European Climate Data Explorer

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1	Identifier	Sector_1	Sector_2	Sector_3	HazardCategory	HazardType	PageTitle	IndexTitle	IndexSentence	Units	DateRange	Filter_1	Filter_2	Filter_3	Filter_4	Context	Index_1	Index_2	Calculation_1	Calculation_2	Calculation_3	Calculation_4	DataSource_1	DataSource_2	Resolution	Explanation_1	Explanation_2	Dropdowns_1	Dropdowns_2	Dropdowns_3	Dropdowns_4	Explore_1	Explore_2	Explore_3	Acknowledge	FurtherInfo	CDS

2	C3S_434_002	Health					Climatic Suitability for the Tiger Mosquito - Season Length	Climatic suitability for the tiger mosquito - season length		(days)	1971-2099	Scenario_45_85				The tiger mosquito (Aedes albopictus) transmits vector-borne diseases, such as dengue and chikungunya. Environmental factors such as temperature and rainfall impact the survival chance and seasonal activity of the tiger mosquito which is a serious threat for human health in Europe.	The season length, in days, of the climatic suitability for the presence of the tiger mosquito	(Aedes albopictus)	is determined by temperature statistics and hours of sunlight (photoperiod).	The data covers the period 1971 to 2099 and statistics are averaged for 30 years in overlapping time periods set 10 years apart.	Finally, the time series are averaged for the model ensemble.		The season length for tiger mosquito suitability is calculated using bias-adjusted EURO-CORDEX data	for two CMIP5 scenarios with different possible future greenhouse gas emissions: RCP4.5 (medium emissions) and RCP8.5 (high emissions).	The horizontal resolution of the data is 0.1°x0.1°.	The duration of tiger mosquito presence is also known as the mosquito season. It begins when the insect’s eggs hatch after winter and continues until the eggs are no longer hatching (going in diapause) in autumn.	The egg hatching in spring is based on two criteria: the photoperiod should be above 11.25 hours and the spring temperature should be above 10.5 °C. The autumn diapause of the mosquito is determined by the autumn temperature that should be below 9.5 °C and the photoperiod that should be below 13.5 hours.	The season length of the climatic suitability for the tiger mosquito is presented	for two CMIP5 scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.			Statistics for the season length of the climatic suitability for the tiger mosquito	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-health-vector?tab=overview).
3	C3S_434_003	Health					Climatic Suitability for the Tiger Mosquito - Suitability Index	Climatic suitability for the tiger mosquito - suitability index			1971-2099	Scenario_45_85				The tiger mosquito (Aedes albopictus) transmits vector-borne diseases, such as dengue and chikungunya. Environmental factors such as temperature and rainfall impact the survival chance and seasonal activity of the tiger mosquito which is a serious threat for human health in Europe.	The climatic suitability index for the presence of the tiger mosquito	(Aedes albopictus)	is determined by annual rainfall, summer temperatures and January temperatures.	The data covers the period 1971 to 2099 and statistics are averaged for 30 years in overlapping time periods set 10 years apart.	Finally, the time series are averaged for the model ensemble.		The tiger mosquito suitability statistics are calculated using bias-adjusted EURO-CORDEX data	for two CMIP5 scenarios with different possible future greenhouse gas emissions: RCP4.5 (medium emissions) and RCP8.5 (high emissions).	The horizontal resolution of the data is 0.1°x0.1°.	A suitability index of 0 indicates that an area is not suitable (has no favourable environmental conditions) for tiger mosquito presence whereas an area with an index of 100 is totally suitable.	For annual rainfall, the suitability is zero when rainfall is lower than 450 mm, maximum suitability is reached when the annual rainfall is higher than 800 mm. For summer temperatures, the suitability is zero when temperatures are lower than 15 °C and higher than 30 °C, and maximum between 20 °C and 25 °C. For January temperatures, the suitability is zero when temperatures are lower than - 1°C and maximum when temperatures are higher than 3 °C.	The climatic suitability index for the tiger mosquito is presented	for two CMIP5 scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.			Statistics for the climatic suitability index of the tiger mosquito	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-health-vector?tab=overview).
4	C3S_434_004	Forestry			Wet and Dry	Wildfire	Fire Weather Index - Monthly Mean	Fire weather index			1979-2020	Month				The Canadian Fire Weather Index System (FWI) is used to assess fire danger in a harmonized way across Europe. The FWI uses information about fuel moisture and weather conditions to determine fire behaviour.	The fire weather index indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed.		The fire weather index is calculated using the Canadian Forest Service Fire Weather Index rating system (FWI).	The fire danger model used to produce the dataset is the Global ECMWF Fire Forecast model (GEFF).	The fire danger model takes into account temperature, relative humidity, wind speed, precipitation, drought conditions, fuel availability, vegetation characteristics and topography.		The fire weather index is calculated using weather forecasts from historical simulations provided by the ECMWF ERA5 reanalysis.	The fire weather index is part of a dataset produced by the Copernicus Emergency Management Service (CEMS) for the [European Forest fire Information System (EFFIS)](https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/).	The horizontal resolution of the data is 0.25°x0.25°.	It is customary to quote a danger class as well as an index number. The fire weather index can be categorised into 6 classes of danger as follows:	Very low danger: FWI is less than 5.2. Low danger: FWI is between 5.2 and 11.2. Moderate danger: FWI is between 11.2 and 21.3. High danger: FWI is between 21.3 and 38.0. Very high danger: FWI is between 38.0 and 50. Extreme danger: FWI is greater than 50.	The fire weather index based on historical ERA5 Reanalysis Short Model data is presented	for each month and year from 1979 to 2020.			Historical fire weather index statistics	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview).
5	C3S_434_005	Forestry			Wet and Dry	Wildfire	Fire Danger Index - for Comparison with the Australian Mark5 Rating System	Fire danger index			1979-2019	Month				Fire danger metrics are part of a dataset produced by the Copernicus Emergency Management Service (CEMS) for the [European Forest fire Information System (EFFIS)](https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/). The fire danger index allows the EFFIS to be compared with the Australian Mark5 rating system.	The fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression.		The fire danger index is calculated using the Australian McArthur (Mark 5) rating system.	The fire danger model used to produce the dataset is the Global ECMWF Fire Forecast model (GEFF).	The fire danger model takes into account temperature, relative humidity, wind speed, precipitation, drought conditions, fuel availability, vegetation characteristics and topography.		The fire danger index is calculated using weather forecasts from historical simulations provided by the ECMWF ERA5 reanalysis.		The horizontal resolution of the data is 0.25°x0.25°.	It is customary to quote a danger class as well as an index number. The fire danger index relates to 6 classes of danger as follows:	0 to 11: low to moderate; 12 to 25: high; 25 to 49: very high. Thereafter the danger class depends on the type of vegetation, forest or grassland. Forest danger classes are: 50 to 75: severe; 75 to 99: extreme; greater than 100: catastrophic. Grassland danger classes are: 50 to 99: severe; 100 to 149: extreme; greater than 150: catastrophic.	The fire danger index based on historical ERA5 Reanalysis data is presented	for each month and year from 1979 to 2019.			Historical fire danger index statistics	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview).
6	C3S_434_006	Health	Energy		Heat and Cold	Extreme Heat	Heat Waves in Europe	Number of heat wave days		(days)	1971-2099	HeatWave	Scenario_45_85			A heat wave is a prolonged period of extremely high temperature for a particular region. Heat waves have consequences for health, agriculture, power, wildfire and infrastructure.	The annual number of heat wave days is based on two definitions, one used in the climate community and another used in the health community, both of which are presented here.		A climate heat wave is considered to be a period of at least 3 consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September season for the control period of 1971 to 2000.	Health heat waves are calculated for the summer period between June and August. Health heat waves are days in which the maximum apparent temperature (Tappmax) exceeds the 90th percentile of Tappmax for each month and the minimum temperature (Tmin) exceeds the 90th percentile of Tmin for each month for at least 2 days. The apparent temperature is a measure of relative discomfort due to combined heat and high humidity.	The data covers the period 1971 to 2099 and statistics are averaged for 30 years in overlapping time periods set 10 years apart.	Finally, the time series are averaged for the model ensemble.	The heat wave statistics are calculated using bias-adjusted EURO-CORDEX data	for two CMIP5 scenarios with different possible future greenhouse gas emissions: RCP4.5 (medium emissions) and RCP8.5 (high emissions).	The horizontal resolution of the data is 0.1°x0.1°.			The number of heat wave days are presented	for either the climatological or health definition of heat waves	for two CMIP5 scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the number of heat wave days	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-heat-and-cold-spells?tab=overview).
7	C3S_434_007	Health					Thermal Comfort Indices - Mean Radiant Temperature	Mean radiant temperature		(°C)	1979-2020	Month				The mean radiant temperature (MRT) is a human biometeorology parameter that is used to assess the linkages between the outdoor environment and human well‐being. Thermal comfort indices describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, wind and radiation.	The mean radiant temperature (°C) is a numerical representation of how human beings experience radiation.		The mean radiant temperature (MRT) is based on the principle that the net exchange of radiant energy between objects is approximately proportional to their temperature difference multiplied by their ability to emit and absorb heat (emissivity).	In this context the MRT applies to a person in an outdoor environment and is a function of the direct, diffuse and reflected thermal and solar radiation they experience.			The mean radiant temperature (MRT) is computed using solar and thermal data from the ECMWF ERA5 reanalysis.		The horizontal resolution of the data is 0.25°x0.25°.			The mean radiant temperature (MRT) is presented	for each month and year from 1979 to 2020.			MRT statistics	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical?tab=overview).
8	C3S_434_008	Health			Heat and Cold	Extreme Heat	Thermal Comfort Indices - Universal Thermal Climate Index	Universal thermal climate index		(°C)	1979-2020	Month				The universal thermal climate index (UTCI) is a human biometeorology parameter that is used to assess the linkages between outdoor environment and human well‐being. Thermal comfort indices describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, wind and radiation.	The universal thermal climate index (UTCI) is an equivalent temperature (°C), it is a measure of the human physiological response to the thermal environment.		The universal thermal climate index (UTCI) describes the synergistic heat exchanges between the thermal environment and the human body, namely its energy budget, physiology and clothing.	UTCI takes into consideration the clothing adaptation of the population in response to actual environmental temperature.	There are four variables required to calculate the UTCI: 2m air temperature, 2m dew point temperature (or relative humidity), wind speed at 10m above ground level and mean radiant temperature (MRT).		The universal thermal climate index (UTCI) is computed using data from the ECMWF ERA5 reanalysis.		The horizontal resolution of the data is 0.25°x0.25°.	There are 10 UTCI thermal stress categories that correspond to specific human physiological responses to the thermal environment. The categories relate to UTCI values as follows:	above +46: extreme heat stress; +38 to +46: very strong heat stress; +32 to +38: strong heat stress; +26 to +32: moderate heat stress; +9 to +26: no thermal stress; +9 to 0: slight cold stress; 0 to -13: moderate cold stress; -13 to -27: strong cold stress; -27 to -40: very strong cold stress; below -40: extreme cold stress.	The universal thermal climate index (UTCI) is presented	for each month and year from 1979 to 2020.			UTCI statistics	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical?tab=overview).
9	C3S_434_009	Agriculture			Heat and Cold	Mean Temperature	Biologically Effective Degree Days	Projected change in biologically effective degree days	The biologically effective degree days index is	(°C)	2011-2099	Time_30yr_2011_2099	Change_MonAnn	Scenario_26_85		The biologically effective degree days index provides information about the growing season.	The biologically effective degree days (°C) index is the sum of daily mean temperatures above 10°C and less than 30°C for a given period.		Projected changes are calculated relative to a (1981-2010) ERA5 reference period.				calculated from an ensemble of five global climate model (GCM) simulations	for two CMIP5 scenario projections: RCP2.6 (with low greenhouse gas emissions) and RCP8.5 (with high greenhouse gas emissions).	The horizontal resolution of the data is 0.5°x0.5°.	A positive value indicates higher biological effectiveness than the reference period while a negative value indicates lower biological effectiveness than the reference period.		Monthly and annual projected changes in the biologically effective degree days index are presented	as 30-year means	for two CMIP5 scenarios; RCP2.6 with low greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for biologically effective degree days	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators?tab=overview).
10	C3S_434_010	Agriculture			Heat and Cold	Cold Spell and Frost	Frost Days	Projected change in the number of frost days	The number of frost days index is	(days)	2011-2099	Time_30yr_2011_2099	Change_MonAnn	Scenario_26_85		The number of frost days provides information on frost damage.	A frost day is when the daily minimum temperature is less than 0°C.		Projected changes are calculated relative to a (1981-2010) ERA5 reference period.				calculated from an ensemble of five global climate model (GCM) simulations	for two CMIP5 scenario projections: RCP2.6 (with low greenhouse gas emissions) and RCP8.5 (with high greenhouse gas emissions).	The horizontal resolution of the data is 0.5°x0.5°.	A positive value indicates more frost days than the reference period while a negative value indicates fewer frost days than the reference period.		Monthly and annual projected changes in the number of frost days index are presented	as 30-year means	for two CMIP5 scenarios; RCP2.6 with low greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the number of frost days	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators?tab=overview).
11	C3S_434_011	Agriculture			Heat and Cold	Extreme Heat	Tropical Nights	Projected change in the number of tropical nights	The number of tropical nights index is	(days)	2011-2099	Time_30yr_2011_2099	Change_MonAnn	Scenario_26_85		The number of tropical nights provides an indication of the likely occurrence of various pests.	On a tropical night the minimum temperature remains above 20°C.		Projected changes are calculated relative to a (1981-2010) ERA5 reference period.				calculated from an ensemble of five global climate model (GCM) simulations	for two CMIP5 scenario projections: RCP2.6 (with low greenhouse gas emissions) and RCP8.5 (with high greenhouse gas emissions).	The horizontal resolution of the data is 0.5°x0.5°.	A positive value indicates more tropical nights than the reference period while a negative value indicates fewer tropical nights than the reference period.		Monthly and annual projected changes in the number of tropical nights index are presented	as 30-year means	for two CMIP5 scenarios; RCP2.6 with low greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the number of tropical nights	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators?tab=overview).
12	C3S_434_012	Agriculture			Wet and Dry	Mean Precipitation	Precipitation Sum	Projected change in precipitation sum	The precipitation sum index is	(%)	2011-2099	Scenario_26_85	Time_30yr_2011_2099	Change_Season		The precipitation sum provides information on possible water shortage or excess.	The precipitation sum index is expressed as a percentage change in total precpitation relative to a reference period.		Projected changes in the precipitation sum are calculated relative to a (1981–2010) ERA5 reference period.				calculated from an ensemble of five global climate model (GCM) simulations	for two CMIP5 scenario projections: RCP2.6 (with low greenhouse gas emissions) and RCP8.5 (with high greenhouse gas emissions).	The horizontal resolution of the data is 0.5°x0.5°.	A positive value indicates more precipitation than the reference period while a negative value indicates less precipitation than the reference period.		Monthly and annual projected changes in the precipitation sum index are presented	as 30-year means	for two CMIP5 scenarios; RCP2.6 with low greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the precipitation sum	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators?tab=overview).
13	C3S_434_013	Agriculture			Heat and Cold	Mean Temperature	Daily Maximum Temperature - Monthly Statistics	Projected change in monthly and seasonal statistics of daily maximum 2m air temperature	Monthly and seasonal statistics of daily maximum 2m surface air temperature are	(°C)	2011-2099	Time_30yr_2011_2099	Change_Mon	Scenario_26_85		The daily maximum temperature index provides information on long-term climate variability and change. Temperature plays a fundamental role in agricultural productivity, biodiversity and public health.	The daily maximum air temperature (°C) is representative of the temperature at a height of 2m above the surface.	This index provides values for the monthly, seasonal, and annual maxima, minima, and mean of daily maximum air temperature. e.g. the annual maximum of daily maximum temperature is the hotest day of the year.	Projected changes are calculated relative to a (1981–2010) ERA5 reference period.				calculated from an ensemble of five global climate model (GCM) simulations	for two CMIP5 scenario projections: RCP2.6 (with low greenhouse gas emissions) and RCP8.5 (with high greenhouse gas emissions).	The horizontal resolution of the data is 0.5°x0.5°.	A positive value indicates a warmer temperature than the reference period while a negative value indicates a cooler temperature than the reference period.		Monthly and seasonal projected changes of the daily maximum temperature index are presented	as 30-year means for each month or season	for two CMIP5 scenarios; RCP2.6 with low greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the mean value of daily maximum temperature, the maximum value of daily maximum temperature and the minimum value of daily maximum temperature	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators?tab=overview).
14	C3S_434_014	Agriculture			Heat and Cold	Mean Temperature	Daily Minimum Temperature - Monthly Statistics	Projected change in monthly and seasonal statistics of daily minimum 2m air temperature	Monthly and seasonal statistics of daily minimum 2m surface air temperature are	(°C)	2011-2099	Time_30yr_2011_2099	Change_Mon	Scenario_26_85		The daily minimum temperature index provides information on long-term climate variability and change. Temperature plays a fundamental role in agricultural productivity, biodiversity and public health.	The daily minimum air temperature (°C) is representative of the temperature at a height of 2m above the surface.	This index provides values for the monthly, seasonal, and annual maxima, minima, and mean of daily minimum air temperature. e.g. the annual maximum of daily minimum temperature is the warmest night of the year.	Projected changes are calculated relative to a (1981–2010) ERA5 reference period.				calculated from an ensemble of five global climate model (GCM) simulations	for two CMIP5 scenario projections: RCP2.6 (with low greenhouse gas emissions) and RCP8.5 (with high greenhouse gas emissions).	The horizontal resolution of the data is 0.5°x0.5°.	A positive value indicates a warmer temperature than the reference period while a negative value indicates a cooler temperature than the reference period.		Monthly and seasonal projected changes of the daily minimum temperature index are presented	as 30-year means for each month or season	for two CMIP5 scenarios; RCP2.6 with low greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the mean value of daily minimum temperature, the maximum value of daily minimum temperature and the minimum value of daily minimum temperature	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators?tab=overview).
15	C3S_434_015	Agriculture			Heat and Cold	Mean Temperature	Daily Mean Temperature - Monthly Statistics	Projected change in monthly and seasonal statistics of daily mean 2m surface air temperature	Monthly and seasonal statistics of daily mean 2m surface air temperature are	(°C)	2011-2099	Time_30yr_2011_2099	Change_Mon	Scenario_26_85		The daily mean temperature index provides information on long-term climate variability and change. Temperature plays a fundamental role in agricultural productivity, biodiversity and public health.	The daily mean air temperature (°C) is representative of the temperature at a height of 2m above the surface.	This index provides values for the monthly mean of daily mean air temperature. The seasonal and annual statistics for this index are averages of these monthly values.	Projected changes are calculated relative to a (1981–2010) ERA5 reference period.				calculated from an ensemble of five global climate model (GCM) simulations	for two CMIP5 scenario projections: RCP2.6 (with low greenhouse gas emissions) and RCP8.5 (with high greenhouse gas emissions).	The horizontal resolution of the data is 0.5°x0.5°.	A positive value indicates a warmer temperature than the reference period while a negative value indicates a cooler temperature than the reference period.		Monthly and seasonal projected changes of the daily mean temperature index are presented	as 30-year means for each month or season	for two CMIP5 scenarios; RCP2.6 with low greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for mean value of daily mean temperature	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators?tab=overview).
16	C3S_434_016	Health			Heat and Cold	Extreme Heat	Health Heatwave (High Temperature and Humidity)	Number of health-related heatwave days		(days)	1971-2099	Scenario_45_85				A health-related heatwave is a prolonged period of extremely high temperature and humidity for a particular region during which daily minima and maxima both exceed thresholds. Heatwaves with high humidity are problematic for human health and well-being.	The annual number of heatwave days is based on the heatwave definition used by the health community.		A health-related heatwave is considered to be a period of at least 2 consecutive days on which the maximum apparent temperature (Tappmax) exceeds the 90th percentile of Tappmax and the minimum temperature (Tmin) exceeds the 90th percentile of Tmin. Health heatwaves are calculated for each month of the summer period between June and August. The apparent temperature is a measure of relative discomfort due to combined heat and high humidity.		The data covers the period 1971 to 2099 and statistics are averaged for 30 years in overlapping time periods set 10 years apart.	Finally, the time series are averaged for the model ensemble.	The health-related heatwave statistics are calculated using bias-adjusted EURO-CORDEX data	for two CMIP5 scenarios with different possible future greenhouse gas emissions: RCP4.5 (medium emissions) and RCP8.5 (high emissions).	The horizontal resolution of the data is 0.1°x0.1°.			The annual number of heatwave days are presented	for the health-related definition of heatwaves	for two CMIP5 scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the number of health-related heatwave days	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-heat-and-cold-spells?tab=overview).
17	C3S_434_017	Agriculture	Energy		Heat and Cold	Extreme Heat	Climatological Heatwave (High Temperature)	Number of climatological heatwave days		(days)	1971-2099	Scenario_45_85				A climatological heatwave is a prolonged period of extremely high temperature for a particular region that exceeds a maximum threshold. Heatwaves have consequences for health, agriculture, power, wildfire and infrastructure.	The annual number of heatwave days is based on the climatological definition of a heatwave.		A climatological heatwave is considered to be a period of at least 3 consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September season for the control period of 1971 to 2000.		The data covers the period 1971 to 2099 and statistics are averaged for 30 years in overlapping time periods set 10 years apart.	Finally, the time series are averaged for the model ensemble.	The climatological heatwave statistics are calculated using bias-adjusted EURO-CORDEX data	for two CMIP5 scenarios with different possible future greenhouse gas emissions: RCP4.5 (medium emissions) and RCP8.5 (high emissions).	The horizontal resolution of the data is 0.1°x0.1°.			The annual number of heatwave days are presented	for the climatological definition of heatwaves	for two CMIP5 scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Statistics for the number of climatological heatwave days	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-heat-and-cold-spells?tab=overview).
18	C3S_434_018	Coastal			Coastal	Coastal Flooding	Annual Highest High Water	Projected change in the annual highest high water	The annual highest high water index	(m)	2070-2100	2040-2100_rcp85-45				The annual highest high water index informs an understanding of European coastal hydrodynamics under the impact of climate change. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management with particular relvance to coastal flooding.	The projected change in annual highest high water (m)		is calculated relative to the mean of annual highest high water levels during the historical period from 1977-2005. Tidal dynamics, sea level change data from climate models, and large-scale long-term changes to land height due to post-glacial rebound are taken into account in the calculation of this index. Storm surges caused by atmospheric forcing are not taken into account for this index.	The index is generated using the Global Tide and Surge Model (GTSM, Deltares) and the Wave Model (WAM, ECMWF). High resolution forcing fields for the GTSM are provided by the Danish Meteorological Institute's regional model (HIRHAM5) which is downscaled from the EC-EARTH global climate model.	The EC-EARTH simulations used input from a relative Sea Surface Height (SSH) dataset that takes into account geophysical drivers of long-term SSH change, such as changes to the Greenland and Antarctic ice sheets, thermal expansion of the ocean, and glacial isostatic adjustment.	Given that the projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take into consideration that there is an inevitable underestimation of the uncertainty associated with this dataset.	The data covers the period from 2070 to 2100 based on CMIP5 scenario RCP4.5 with medium greenhouse gas emissions.	The data covers the period from 1977 to 2100. It consists of a historical period, 1977-2005, based on the ERA5 reanalysis and two future periods: 2040-2070 based on CMIP5 scenario RCP8.5 with high greenhouse gas emissions, and 2070-2100 based on CMIP5 scenario RCP4.5 with medium greenhouse gas emissions.	The resolution of the Global Tide and Surge Model is finest near the coast. On the coast itself the data points have a 10 km spacing with additional data points at the location of tide guages. Within 100km of the coast the model resolution is 0.25°, within 500km the resolution is 0.5°, and beyond 500km the resolution is 1.0°.	A positive value indicates a higher sea level than the reference period while a negative value indicates a lower sea level than the reference period.		The projected change in the annual highest high water is presented	for the future period 2070-2100 based on CMIP5 scenario RCP4.5 with medium greenhouse gas emissions.	for two future periods: 2040-2070 based on CMIP5 scenario RCP8.5 and 2070-2100 based on CMIP5 scenario RCP4.5.		Regional statistics for the projected change in annual highest high water	are also available via the "Explore in Detail" button	.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-indicators?tab=overview).
19	C3S_434_019	Coastal			Coastal	Relative Sea Level	Mean Relative Sea Level	Projected change in mean relative sea level	The mean relative sea level index	(m)	2070-2100	2040-2100_rcp85-45				The mean relative sea level index informs an understanding of European coastal hydrodynamics under the impact of climate change. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management with particular relvance to coastal management.	The projected change in mean relative sea level (m)		is calculated relative to the mean of the mean sea level during the historical period from 1977-2005. Tidal dynamics, sea level change data from climate models, and large-scale long-term changes to land height due to post-glacial rebound are taken into account in the calculation of this index. Storm surges caused by atmospheric forcing are not taken into account for this index.	The index is generated using the Global Tide and Surge Model (GTSM, Deltares) and the Wave Model (WAM, ECMWF). High resolution forcing fields for the GTSM are provided by the Danish Meteorological Institute's regional model (HIRHAM5) which is downscaled from the EC-EARTH global climate model.	The EC-EARTH simulations used input from a relative Sea Surface Height (SSH) dataset that takes into account geophysical drivers of long-term SSH change, such as changes to the Greenland and Antarctic ice sheets, thermal expansion of the ocean, and glacial isostatic adjustment.	Given that the projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take into consideration that there is an inevitable underestimation of the uncertainty associated with this dataset.	The data covers the period from 2070 to 2100 based on CMIP5 scenario RCP4.5 with medium greenhouse gas emissions.	The data covers the period from 1977 to 2100. It consists of a historical period, 1977-2005, based on the ERA5 reanalysis and two future periods: 2040-2070 based on CMIP5 scenario RCP8.5 with high greenhouse gas emissions, and 2070-2100 based on CMIP5 scenario RCP4.5 with medium greenhouse gas emissions.	The resolution of the Global Tide and Surge Model is finest near the coast. On the coast itself the data points have a 10 km spacing with additional data points at the location of tide guages. Within 100km of the coast the model resolution is 0.25°, within 500km the resolution is 0.5°, and beyond 500km the resolution is 1.0°.	A positive value indicates a higher sea level than the reference period while a negative value indicates a lower sea level than the reference period.		The projected change in mean relative sea level is presented	for the future period 2070-2100 based on CMIP5 scenario RCP4.5 with medium greenhouse gas emissions.	for two future periods: 2040-2070 based on CMIP5 scenario RCP8.5 and 2070-2100 based on CMIP5 scenario RCP4.5.		Regional statistics for the projected change in mean relative sea level	are also available via the "Explore in Detail" button	.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-indicators?tab=overview).
20	C3S_434_020	Forestry	Tourism	Health	Wet and Dry	Wildfire	Fire Weather Index - Days With High Fire Danger	Projected change in the number of days with high fire danger		(days)	2011-2099	Time_30yr_2011_2099	Scenario_26_45_85			The Canadian Fire Weather Index System (FWI) is used to assess fire danger in a harmonized way across Europe. The FWI uses information about fuel moisture and weather conditions to determine fire behaviour.	The calculation of the FWI is based on	24-hour accumulated precipitation and daily noon values of air temperature, relative humidity, and wind speed.	The projected change in the number of days with high fire danger is calculated relative to the 1986-2005 period.	The FWI is generated using the Global ECMWF Fire Forecasting model (GEFF).	The incidence of high fire danger is expressed as the number of days per year with a FWI greater than 30.	The projected changes to fire danger under future climate conditions are calculated relative to the 1986-2005 period.	Projections from multiple global climate models downscaled to a regional climate model were used to generate the meteorological input for the GEFF model.	The GEFF model was run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios consistent with an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century (RCP8.5).	The data is on a regular latitude-longitude grid with a horizontal resolution of 0.11° x 0.11°.	The FWI can be understood in terms of six danger classes based on the [European Forest fire Information System (EFFIS)](https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/) classification.	Very low danger: FWI is less than 5.2. Low danger: FWI is between 5.2 and 11.2. Moderate danger: FWI is between 11.2 and 21.3. High danger: FWI is between 21.3 and 38.0. Very high danger: FWI is between 38.0 and 50. Extreme danger: FWI is greater than 50.	The projected change in the number of days per year with high fire danger (FWI greater than 30) is presented	for three CMIP5 scenarios: RCP2.6 with low greenhouse gas emissions, RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.			Statistics for the number of days with high fire danger	are also available via the "Explore in Detail" button	as national, sub-national and trans-national area-means for which time series data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-fire-danger-indicators?tab=overview).
21	C3S_434_021	Tourism			Snow and Ice	Snow and Land Ice	Total Winter Snow	Projected change in total snow precipitation from November to April	Cumulative snowfall over the winter sports season (November to April).	(kg m-2)	2011-2099	Time_30yr_2011_2099	Scenario_45_85	Elevation		Snow indices for Europe can be used to characterize the operating conditions of winter ski resorts under future climate scenarios. They are used in the assessment of snow reliability for mountainous regions across Europe.	Total snow precipitation is the cumulative value of snowfall over the winter sports season (November year N-1 to April year N).		The projected change in cumulative snowfall for each region and elevation is calculated relative to the 1981-2010 period of the [UERRA](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-complete?tab=overview) historical reanalysis.	Snow indices are generated using the Crocus snowpack model, a multi-layer snowpack model embedded in the SURFEX land surface model.	The SURFEX (Surface Externalisée) model is forced with atmospheric fields provided by adjusted EURO-CORDEX ensemble climate projections.	Multi-model statistics (including the mean, maximum and minimum) are derived from eight global climate model (GCM) and regional climate model (RCM) pairings.	The data covers the period from 2011-2099. It consists of two climate change scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Data is provided at the scale of NUTS-3 regions ([Nomenclature of Territorial Units for Statistics](https://ec.europa.eu/eurostat/web/nuts/background)) and by steps of 100 m elevation.			The projected change in cumulative snowfall from November to April is provided for [NUTS-3](https://ec.europa.eu/eurostat/web/nuts/background) sub-national regions,	as 30-year means	for two CMIP5 scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.	The snow index is further discretised in steps of 100m land-surface elevation.	Statistics for cumulative snowfall	are also available via the "Explore in Detail" button	where timeseries data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-snow-indicators?tab=overview)
22	C3S_434_022	Tourism			Snow and Ice	Snow and Land Ice	Days with a High Amount of Natural Snow	Projected change in the period with a high amount of natural snow	The number of days, from August to July, with a high amount of natural snow	(days)	2011-2099	Time_30yr_2011_2099	Scenario_45_85	Elevation		Snow indices for Europe can be used to characterize the operating conditions of winter ski resorts under future climate scenarios. They are used in the assessment of snow reliability for mountainous regions across Europe.	The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a natural snow simulation.		The projected change in the number of days with a high amount of natural snow for each region and elevation is calculated relative to the 1981-2010 period of the [UERRA](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-complete?tab=overview) historical reanalysis.	Snow indices are generated using the Crocus snowpack model, a multi-layer snowpack model embedded in the SURFEX land surface model.	The SURFEX (Surface Externalisée) model is forced with atmospheric fields provided by adjusted EURO-CORDEX ensemble climate projections.	Multi-model statistics (including the mean, maximum and minimum) are derived from eight global climate model (GCM) and regional climate model (RCM) pairings.	The data covers the period from 2011-2099. It consists of two climate change scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.		Data is provided at the scale of NUTS-3 regions ([Nomenclature of Territorial Units for Statistics](https://ec.europa.eu/eurostat/web/nuts/background)) and by steps of 100 m elevation.			The projected change in the number of days with a high amount of natural snow from August to July are provided for [NUTS-3](https://ec.europa.eu/eurostat/web/nuts/background) sub-national regions,	as 30-year means	for two CMIP5 scenarios: RCP4.5 with medium greenhouse gas emissions and RCP8.5 with high greenhouse gas emissions.	The snow index is further discretised in steps of 100m land-surface elevation.	Statistics for the number of days with a high amount of natural snow	are also available via the "Explore in Detail" button	where timeseries data can be plotted.	The data was collated on behalf of the Copernicus Climate Change Service (C3S).	Further information about this index can be found in the C3S documentation resources in the	[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-snow-indicators?tab=overview)
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