifications (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as Ideally the indices should come from the same dataset with identical specifications.

2.4.2. Indicator calculation

The indicators were calculated according to the recommended definitions based on a technical paper from the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC/CCA). The indices are either retrieved from datasets when available or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. 

One exception is the Fire Weather Index (FWI) data of the SIS Tourism dataset. A bias was found between the original values and the values based on reanalysis (CAMS) and it was decided to apply a bias-correction procedure to correct the mean and the variance of the simulated FWI with the mean and variance of the reanalysis based FWI and taking the 1981-2010 period as reference (see Appendix 1).

2.4.3. Regional aggregation

Where relevant the indicators have been aggregated over standard administrative bounderies used by european institution for reporting. The aggregation was performed using the CDS Toolbox aggregation tools. The administrative boundaries available are:

• the NUTS classification (Nomenclature of territorial units for statistics) maintained by Eurostat (see Eurostat (2021) website). NUTS is a hierarchical system for dividing up the economic territory of the EU and the UK. The indicators uses NUTS regions ranging from NUTS0 (country) to NUTS2 (sub-country) and NUTS3 in the specific case of Snowfall amount. 
• Europe zones is the number of countries considered for the European domain (27 EU countries, 32 EEA member countries and 38 EEA member and cooperating countries).
• Transnational regions regions involve cooperating regions from several countries of the EU forming bigger areas (e.g. danube, alpine, ionian etc.) to promote better cooperation and regional development within the Union

Appendix 2 lists all the available regional layers.

2.4.4. Limitations

The EEA ECDE initiative gathers in a single dataset a number of indices that are relevant for adaptation planning at the European and national level. Ideally the indices should come from the same dataset with identical specifications, as this was not possible considering the available datasets hosted on the CDS an effort was made in the dataset selection to limit the heterogeneity of the underlying dataset. Out of the 30 ECDE indicators in this catalogue entry, 19 of them use the same input dataset (SIS Energy and ERA5 Single level, see Table 3) and provide a consistent core of indicators. The other indices required the use of different datasets because they require more sector specific data (fire, hydrology, tourism) and are therefore not directly comparable. In particular, differences in future behaviour (trends, variability, etc..) between indicators using different input datasets can be, at least partially, attributed to the underlying climate projection included in each dataset. Besides the possible heterogeneity between indicators it is advised to consult the Product User Guide of individual indicators to understand their own limitations. 

2.4.5. Validation and quality assurance

The strategy behind the calculation of the ECDE indicators is to use Quality Assured and validated datasets distributed through the CDS and use fully traceable and repeatable CDS Toolbox workflows to perform the computation. All the datasets used in input and listed in 2.2.3. Input Datasets by Index have gone through the CDS quality assurance and are under C3S governance and scrutiny should any error be found in the future. This strategy eliminates the uncertainties or errors due to the input data or tools used.

The workflows to calculate the indicators and their documentation could be subject to errors and to minimise them the quality assurance procedure described below has been followed. This process included several reviews by different responsible at various steps during the computation and publication of the indicators.

Figure 1: ECDE internal quality assurance and review process

Regarding the validation of specific indicators the validation has mainly been performed by comparing the output with either the already existing indicators available on the ECDE or other publication from the EEA where the same indicators were presented and computed different input data. Even though it was not necessarily possible to check against a numerically identical dataset it was possible to check that the indicators are consistent with other publications.

3. Concluding remarks

The ECDE indicators dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the CDS. It gathers data both in a gridded format and as regional averages that are used to make the indicators available through interactive visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. It is 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.

4. Glossary

5. References

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

6. Appendix 1: Fire weather index bias correction

The projected FWI data distributed in the CDS is not bias-corrected against any observation data. To be able to compare the projected FWI data to FWI data derived from reanalysis a bias correction to correct the mean and the variance of the simulated FWI has been implemented.

The implemented bias-correction follows the following steps for each single FWI simulation:

• Compute the monthly climatology of the model for the period 1981 to 2010.
• Compute the monthly standard deviation of the model for the period 1980 to 2021.
• Compute the monthly climatology of the reanalysis derived FWI for the period 1981 to 2010.
• Compute the monthly standard deviation of the reanalysis derived FWI for the period 1980 to 2021.
• For each month:
• Compute the 30 year running mean. 
• Detrend: subtract the 30 year running mean to the daily projected values.
• Variance adjustment: multiply the projected detrended daily values by the standard deviation ratio (reanalysis standard deviation divided by the projection standard deviation). For projection standard deviation below 0.1 (corresponding to an FWI close to 0) the ratio is set to 1 to avoid diverging values.
• Retrend: add the 30 year running mean to the variance adjusted values.
• Compute the anomaly between the re-trended values and the projections monthly climatology.
• Sum the anomalies to the reanalysis climatology to obtain the bias corrected values.

7. Appendix 2: Regional layers

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

8. Related articles

• Page:
  Windstorm footprints: Product User Guide
• Page:
  NEMO-ERSEM and POLCOMS-ERSEM: User Guides for Products (v1.1)
• Page:
  Sea level and ocean wave time series and indicators
• Page:
  Product user guide for sea level and ocean wave products - time series and indicators
• Page:
  ECDE indicators catalogue entry: Product User Guide