Climate change, impacts and vulnerability in Europe 2012

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EEA Report No 12/2012
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This European Environment Agency (EEA) report presents information on past and projected climate change and related impacts in Europe, based on a range of indicators. The report also assesses the vulnerability of society, human health and ecosystems in Europe and identifies those regions in Europe most at risk from climate change. Furthermore, the report discusses the principle sources of uncertainty for the indicators and notes how monitoring and scenario development can improve our understanding of climate change, its impacts and related vulnerabilities.

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Trend in relative sea level at selected European tide gauge stations Trend in relative sea level at selected European tide gauge stations The map shows the trend in relative sea level at selected European tide gauge stations since 1970. These measured trends are not corrected for local land movement. No attempt has been made to assess the validity of any individual fit, so results should not be treated as suitable for use in planning or policymaking. Geographical coverage reflects the reporting of tide gauge measurements to the Permanent Service for Mean Sea Level (PSMSL).
Projected change in annual and summer precipitation Projected change in annual and summer precipitation Projected changes in annual (left) and summer (right) precipitation (%) in the period 2071-2100 compared to the baseline period 1971-2000 for the forcing scenario RCP 8.5. Model simulations are based on the multi-model ensemble average of RCM simulations from the EURO-CORDEX initiative.
European average air temperature anomalies (1850 to 2012) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods European average air temperature anomalies (1850 to 2012) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods European average air temperature anomalies (1850 to 2012) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods relative to pre-industrial baseline period. 1) Black line - HadCRUT4 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Morice et al. 2012) with the grey area representing the 95% confidence range, 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010).
Projections for combined changes in temperature and precipitation Projections for combined changes in temperature and precipitation Annual changes in temperature and precipitation in northern (top panels) and southern Europe (bottom panels) for the periods 2030–2049 (left panels) and 2080–2099 (right panels) relative to 1961–1990. GCM simulations for the SRES A1B scenario, which assumes rather high population and economic growth and a balanced use of energy sources, are shown with green points. Comparable simulations for the ENSEMBLES E1 mitigation scenario are shown with orange points.
Key observed and projected climate change and impacts for the main regions in Europe Key observed and projected climate change and impacts for the main regions in Europe The map shows the observed and projected climate change and impacts for the main biogeographical regions in Europe
Projected distribution of economic costs from climate change and socio-economic developments by impact type and European region Projected distribution of economic costs from climate change and socio-economic developments by impact type and European region Projected distribution of economic costs from climate change and socio-economic developments by impact type and European region. EU-27 only; A1B scenario, 2070–2100, combined effects of climate change and socio-economic change
Projections of economic costs from climate change and socio-economic developments for four major categories Projections of economic costs from climate change and socio-economic developments for four major categories Projections of economic costs from climate change and socio-economic developments for four major categories for two different socio economic scenarios and three different future periods. Left: damage costs for the A1B scenario for energy for cooling, heat-related mortality (weighted average of Value of a Statistical Life (VSL) and Value of a Life Year Lost (VOLY)), river floods and coastal zones. Time horizon: 2010–2040, 2040–2070 and 2070–2100. Right: A1B and E1 scenarios, 2070–2100.
Potential aggregate impact, adaptive capacity and vulnerability Potential aggregate impact, adaptive capacity and vulnerability Overall impacts derived from 26 impact indicators, overall adaptive capacity from 15 individual indicators, and overall vulnerability from a combination of overall impacts and adaptive capacity.
Annual water stress for present conditions and projections for two scenarios Annual water stress for present conditions and projections for two scenarios Left: present climate; middle: projection for 2050 based on Economy First scenario, median of general circulation models — regional climate models (GCM-RCM) combinations; right: projection for 2050 based on Sustainability Eventually scenario, median of GCM-RCM combinations. Yellow: low water stress (withdrawals-to-availability ratio: 0–0.2); orange: mild water stress (withdrawals-to-availability ratio: 0.2–0.4); red: severe water stress (withdrawals-to-availability ratio: > 0.4).
Potential physical, cultural and social impact of climate change Potential physical, cultural and social impact of climate change (Physical) Combined potential impacts of changes in inundation heights of a 100 year river flood event and a sea level rise adjusted 100 year coastal storm surge event and changes in flash flood potential on settlements, major roads, railways, airports, harbours, thermal power stations and refineries. (Social) Combined potential impacts of changes in inundation heights of a 100 year river flood event and a sea level rise adjusted 100 year coastal storm surge event as well as changes in flash flood potential and summer heat on population. (Cultural) Combined potential impacts of changes in inundation heights of a 100 year river flood event and a sea level rise adjusted 100 year coastal storm surge event on registered World Heritage sites and museums.
Estimated number of people and gross value affected by 100-year flood events in the ‘Economy First’ scenario for the 2050s Estimated number of people and gross value affected by 100-year flood events in the ‘Economy First’ scenario for the 2050s Number of people (a) and amount of manufacturing gross value added (GVA), (b) affected by 100-year flood events in the 'Economy First' scenario for the 2050s. Calculations based on median ensemble results from LISFLOOD linked to population projections from SCENES scenarios.
Projected changes in the tourism climatic index for all seasons Projected changes in the tourism climatic index for all seasons Tourism Climatic Index (TCI) for four seasons in the present period (1961–1990, left), under future climate change (2071–2100, middle), and change between present and future period (left). Future climate conditions are based on the SRES A2 scenario and derived from the ensemble mean of five regional climate models (RCMs) that participated in the PRUDENCE project.
Number of reported extreme weather events and wildfire Number of reported extreme weather events and wildfire This figure shows the yearly number of extreme weather events (cold, storm, flood and wet mass movement, heat wave, wildfire, drought and drymass movement dry) in EEA member and collaborating countries in the period 1980 - 2011.
Observed change in the distribution of demersal fish in response to observed rise in sea surface temperatures Observed change in the distribution of demersal fish in response to observed rise in sea surface temperatures Changes in abundance in response to observed temperature change are relative changes (unitless).
Burnt forest area in five southern European countries Burnt forest area in five southern European countries Burnt forest area (in ha) in five southern European countries from 1980 to 2010.
Projected changes in annual and summer precipitation Projected changes in annual and summer precipitation Projected changes in annual (left) and summer (right) precipitation (%) between 1961-1990 and 2071-2100 as simulated by ENSEMBLES Regional Climate Models for the IPCC SRES A1B emission scenario.
European summer temperatures for 1500-2010 European summer temperatures for 1500-2010 Statistical frequency of the european temperature
Actual and potential future alien plant invasion hotspots under two emissions scenarios Actual and potential future alien plant invasion hotspots under two emissions scenarios The map shows the potential future alien plant invasion hotspots in Austria and Germany under climate change, based on 30 invasive alien vascular plant species and the SRES A2 and B2 emissions scenarios. Colours mark number of invasive alien species suitable in an area.
Probability of complete loss of northern Fennoscandian areas suitable for palsas Probability of complete loss of northern Fennoscandian areas suitable for palsas The figure shows the probability of complete loss of northern Fennoscandian areas suitable for palsas during the 21st century estimated using a probabilistic projection of climate change for the SRES A1B scenario
Projected changes in annual, summer and winter temperature 2021-2050 (top) and 2071-2100 (bottom) Projected changes in annual, summer and winter temperature 2021-2050 (top) and 2071-2100 (bottom) Projected changes in annual near-surface air temperature (°C) using multi-model ensemble average of RCM simulations for the period 2021-2050 (left) and 2071-2100 (right). Model simulations of the EU-ENSEMBLES project using the IPCC SRES A1B emission scenario for the periods 1961-1990, 2021-2050 and 2071-2100 (van der Linden and Mitchell, 2009).
Trends in warm days and cool nights across Europe Trends in warm days and cool nights across Europe Warm days are defined as being above the 90th percentile of the daily maximum temperature and cool nights as below the 10th percentile of the daily minimum temperature (Alexander et al., 2006). Grid boxes outlined in solid black contain at least three stations and so are likely to be more representative of the grid-box. High confidence in the long-term trend is shown by a black dot. (In the maps above, this is the case for all grid boxes.) Area averaged annual time series of percentage changes and trend lines are shown below each map for one area in northern Europe (green line, 5.6 ° to 16.9 °E and 56.2 ° to 66.2 °N) and one in south-western Europe (purple line, 350.6 ° to 1.9 °E and 36.2 ° to 43.7 °N).
Change in global average temperature from three sources (1850–2011) Change in global average temperature from three sources (1850–2011) Left figure: Global average air temperature anomalies (1850 to 2011) in degrees Celsius (°C) relative to a pre-industrial baseline period for 3 analyses of observations: 1) Black line - HadCRUT3 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Brohan et al., 2006) with the grey area representing the 95% confidence range, 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010). Upper graph shows annual anomalies and lower graph shows decadal average anomalies for the same datasets. Right figure: Rates of change of global average temperature (1850 to 2011) in ºC per decade, based on 10-year running average of the 3 datasets: 1) Black line - HadCRUT3 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Brohan et al., 2006), 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010).
Change of flowering date for winter wheat Change of flowering date for winter wheat This figure shows the rate of change of the flowering date for winter wheat. The flowering date is defined as the day at which a modelization of the winter wheat reaches a development state of 100 in a scale 0 - 200 defined for the WOFOST growth model (Van Keulen H, Wolf J (1986) Modelling of agricultural production: weather soils and crops, Simulation monographs. Pudoc, Wageningen). The map shows the yearly change rate in days per year calculated for the period January 1975 - December 2010.
Annual mean ozone concentrations by station type Annual mean ozone concentrations by station type Annual mean ozone concentrations by station type
Estimated soil erosion by water in Europe Estimated soil erosion by water in Europe Calculated by the Revised Universal Soil Loss Equation (RUSLE). While the overall patterns of erosion are generally sound, the validation of erosion data can be challenging. The data presented are currently being validated through comparisons with national datasets and expert judgement. In this sense, zooming in on a specific locality can give the impression of a situation that differs from reality. In addition, the model used in this exercise does not consider localised intense precipitation.
Estimated number of days for wind erosion Estimated number of days for wind erosion Calculations are based on wind velocity and soil texture. While the overall patterns of erosion are generally sound, the validation of erosion data can be challenging. The data presented are currently being validated through comparisons with national datasets and expert judgement. In this sense, zooming in on a specific locality can give the impression of a situation that differs from reality.
Trend in heating degree days in the EU-27 Trend in heating degree days in the EU-27 Trend in heating degree days in the EU-27 (1980 - 2009). Eurostat calculates heating degree days as (18 °C - Tmean) if Tmean is lower than 15 °C (heating threshold) and zero if Tmean is greater than or equal 15 °C; Tmean is the mean daily outdoor temperature, calculated as Tmean = (Tmin + Tmax / 2)
Projected changes in fire danger Projected changes in fire danger Fire danger is expressed by the Seasonal Severity Rating (SSR). Based on projections by the Regional Climate Model (RCM) RACMO2 driven by the Global Climate Model (GCM) ECHAM5 for the SRES A1B emission scenario. Left: projected change in SSR by 2071–2100 as compared to 1961–1990 baseline period; Right: projected annual average SSR in 2071–2100.
State and trend of fire danger State and trend of fire danger Fire danger is expressed by the Seasonal Severity Rating (SSR). Daily severity values can be averaged over the fire season using the SSR index, which allows objective comparison of fire danger from year to year and from region to region. The coarse scale of the map does not allow accounting for specific conditions of given sites, as for example in the Alpine region, where the complex topography may strongly affect local fire danger.
Rate of change of the meteorological water balance Rate of change of the meteorological water balance This figure shows the rate of change of the ‘water balance’. The map provides an estimate increase (red in map) or decrease (blue in map) of the volume of water required from irrigation assuming that all other factors are unchanged and given that there is an irrigation demand.
Simulated change in water-limited wheat production Simulated change in water-limited wheat production The figure shows the simulated change in water-limited wheat production for 2030 compared with 2000 for the A1B emission scenario using a cold (ECHAM5) (left) and a warm (HADCM3) (right) climate change projection. The simulation was performed on a 25x25 km grid (assuming current area of wheat cropping) but the results are presented here at the NUTS-2 level.
Projected changes in water-limited crop yield Projected changes in water-limited crop yield This map provides an aggregated picture of expected changes in crop yields across Europe for the 2050s (compared with 1961–1990). The simulations by the ClimateCrop model are based on an ensemble of 12 GCMs under the A1B emission scenario. They include effects of changes in temperature, precipitation and CO2 concentration on crop yields of three main crops assuming current irrigated area.
Global surface soil moisture content based on remote sensing data Global surface soil moisture content based on remote sensing data SMOS provides a global image of surface soil moisture every three days; this map covers the period 8–15 June 2010. Yellow colours indicate drier soil surfaces; blue colours denote wetter conditions. SMOS can measure soil moisture levels to an accuracy of 4 % at a spatial resolution of 50 km — about the same as detecting a teaspoonful of water mixed into a handful of dry soil.
Annual average sea surface temperature anomaly in different European seas Annual average sea surface temperature anomaly in different European seas Time series of annual average sea surface temperature (°C), referenced to the average temperature between 1986 and 2010, in each of the European seas. Data sources: SST datasets from the Hadley Centre (HADISST1 (global)), MOON-ENEA (Mediterranean Sea), and Bundesamt für Seeschifffahrt und Hydrographie (Baltic and North Seas), and MyOcean.
Calanus ratio in the North Sea Calanus ratio in the North Sea Continuous Plankton Recorder data. Left: Temporal and seasonal distribution of the Calanus ratio (1958–2009). Right: Change in Calanus ratio in the North Sea between (1958 and 2009).
Mean annual sea surface temperature trend in European seas Mean annual sea surface temperature trend in European seas Spatial distribution of sea surface temperature trend over the past 25 years (1987-2011) for the European seas as calculated from the HADISST1 dataset. The units are °C/yr. Source: HADSST1 dataset (http://hadobs.metoffice.com/hadisst/data/download.html), masked where ice coverage constituted more than 20% of the sea water.
Observed latitudinal shifts of four species over 25 years in Britain Observed latitudinal shifts of four species over 25 years in Britain This graph shows the observed latitudinal shifts of the northern range boundaries of species within 4 exemplar taxonomic groups, studied over 25 years in Britain. (A) Spiders (85 species), (B) ground beetles (59 species), (C) butterflies (29 species), and (D) grasshoppers and allies (22 species). Positive latitudinal shifts indicate movement toward the north (pole); negative values indicate shifts toward the south (Equator). Horizontal lines mark the Median, boxes the 25 to 75 % quartile and whisker the range (up to 1.5 times the interquartile distance). Open Circles are outliers.
European variations in the temporal trend of bird and butterfly community temperature index European variations in the temporal trend of bird and butterfly community temperature index The map shows the temporal trend of bird and butterfly CTI for each country. A temporal increase in CTI directly reflects that the species assemblage of the site is increasingly composed of individuals belonging to species dependent on higher temperature. The height of a given arrow is proportional to the temporal trend and its direction corresponds to the sign of the slope (from south to north for positive slopes). The arrow is opaque if the trend is significant.
Trends in monthly stream flow for the period 1962-2004 Trends in monthly stream flow for the period 1962-2004 Trend given as standard deviations per year for monthly stream flow. Red colours mark decreases in stream flow whereas blue colours mark increases in stream flow.
Projected change in average annual and seasonal river flow Projected change in average annual and seasonal river flow Projected change in mean annual and seasonal river flow between the climate change scenario (SRES A1B, 2071-2100) and the control period (1961-1990). Simulations with LISFLOOD based on an ensemble of 11 RCMs.
Expected average percentage of stable area of 856 plant species for two different climate scenarios Expected average percentage of stable area of 856 plant species for two different climate scenarios The figure shows the expected average percentage of stable area of 856 plant species for two different climate scenarios by 2100. The S550e scenario corresponds to a stabilisation at 550 ppm CO2 equivalent and a global mean temperature increase of 2°C, the baseline scenario corresponds to a global mean temperature increase of more than 3°C.
Trends in spring phenology in Europe Trends in spring phenology in Europe This figure shows the spring trends of phenology 1971-2000 grouped by their mean onset date. Each dot represents a station. Dot size adjusted for clarity. A negative phenological trend corresponds to an earlier onset of spring.
Projected change in river floods with a return period of 100 years Projected change in river floods with a return period of 100 years Projected change in the level of a 100-year maximum level of river discharge between the reference period 1961–1990 and the 2020s (left), 2050s (centre) and 2050s (right) based on an ensemble of 12 RCM simulations with LISFLOOD for the SRES A1B scenario.
Projected change in minimum river flow with return period of 20 years Projected change in minimum river flow with return period of 20 years Relative change in minimum river flow for a) 2020s, b) 2050s and c) 2080s compared to 1961-1990 for SRES A1B scenario.
Trend in relative sea level at selected European tide gauge stations Trend in relative sea level at selected European tide gauge stations The map shows investigation of global mean sea level trends since 1970. These measured trends are not corrected for local land movement. No attempt has been made to assess the validity of any individual fit, so results should not be treated as suitable for use in planning or policymaking
Trend in absolute sea level across Europe based on satellite measurements (1992–2011) Trend in absolute sea level across Europe based on satellite measurements (1992–2011) Based on satellite data; trends in mm/year, inverted barometer included, seasonal signal removed
Range of high-end estimates of global sea-level rise published after the IPCC AR4 Range of high-end estimates of global sea-level rise published after the IPCC AR4 This figure shows the range of high-end global sea-level rise (metre per century) estimates published after the IPCC Fourth Assessment Report (AR4). AR4 results are shown for comparison in the three left-most columns.
Maximum ice cover extent in the Baltic Sea Maximum ice cover extent in the Baltic Sea The figure shows the maximum extent of ice cover in the Baltic Sea in the winters 1719/20–2010/11 (blue bars) and 15-year moving average (red line).
Observed permafrost temperatures from selected boreholes in European mountains Observed permafrost temperatures from selected boreholes in European mountains The figure shows observed permafrost temperatures from 10 m (left) and 20 m (right) depth and their evolution for selected boreholes in European mountains: the sites of the PACE transect and two additional sites in Switzerland (Matterhorn and M.d. Barba Peider) and one in Norway (Dovrefjell).
Comparison of active layer thickness from boreholes in the Alps, Norway and Svalbard Comparison of active layer thickness from boreholes in the Alps, Norway and Svalbard The figure shows the comparison of the active layer thickness from boreholes in the Alps, Norway and Svalbard.
Projected changes in the volume of all mountain glaciers and ice caps in the European glaciated regions Projected changes in the volume of all mountain glaciers and ice caps in the European glaciated regions This figure shows the projected volume (in cubic km) for 2001–2100 of all mountain glaciers and ice caps in the European glaciated regions derived using a mass balance model driven with temperature and precipitation scenarios from 10 GCMs. European Alps (top left), Scandinavia (top right), Iceland (bottom left), Svalbard (bottom right).
Cumulative specific net mass balance of European glaciers Cumulative specific net mass balance of European glaciers The figure shows the cumulative specific net mass balance (mm water equivalent) of European glaciers 1946–2010.
Projected changes in annual snowfall days Projected changes in annual snowfall days The figure shows the multi-model mean of changes in annual snowfall days from 1971-2000 to 2041-2070 exceeding (left) 1 cm and (right) 10 cm based on six RCM simulations and the emission scenario A1B
Trend in autumn, winter and spring snow cover extent over the Northern Hemisphere Trend in autumn, winter and spring snow cover extent over the Northern Hemisphere The figure shows the mean autumn (September, October, November), winter (December, January, February) and spring (March, April, May) snow cover extent over the Northern Hemisphere in 1967–2011 with linear trends.
Trends in the extreme wind speeds in the period 1871-2008 based on reanalysis Trends in the extreme wind speeds in the period 1871-2008 based on reanalysis Trends in the annual 95th percentile of daily maximum wind speeds in the 20th century reanalysis data set (ensemble mean) during the period 1871–2008. The trend is given in the units of the interannual standard deviation and plotted only when significant. The coloured circles indicate trends in the number of 'gale days' (an index that represents the number of extremely windy days) over the period at the specific locations.
Projected changes in extreme wind speed based on GCM and RCM ensembles Projected changes in extreme wind speed based on GCM and RCM ensembles Ensemble mean of changes in extreme wind speed (defined as the 98th percentile of daily maximum wind speed) for A1B (2071–2100) relative to 1961–2000. Left: based on 9 GCMs. Right: based on 11 RCMs. Coloured areas indicate the magnitude of change (unit: m/s), statistical significance above 0.95 is shown by black dots.
Mass balance of the Greenland ice sheet from mass budget calculations Mass balance of the Greenland ice sheet from mass budget calculations The figure shows the mass balance of the Greenland ice sheet from mass budget calculations.
Trend in yearly cumulated melting area of the Greenland ice sheet Trend in yearly cumulated melting area of the Greenland ice sheet The figure shows the change in yearly cumulated area of the Greenland ice sheet and it's melt during the period 1979 to 2011 in percentage relative to area in 1979=100. The linear trend 1979–2011 is included.
Global average surface temperature anomalies from in-situ and from reanalysis Global average surface temperature anomalies from in-situ and from reanalysis Global average air temperature anomalies from thress sources (HadCRU, GISS, NCDC, NCEP, ERA-40)
Projected changes in annual and summer precipitation between 1961–1990 and 2071–2100 Projected changes in annual and summer precipitation between 1961–1990 and 2071–2100 Projected changes in annual (left) and summer (right) precipitation (%) between 1961-1990 and 2071-2100 as simulated by ENSEMBLES Regional Climate Models for the IPCC SRES A1B emission scenario.
European average air temperature anomalies (1850 to 2011) in °C over land areas only European average air temperature anomalies (1850 to 2011) in °C over land areas only The sources of the original data: 1) Black line - HadCRUT3 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Brohan et al., 2006) with the grey area representing the 95% confidence range, 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010). Upper graph shows anomalies and lower graph shows decadal average anomalies for the same datasets. Europe is defined as the area between 35° to 70° North and -25° to 30° East, plus Turkey (35° to 40° North and 30° to 45° East).
Rate of change of global average temperature, 1850-2011 (in ºC per decade) Rate of change of global average temperature, 1850-2011 (in ºC per decade) Rates of change of global average temperature (1850 to 2011) in ºC per decade, based on 10-year running average of the 3 datasets: 1) Black line - HadCRUT3 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Brohan et al., 2006), 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010).
Trends in cool nights across Europe Trends in cool nights across Europe How to read the map: Cool nights are defined as being below the 10th percentile of the daily minimum temperature. Grid boxes outlined in solid black contain at least 3 stations and so are likely to be more representative of the grid-box. Higher confidence in the long-term trend is shown by a black dot. Area averaged annual time series of percentage changes and trend lines are shown below each map for one area in northern Europe (Green line, 5.6 to 16.9 E and 56.2 to 66.2 N) and one in south-western Europe (Pink line, 350.6 to 1.9 E and 36.2 to 43.7 N).
Projections of extreme temperatures as represented by the combined number of hot summer (June-August) days (TMAX>35°C) and tropical nights (TMIN>20°C) Projections of extreme temperatures as represented by the combined number of hot summer (June-August) days (TMAX>35°C) and tropical nights (TMIN>20°C) Maps show changes in extreme temperature for two future periods, relative to 1961-1990. Extreme temperatures are represented by the combined number of hot summer (June-August) days (TMAX>35°C) and tropical nights (TMIN>20°C). All projections are the average of 5 Regional Climate Model simulations of the EU-ENSEMBLES project using the IPCC SRES A1B emission scenario for the periods 1961-90, 2021-2050 and 2071-2100 (Fischer and Schär, 2010).
European average air temperature anomalies (1850 to 2011) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods European average air temperature anomalies (1850 to 2011) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods European average air temperature anomalies (1850 to 2011) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods relative to pre-industrial baseline period. 1) Black line - HadCRUT3 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Brohan et al., 2006) with the grey area representing the 95% confidence range, 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010).
Trends in warm days across Europe Trends in warm days across Europe How to read the map: Warm days are defined as being above the 90th percentile of the daily maximum temperature. Grid boxes outlined in solid black contain at least 3 stations and so are likely to be more representative of the grid-box. Higher confidence in the long-term trend is shown by a black dot. Area averaged annual time series of percentage changes and trend lines are shown below each map for one area in northern Europe (Green line, 5.6 to 16.9 E and 56.2 to 66.2 N) and one in south-western Europe (Pink line, 350.6 to 1.9 E and 36.2 to 43.7 N).
Urban flooding — impervious surfaces reduce the drainage of rain water and increase the risk for urban flooding Urban flooding — impervious surfaces reduce the drainage of rain water and increase the risk for urban flooding The map shows the average soil sealing degree inside the UMZ of European core cities (core city defined in Urban Atlas / Urban Audit). Soil sealing degrees are represented in coloured dots. The city dots are overlaid onto a modelled map displaying the change in annual number of days with heavy rainfall between the reference periods 1961-1990 and 2071-2100.
Vulnerable people — the elderly are considered to be a group more sensitive to various climatic stress factors than people of a working age Vulnerable people — the elderly are considered to be a group more sensitive to various climatic stress factors than people of a working age The map shows the share of elderly people (>= 65 years) within a city (represented by the dot colours) to the total population (represented by dot size).
Heat waves — both a low share of green and blue urban areas and high population densities can contribute to the urban heat island effect in cities Heat waves — both a low share of green and blue urban areas and high population densities can contribute to the urban heat island effect in cities The cities are displayed as dots of different colours and sizes. The colours represent the share of green and blue urban areas inside the Urban Morphological Zone of the city, the size of the dots reflects the population density within the core cities’ UMZ. The background map is the result of climatic modelling and represents the number of combined tropical nights (T>20°C) and hot days (T>35°C) for the period 2071 to 2100.
Past and projected global surface temperature change (relative to 1980–1999), based on multi‑model averages for selected IPCC scenarios Past and projected global surface temperature change (relative to 1980–1999), based on multi‑model averages for selected IPCC scenarios The figure shows the past and projected change in global surface temperatures. If global greenhouse gas emissions would not be reduced, the 2°C target will be exceeded towards the middle of the 21st century. The horizontal 2°C target line takes into account warming of about 0.6 °C from pre-industrial to 1990. “Likely” ranges in average 2090-2099 warming for all six IPCC scenarios are shown on the right
Flood plains of the river Elbe in the municipality of Dresden, Germany, during different flooding events Flood plains of the river Elbe in the municipality of Dresden, Germany, during different flooding events The figure shows the flooded area in Dresden during different flood events
Occurrence of major floods in Europe Occurrence of major floods in Europe Occurrence of major floods in Europe (1998–2009)

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Flood risk in Europe: the long-term outlook Flood risk in Europe: the long-term outlook Floods in Central Europe have caused deaths and widespread property damage across parts of the Czech Republic, Germany and Austria. Such events are likely to increase in Europe for several reasons including climate change, according to recent assessments from the European Environment Agency (EEA).
Climate change and air Climate change and air Our climate is changing. Many climate-changing gases are also common air pollutants that affect our health and the environment. In many ways, improving air quality can also give a boost to climate change mitigation efforts and vice versa, but not always. The challenge ahead is to ensure that climate and air policies focus on win-win scenarios.
Climate change evident across Europe, confirming urgent need for adaptation Climate change evident across Europe, confirming urgent need for adaptation Climate change is affecting all regions in Europe, causing a wide range of impacts on society and the environment. Further impacts are expected in the future, potentially causing high damage costs, according to the latest assessment published by the European Environment Agency today.

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For references, please go to https://www.eea.europa.eu/publications/climate-impacts-and-vulnerability-2012 or scan the QR code.

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