Last modified on Nov 06, 2024 11:49

Contributors: The Climate Data Factory and B-Open.

Issued by: The Climate Data Factory.

Issued Date: May 2024.

Ref: C3S3_430a – ECDE maintenance and development.

Official reference number service contract: 2021/C3S2_430a_BOPEN.

Acronyms

1. Introduction

This document describes the indicators presented in the European Climate Data Explorer (ECDE) visualisation application (referred to as ECDE application hereafter). The list and definitions of these indicators are based on a technical report from the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They were produced from existing C3S CDS datasets using a set of CDS Toolbox workflows ensuring total reproducibility within the CDS. One exception is the daily SST projections that were downloaded from the Earth System Grid Federation (ESGF). The data underlying the ECDE application was recently published in the C3S CDS*.  

The next section first gives a general description of the underlying  datasets and their specifications. Second, the indicators are listed and defined. Third, the ensemble of models used for each indicator are described in order to give  a sense of the coherence of the underlying datasets. Finally, some specific processing that was necessary for some indicators is described.

*Copernicus Climate Change Service (C3S) (2024): ECDE indicators dataset underpinning the ECDE Indicators visualisation application. C3S Climate Data Store (CDS). https://cds-beta.climate.copernicus.eu/datasets/sis-ecde-climate-indicators?tab=overview

2. Indicators description

2.1. Input datasets

The ECDE indicators are based on several pre-existing C3S CDS reanalysis and projection datasets with different spatial and temporal resolutions. The majority of the ECDE indicators were computed using ERA single levels as a reference baseline and the SIS Energy dataset for future evolution. For some particular indicators (hydrology and ocean typically) more specific input datasets were used with different model simulations. Table 1 presents all the input datasets with a link to the CDS catalogue entry and their main spatial and temporal specifications. The following sections provide more details and the set of climate models used for each input datasets.

Table 1: List of input datasets with their main spatial and temporal specifications.

2.1.1. 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 CDS dataset documentation. 

Table 2: The 9 GCM/RCM combinations of the Climate and energy indicators for Europe.

2.1.2. ERA5 single level

The ERA5 reanalysis is regarded as a good proxy for observed atmospheric conditions and currently covers 1940 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 (CEMS) for the Global ECMWF Fire Forecasting model (GEFF) and the European Forest Fire Information System (EFFIS). More technical specifications can be found in the CDS dataset documentation.

2.1.3. 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 CDS dataset documentation. 

2.1.4. 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 3: The 8 GCM/RCM combinations of the water related indices dataset.

2.1.5. SIS Tourism (FWI)

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 CDS dataset documentation. 

Table 4: The 5 GCM/RCM combinations of the Fire danger indicators dataset.

2.1.6. SIS Tourism (Snow)

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 CDS dataset documentation. 

Table 5: The 9 GCM/RCM combinations of the snow indicators dataset.

2.1.7. SIS EU Services (Sea-level)

The 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. Data are available for future period corresponding on the SSP 5-8.5 highest scenario.

Table 6: The 5 GCM of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. 

2.1.8. SIS EU Services (Sea Level Indicators)

This dataset provides three different 30-year periods: 1951-1980, 1985-2014, and 2021-2050. The future period is based on global climate projections using the high-emission scenario SSP5-8.5. The dataset is based on climate forcing from ERA5 global reanalysis and 4 Global Climate Models (GCMs) of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. Data are available for future period corresponding on the SSP 5-8.5 highest scenario.

Table 7: The 4 GCM  combinations of the Storm Surge indicator.

2.1.9. CMIP6

The ESGF portal provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For sea surface temperature 34 models are available for both SSP 2-4.5 and SSP 5-8.5. Out of these models 23 provide a daily resolution for both SSP 2-4.5 and SSP 5-8.5 which is required for the computation of Marine Heatwave Days.

Table 8: The 34 GCM from CMIP6 ensemble for the Sea Surface Temperature and the subsample (23) for the Marine Heatwaves.

2.1.10. SIS EU Fisheries

The dataset contains model projections of changes in marine physics and biogeochemistry and the lower trophic levels of the marine food web across the Northwest European Shelf and Mediterranean Sea out to the year 2100. The dataset has been produced using the marine ecosystem model, ERSEM v15.06 (European Regional Seas Ecosystem Model), coupled to the regional ocean circulation models, POLCOMS (the Proudman Oceanographic Laboratory Coastal Ocean Modelling System) and NEMO (Nucleus for European Modelling of the Ocean) using the FABM (Framework for Aquatic Biogeochemical Models) coupler.

List of coupled models: NEMO-ERSEM, POLCOMS-ERSEM

2.1.11. SIS EU Health

The Tiger Mosquito indicators are from  Climatic suitability for the presence and seasonal activity of the Aedes albopictus mosquito for Europe derived from climate projections dataset. The dataset is based on a set of 8 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. The index data simulations have yearly resolution where each year represents the 30-yr smoothed average around that particular  year and cover scenarios RCP4.5 and RCP8.5. More technical specifications can be found in the CDS dataset documentation. 

Table 8: The 8 GCM/RCM combinations of the Tiger Mosquito dataset.

2.2. Indicators overview

Table 9 shows the set of 38 indicators accessible through the ECDE application. This set of indicators is subdivided in six hazard category: Heat and cold, Wet and Dry, Snow and ice, Coastal, Oceanic and Healthcare. For each indicator, the definition is given with the relevant impacted sectors and the origin of the dataset, described in Table 1.

The various indicators and variables cover the following ECDE sectors (several sectors for one indicator is possible):

• All sectors: 4
• Agriculture: 14
• Energy: 5
• Health: 8
• Water and coastal: 15
• Forestry: 3
• Tourism: 5

Table 9: List of indicators by hazard category with sectors and origin dataset links.

2.3. Indicators specifications

This section details the specifications of the ECDE indicators by giving their short name, a context comment, their definition, they essential climate variable (ECV) their are based on, their unit, their reference dataset, and the relevant sector(s). The indicators are listed by hazard category.  

2.3.1. Heat and Cold

2.3.1.1. Mean Temperature

Short name t
Comment 'Mean temperature' is a high-priority index with a wide range of applications. Changes in annual mean temperature are often used as a headline index in regional climate change assessments whereas changes in seasonal mean temperature are relevant for various sectoral applications.
Definition Monthly, seasonal or annual mean surface temperature.
ECV 2 m daily mean temperature.
Unit C° (degree celsius).
Reference dataset SIS Energy.
Sector All sectors.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.2. Growing Degree Days

Short name gdd
Comments 'Growing degree days' is a high-priority index for agriculture. This index uses cumulative degrees above a threshold from daily mean temperature. In some cases, an upper temperature limit is applied as well. National or regional applications may use different temperature thresholds to account for local conditions and crop types.
Definition The sum of daily degrees above the daily mean temperature of 278,15° K (5°C). The data is aggregated over the months. Source: from "Downscaled bioclimatic indicators for selected regions from 1950 to 2100 derived from climate projections").

ECV 2 m daily mean temperature.
Unit C°*Days (degree days).
Reference dataset SIS Energy.
Sector Agriculture.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.3. Heating Degree Days

Short name hd
Comments The index Heating degree days (HDD) is a proxy for the energy needed to heat a building.
Definition There are different implementations, but the specific variant adopted here is the one from Spinoni et al. 2018 that has a threshold value of 15.5 °C.

ECV 2 m daily minimum and maximum temperature.
Unit C°*Days (degree days). .
Reference dataset SIS Energy.
Sector Energy.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.4. Cooling Degree Days

Indicator number #4
Short name cd
Comments A high-priority index for the energy sector. Different implementations use different underlying data (e.g. daily mean vs. daily minimum and maximum temperature), threshold temperatures, and possible seasonal restrictions (e.g. only predefined heating or cooling season).
DefinitionThere are different implementations, but the specific variant adopte here is the one from Spinoni et al. 2018 that has a threshold value of 22°C.

ECV 2 m daily minimum and maximum temperature.
Unit C°*Days (degree days)
Reference dataset SIS Energy.
Sector Energy.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.5. Maximum Temperature

Short name txx
Comment Surface air maximum temperature is an essential climate variable making it fundamental for following climate variability and change. The “Maximum Temperature'' provides pan-European information relevant to this index in the present and in the future until the end of the century.
Definition  Monthly, seasonal or annual maximum surface temperature.
ECV 2 m daily maximum temperature.
Unit C° (degree celsius)
Reference dataset SIS Energy.
Sector All sectors.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.6. Minimum Temperature

Short name tnn
Comment 'Minimum temperature' is an essential climate variable making it fundamental for following climate variability and change. The “Minimum Temperature'' application provides pan-European information relevant to this index in the present and in the future until the end of the century.
Definition Monthly, seasonal or annual minimum surface temperature.
ECV 2 m daily minimum temperature.
Unit C° (degree celsius)
Reference dataset SIS Energy.
Sector All sectors.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.7. Tropical Nights

Short name tr
Comments 'Tropical nights' is a high-priority index with relevance for human health.
Definition The Annual count of days when daily minimum temperature > 20°C. Source ETC-CCA report and "Climate extreme indices and heat stress indicators derived from CMIP6 global climate projections" dataset.
ECV 2 m daily minimum temperature.
Unit Number of Days.
Reference dataset SIS Energy.
Sector Health.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.8. Hot Days

Short name hotd
Comments The index Hot days counts the number of days over a certain time interval with daily maximum temperature above a threshold. The 30 °C threshold is considered suitable for a pan-European perspective. Other temperature values, for example 35 °C and 40 °C, could be used as additional thresholds for warmer regions.
Definition The count of days with a daily minimum temperature > 30°C. Source ETC-CCA report.
ECV 2 m daily maximum temperature.
Unit Number of Days (over a certain period, usually annually).
Reference dataset SIS Energy.
Sector Health.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.9. Warmest Three Day Period

Short name w3d
Comments The index measures heatwave intensity and is mainly applicable in health-related sectors and agriculture
DefinitionThe index reports the highest daily mean temperature averaged over a three- day window over the year. Source ETC-CCA report referring to World Weather Attribution (2017): Euro-Mediterranean heat, summer 2017 (link).
ECV 2 m daily mean temperature.
Unit °C.
Reference dataset SIS Energy.
Sector Health, agriculture.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.10. Apparent Temperature Heatwave Days

Short name hw-t
Comments The index counts the number of days in a year within prolonged periods of extreme humid heat conditions. The apparent temperature is a measure of relative discomfort due to combined heat and high humidity, based on physiological studies on evaporative skin cooling. It can be calculated at hourly scale as a combination of air and dew point temperature:
Definition Heatwaves are defined as periods of at least two consecutive days during the summer months (June, July, August) in which the maximum apparent temperature (T) and app minimum temperature exceed their corresponding 90th percentiles for each month computed over the control period 1979–2008. Source ETC-CCA according to the EuroHEAT definition (Michelozzi et al., 2007; WHO Europe and EC, 2009).
ECV 2 m daily minimum temperature, 2 m hourly air temperature and dew-point temperature
Unit Number of days.
Reference dataset SIS Health.
Sector Health
Reanalysis period Not available 
Historical simulation period 1986 - 2005.
Future simulation period 2006 -2085 (RCP 4.5 , RCP 8.5).

2.3.1.11. Climatological Heatwave Days

Short name hw-c
Comments The index counts the number of days in a year within prolonged periods of unusually high temperatures.
Definition A period of at least three consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September season during the reference period (here 1971–2000). Source ETC-CCA according to EURO-CORDEX definition in Jacob et al 2013 (link).
ECV 2 m daily maximum temperature
Unit Number of days.
Reference dataset SIS Energy.
Sector Health.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.1.12. Tiger Mosquito Climatic Season Length

Short name moscsl
Comments The index counts the number of days in a year within prolonged periods of unusually high temperatures.
Definition The season length is defined along a GIS-based seasonal activity model as the time when insect's eggs hatch after winter until when the eggs are no longer hatching (going in diapause) in autumn. The model of Medlock et al. (2006) is used, that is based on the overwintering criterion with weekly temperatures and photoperiods to simulate the weeks of activity. The suitability of the mosquito to survive the winter is based on the January temperature and the annual rainfall. If the January temperature is below 0 °C and the annual rainfall below 500 mm, then it is not suitable on this specific location.
ECV 2 m daily maximum temperature, Daily total precipitation.
Unit Number of days.
Reference dataset SIS Health.
Sector Health, Tourism.
Reanalysis period Not available 
Historical simulation period 1986 - 2005.
Future simulation period 2006 -2085 (RCP 4.5 , RCP 8.5).

2.3.1.13. Tiger Mosquito Climatic Suitability

Short name moscs
Comments Likelihood of favourable environmental conditions for Aedes albopictus presence (0 to 100 index) averaged over the selected region and time span.
Definition Suitability maps of Aedes albopictus are generated based on the Multi-criteria decision analysis after ECDC (ECDC 2009). This approach considers empirical suitability functions, which link a number of (aggregated) climate variables to the suitability of a habitat for a given vector species,
e.g. for a species to be active a minimum threshold of temperature is required below which the species is not active. These suitability functions are presented by sigmoidal functions with intervals ranging between 0 and 255 (Figure 2). More specifically, the suitability was reduced to zero when the annual rainfall was lower than 450 mm, and maximum suitability was reached when the annual rainfall was higher than 800 mm. For summer temperatures, the suitability was zero when temperatures were lower than 15 °C and higher than 30 °C, and maximum between 20 °C and 25 °C. For January temperatures, the suitability was zero when temperatures were lower than - 1°C and maximum when temperatures were higher than 3 °C.
ECV 2 m daily maximum temperature, Daily total precipitation.
Unit Dimensionless.
Reference dataset SIS Health.
Sector Health, Tourism.
Reanalysis period Not available 
Historical simulation period 1986 - 2005.
Future simulation period 2006 -2085 (RCP 4.5 , RCP 8.5).

2.3.1.14. High UTCI Days

Short name utci
Comments The index was developed as a universal heat-related health risk index for Europe. The existing dataset is based on ERA5 and there is no projection dataset. The index is provided for present climate only.
Definition The calculation involves 4 ECVs hourly values and is quite sophisticated. Details can be found in DI Napoli et al 2018 (link). The count of days when UTCI remains above 32°C. UTCI stands for Universal Thermal Climate Index and is an equivalent to temperature (°C) corresponding to a measure of the human physiological response to meteorological conditions that also takes into consideration the clothing adaptation of the population in response to outdoor temperature. It is based on four surface variables: air temperature, relative humidity, wind speed and mean radiant temperature.
ECV 2 m air temperature, relative humidity, 10 m wind speed and radiation fluxes at the Earth's surface.
Unit Number of days.
Reference dataset ERA5 Heat
Sector Health.
Reanalysis period 1940 - present.
Historical simulation period Not available 
Future simulation period Not available 

2.3.1.15. Frost Days

Short name fd
Comments The index counts the number of days over a certain period with daily minimum temperature below 0 °C. It is mainly applied in agriculture to account for frost damages, but it is also relevant for the transportation sector.
Definition Number of days with daily minimum temperature below 0 °C
ECV 2 m daily minimum temperature
Unit Number of days.
Reference dataset SIS Energy.
Sector Agriculture, energy.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2. Wet and Dry

2.3.2.1. Total Precipitation

Short name pr
Comments A fundamental index representing the amount of precipitation at different timescales (e.g. monthly, seasonal, annual).
Definition Cumulated or averaged precipitation over a period.
ECV Daily total precipitation.
UnitMM or mm/day.
Reference dataset SIS Energy.
Sector all sectors.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2.2. Maximum Consecutive Five-Day Precipitation

Short name rx5day
Comments The index reports the maximum precipitation sum occurred over 5 consecutive days in a given period.
Definition Maximum five days cumulated or averaged precipitation over a period.
ECV Daily total precipitation.
Unit mm or mm/day.
Reference dataset SIS Energy.
Sector Agriculture, water and coastal.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2.3. Extreme Precipitation Total

Short name prex
Comments The index reports the total precipitation on all days with heavy precipitation, defined as exceeding the 99th percentile of daily precipitation over the reference period.
Definition The total sum in a year of daily precipitation values exceeding the 99th percentile of the reference period.
ECV Daily total precipitation.
Unit mm.
Reference dataset SIS Energy.
Sector Agriculture, water and coastal.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2.4. Extreme Precipitation Days

Short name fprex
Comments The index reports the number of days over a certain period with daily total precipitation exceeding the 95th percentile of the reference interval.
Definition Number of days with daily total precipitation exceeding the 95th percentile over a period. Source ETC-CCA report referring to Myhre et al. 2019 (link).
ECV Daily total precipitation.
Unit Number of days
Reference dataset SIS Energy.
Sector Agriculture, water and coastal.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2.5. Flood Recurrence

Short name rfr
Comments The index reports the values of annual maximum river discharge for the 50-year return periods For future periods the indicator can be given as a relative change against the reference period (1971-2000).
Definition Absolute of relative change of annual maximum river discharge against the reference period 1971-2000. Source SIS OWS PUG.
ECV River discharge.
Unit m3 s-1 or %
Reference dataset SIS Operational Water Service.
Sector Agriculture, water and coastal.
Reanalysis period Not available 
Historical simulation period 1971 - 2000. 
Future simulation period 2011-2040, 2041-2070 and 2071-2100 (RCP 4.5 , RCP 8.5).

2.3.2.6. River Discharge

Short name rd
Comments The index reports the mean annual daily maximum discharge over a 30 year period. For future periods the indicator can be given as relative change against the reference period (1971-2000).
Definition Absolute or relative change of annual mean daily maximum river discharge against the reference period 1971-2000. Source SIS OWS PUG.
ECV River discharge.
Unit m3 s-1 or %
Reference dataset SIS Operational Water Service.
Sector Agriculture, water and coastal.
Reanalysis period Not available 
Historical simulation period 1971 - 2000. 
Future simulation period 2011-2040, 2041-2070 and 2071-2100 (RCP 4.5 , RCP 8.5).

2.3.2.7. Aridity Actual

Short name aridity
Comments The index reports the ratio between actual evapotranspiration and total precipitation accumulated over a certain time period. Actual evapotranspiration is retrieved from the outputs of hydrological models.
DefinitionThe index is calculated as the monthly mean values of the ratio between actual evapotranspiration and precipitation over a 30 year period. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Source SIS OWS PUG.
ECV Daily total precipitation, output of hydrological models.
Unit Dimensionless.
Reference dataset SIS Operational Water Service.
Sector Agriculture, water and coastal.
Reanalysis period Not available 
Historical simulation period 1971 - 2000. 
Future simulation period 2011-2040, 2041-2070 and 2071-2100 (RCP 4.5 , RCP 8.5).

2.3.2.8. Consecutive Dry Days

Short name cdd
Comments The index reports the maximum number of consecutive dry days over a 30 year period as a relative change against the reference period.
DefinitionThe index is calculated as the longest dry period over a certain time period with daily precipitation below 1 mm.
ECV Daily total precipitation.
Unit Number of days.
Reference dataset SIS Energy.
Sector Agriculture, water and coastal.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2.9. Meteorological Drought Duration

Short name spi3d
Comments The index reports the total number of months in a year that experience drought conditions determined by anomalously low precipitation values as characterised by the Standard Precipitation Index (SPI).
Definition The index is based on the 3-month Standardised Precipitation Index (SPI-3), which accounts for the deficit (or surplus) of precipitation accumulated over 3 months with respect to the corresponding reference value from a 30 year baseline in the historical period. The precipitation values over the baseline period are fitted by a Gamma probability distribution, which is then transformed into a normal distribution so that the SPI mean for the period is zero. SPI values are therefore the number of standard deviations from the long-term mean and can be used to compare different geographical locations and timescales. According to the most common definition a drought event starts when SPI values fall below -1 for at least two consecutive months and ends when the index returns positive. Source ETC-CCA report with reference to Spinoni et al. 2020 (link).
ECV Daily total precipitation.
Unit Number of months.
Reference dataset SIS Energy.
Sector Agriculture, water and coastal.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2.10. Meteorological Drought Magnitude 

Short name spi3m
Comments The index reports the cumulated severity of drought events as determined by anomalous low precipitation values as characterised by the Standard Precipitation Index (SPI).
Definition The index is based on the 3-month Standardised Precipitation Index (SPI-3), which accounts for the deficit (or surplus) of precipitation accumulated over 3 months with respect to the corresponding reference value from a 30 year baseline in the historical period. The precipitation values over the baseline period are fitted by a Gamma probability distribution, which is then transformed into a normal distribution so that the SPI mean for the period is zero. SPI values are therefore the number of standard deviations from the long-term mean and can be used to compare different geographical locations and timescales. According to the most common definition a drought event starts when SPI values fall below -1 for at least two consecutive months and ends when the index returns positive. The severity of a drought event is computed as the sum of SPI absolute values in the months included in the drought episode. Source ETC-CCA report with reference to Spinoni et al. 2020 (link).
ECV Daily total precipitation.
Unit Number of months.
Reference dataset SIS Energy.
Sector Agriculture, water and coastal.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.2.11. Mean Soil Moisture

Short name dsmd
Comments The index reports the total number of months in a year experiencing soil drought conditions.
DefinitionSoil droughts are defined as months with a soil moisture content (cumulated over all soil layers) below the 20th percentile from a 30-year reference period. Source ETC-CCA report with reference to Spinoni et al. 2020 (link).
ECV Soil moisture.
Unit Number of months.
Reference dataset SIS Operational Water Service.
Sector Agriculture, water and coastal, forestry.
Reanalysis period Not available 
Historical simulation period 1971 - 2000. 
Future simulation period 2011 - 2040, 2041 - 2070 and 2071 - 2100 (RCP 4.5 , RCP 8.5).

2.3.2.12. Fire Weather Index

Short name fwi
Comments The fire weather index values at a daily temporal resolution for the selected year. The higher the index value, the more favorable the meteorological conditions to trigger a wildfire are.
Definition The Canadian Fire Weather Index System (FWI) is a meteorologically based index, used worldwide to estimate fire danger in a generalized fuel type (mature pine stands). It consists of different components that assess the responses of moisture to atmospheric forcing at different soil depths and then combine these in order to derive fire behavior indices in terms of ease of spread and intensity. For the calculation of the index, daily noon values of air temperature, relative humidity, wind speed and 24-h accumulated precipitation are required. It is implemented in the Global ECMWF Fire Forecasting model (GEFF).
ECV Daily temperature (mean, max), total precipitation, minimum relative humidity, wind speed.
Unit Dimensionless.
Reference dataset SIS EU Tourism.
Sector Forestry.
Reanalysis period Not available 
Historical simulation period 1970 - 2005. 
Future simulation period 2006 - 2100 (RCP 4.5 , RCP 8.5).

2.3.2.13. High Fire Danger Days

Short name fwid
Comments The index is based on the Canadian Fire Weather Index (FWI). The FWI is a numerical rating of the meteorological forest fire danger that combines assessments of fire ignition and spread. The index reports the total number of days in a year with FWI above a threshold.
Definition Number of days per year with Fire Weather Index greater than 30 based upon the European Forest Fire Information System (EFFIS) classification. Source SIS EU Tourism PUG. See also ETC-CCA report with reference to EFFIS (link) and Rigo et al 2017 (Link).
ECV Daily temperature (mean, max), total precipitation, minimum relative humidity, wind speed.
Unit Number of days.
ECV Daily temperature (mean, max), total precipitation, minimum relative humidity, wind speed.
Unit Number of days.
Reference dataset SIS EU Tourism.
Sector Forestry.
Reanalysis period Not available 
Historical simulation period 1970 - 2005. 
Future simulation period 2006 - 2100 (RCP 4.5 , RCP 8.5).

2.3.3. Wind

2.3.3.1. Mean Wind Speed

Short name sfcwind
Comments The index represents the mean horizontal wind speed over a given period.
Definition Mean 10m wind speed over a period. ETC-CCA report with reference Tobin et al 2017 (Link).
ECV 10 m daily wind speed.
Unit m∙ s-1.
Reference dataset SIS Energy.
Sector Energy.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.3.2. Extreme Wind Speed Days

Short name sfcwindex
Comments The index counts the number of days in a year with daily maximum wind speed above the 98th percentile computed over a reference period.
Definition Number of days with daily 10m wind speed over the 98th percentile over a period. ETC-CCA report with reference Spinoni et al 2020 (Link).
ECV 10 m daily wind speed.
Unit Number of days
Reference dataset SIS Energy.
Sector Energy.
Reanalysis period 1940 - present.
Historical simulation period 1970 - 2005.
Future simulation period 2006 -2098 (RCP 4.5 , RCP 8.5).

2.3.4. Snow and Ice

2.3.4.1. Snowfall Amount

Short name prsn
Comments The index represents the cumulative value of snowfall precipitation over the winter season from November to April.
Definition Cumulative value of snowfall precipitation over the winter sports season (November year N-1 to April year N). SIS EU Tourism PUG and ETC-CCA report with reference Soci et al 2016 (Link).
ECV Total snow precipitation.
Unit kg m-2
Reference dataset SIS EU Tourism.
Sector Tourism.
Reanalysis period Not available 
Historical simulation period 1950 - 2005. 
Future simulation period 2006 - 2100 (RCP 4.5 , RCP 8.5).

2.3.4.2. High Snow Days

Short name prsnmax
Comments The index counts the number of days where the water equivalent of natural snow is above a given threshold over the winter season from November to April.
DefinitionNumber of days where the water equivalent of natural snow is above 120 kg m-2 given over the winter season from November to April. SIS EU Tourism PUG and ETC-CCA report with reference Vionet et al 2012 (Link).
ECV Total snow precipitation.
Unit kg m-2.
Reference dataset SIS EU Tourism.
Sector Tourism.
Reanalysis period Not available 
Historical simulation period 1950 - 2005. 
Future simulation period 2006 - 2100 (RCP 4.5 , RCP 8.5).

2.3.5. Coastal

2.3.5.1. Relative Sea Level Rise

Short name slr
Comments The index represents the annual mean sea level height relative to the 1986-2005 period. It includes geophysical sources that drive long-term changes, such as ice and ocean related components, land water storage and glacial isostatic adjustment, except for local land movement effects.
Definition Difference of average height of sea water in a given year and region in comparison to a reference period. Source C3S 435 Lot 8 Deltares PUG.
ECV Sea level height.
Unit m
Reference dataset C3S 435 Lot 8 Deltares.
Sector Tourism.
Reanalysis period Not available 
Historical simulation period 1986 - 2005. 
Future simulation period 2021 - 2030, 2031 - 2040 and 2041 - 2050 (SSP 5-8.5).

2.3.5.2. Extreme Sea Level

Short name slrex
Comments The index represents the Total water level value for a return period of 100 years caused by tidal and surge levels as well as their interactions but without including sea level rise.
Definition Annual highest high water relative to the mean of the mean sea level during the reference period 1977-2005. Source C3S 435 Lot 8 Deltares PUG.
ECV Total water level.
Unit m
Reference dataset C3S 435 Lot 8 Deltares.
Sector Agriculture, water and coastal.
Reanalysis period Not available 
Historical simulation period 1986 - 2005. 
Future simulation period 1951 - 1980, 1985 - 2014 and 2041 - 2050 (SSP 5-8.5).

2.3.6. Oceanic

2.3.6.1. Sea Surface Temperature

Short name sst
Comments The index represents the mean sea surface temperature for different periods.
Definition Mean sea surface temperature over a period. SIS EU Fisheries PUG and ETC-CCA report with reference Alexander et al 2018 (link).
ECV Sea surface temperature.
Unit°C.
Reference dataset CMIP6.
Sector Water and coastal.
Reanalysis period Not available. 
Historical simulation period 1850 - 2014. 
Future simulation period 2015 - 2100 (SSP 2-4.6, SSP 5-8.5).

2.3.6.2. Marine Heatwave Days

Short name dmhw
Comments The index represents the total number of days in a year where a marine region experiences unusually warm sea temperature.
Definition The Marine Heatwave Days index (MHD) is defined as the count of days under marine heatwaves conditions (Number of days). A Marine Heatwave day is a day exceeding the daily climatological 90th percentile. The daily climatological 90th percentile is evaluated in two steps. First, it is computed over a 11-day window centred on the calendar day for a reference period (here 1981-2010). Then, the obtained daily quantiles are smoothed with a 30-day running mean.

The Marine Heatwave Days are also classified into intensity categories. Those are set based on a scale defined by the local difference between the daily climatological mean and the daily climatological 90th percentile (diff). The multiple of the local difference is used to set the Marine Heatwave category defined as:

- Category 1, Moderate (≥ 1 diff)

- Category 2, Strong (≥ 2 diff) 

- Category 3, Severe (≥ 3 diff) 

- Category 4, Extreme (≥ 4 diff).

ECV Sea surface temperature.
Unit Number of days.
Reference dataset CMIP6.
Sector Water and coastal.
Reanalysis period Not available. 
Historical simulation period 1850 - 2014. 
Future simulation period 2015 - 2100 (SSP 246, SSP 585).

2.3.6.3. Dissolved Oxygen Level

Short name dol
Comments The index is defined as the average of mean oxygen concentration over different periods.
DefinitionAverage of mean oxygen concentration over depth levels and periods. Source ETC-CCA report with reference Wakelin et al 2020 (link) and PUG.
ECV Dissolved oxygen.
Unit mol m-3.
Reference dataset SIS EU Fisheries.
Sector Water and coastal.
Reanalysis period Not available. 
Historical simulation period Not available. 
Future simulation period 2011 - 2040, 2041 - 2070 and 2071 - 2100 (RCP 4.5, RCP 8.5).

2.3.6.4. Ocean pH Level

Short name ph
Comments The index Ocean pH level is defined as the mean rate of ocean acidification over different periods.
Definition Seawater pH over different depths and periods. Source ETC-CCA report with reference Wakelin et al 2020 (link).
ECV Seawater pH.
Unit Dimensionless.
Reference dataset SIS EU Fisheries.
Sector Water and coastal.
Reanalysis period Not available. 
Historical simulation period Not available.  
Future simulation period 2011 - 2040, 2041 - 2070 and 2071 - 2100 (RCP 4.5, RCP 8.5).

2.4.  Model ensembles 

The climate projection datasets listed in Table 1 are based on multi-model ensembles including simulations from the historical and future scenarios (RCPs or SSPs). In order to compute climate change information, simulations from both historical and future scenarios are required. Therefore, in the ECDE dataset we consider only those models providing simulations for the historical period and, for one future scenarios RCP4.5/SSP2-4.5 (blue) and RCP8.5/SSP5-8.5 (orange) (Table 12).

Table 10: Model ensemble used in the ECDE indicators with models (columns) by  indicators (lines). Colours indicate the different scenarios available in the ECDE indicators: grey for historical, blue for RCP4.5/SSP2-4.5 and orange for RCP8.5/SSP5-8.5.

2.5.  Specific data processing

2.5.1. Fire Weather index

Two particular data processings were applied to the original FWI index data in this application. First, as the FWI is not suitable to be averaged over space or time, a non-linear transformation into the Daily Severity Rating (DSR) is necessary (DSR = 0.0272 FWI1.77). DSR is intended to be directly proportional to the expected effort required for fire suppression and control and is suitable for space or and time averaging. Any FWI spatially or temporally averaged values are thus inferred through the DSR. Second, as the simulations FWI have a bias, a bias-correction procedure was implemented (see Appendix 3) 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.   

3. Versions of the document 

4. Related articles

 A. Crespi, S. Terzi, S. Cocuccioni, M. Zebisch, J. Berckmans, H.-M. Füssel “Climate-related hazard indices for Europe”. European topic Centre on climate change impacts Vulnerability and Adaptation (ETC/CCA) Technical Paper 2020/1 (2020).

Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le moigne, P., Martin, E., Willemet, J.-M., 2012. The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2. Geosci. Model Dev. 773–791. https://doi.org/10.5194/gmd-5-773-2012

Alexander, M.A., Scott, J.D., Friedland, K.D., Mills, K.E., Nye, J.A., Pershing, A.J., Thomas, A.C., 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elementa 6. https://doi.org/10.1525/elementa.191

Frölicher, T.L., Fischer, E.M., Gruber, N., 2018. Marine heatwaves under global warming. Nature 560, 360–364. https://doi.org/10.1038/s41586-018-0383-9

Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M., 2018. Changes of heating and cooling degree-days in Europe from 1981 to 2100.  Int. J. Climatol, 38(51), e191-e208. https://doi.org/10.1002/joc.5362

Spinoni, J., Formetta, G., Mentaschi, L., Forzieri, G. et al., Global warming and windstorm impacts in the EU – JRC PESETA IV project – Task 13, European Commission: Joint Research Centre, Publications Office, 2020, https://data.europa.eu/doi/10.2760/039014

Spinoni, J., Barbosa, P., Bucchignani, E., Cassano, J., Cavazos, T., Christensen, J. H., Christensen, O. B., Coppola, E., Evans, J., Geyer, B., Giorgi, F., Hadjinicolaou, P., Jacob, D., Katzfey, J., Koenigk, T., Laprise, R., Lennard, C. J., Kurnaz, M. L., Li, D., Llopart, M., McCormick, N., Naumann, G., Nikulin, G., Ozturk, T., Panitz, H., Porfirio da Rocha, R., Rockel, B., Solman, S. A., Syktus, J., Tangang, F., Teichmann, C., Vautard, R., Vogt, J. V., Winger, K., Zittis, G., & Dosio, A., 2020. Future Global Meteorological Drought Hot Spots: A Study Based on CORDEX Data. Journal of Climate, 33(9), 3635-3661.  https://doi.org/10.1175/JCLI-D-19-0084.1

Wakelin, S.L., Artioli, Y., Holt, J.T., Butenschön, M., Blackford, J., 2020. Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario. Progr. Oceanogr., 187, 102400. https://doi.org/10.1016/j.pocean.2020.102400

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