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You are here: Home / Publications / Climate change, impacts and vulnerability in Europe 2012

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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  • EEA (European Environment Agency)
  • Published: Nov 21, 2012

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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 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.
Impacts and consequences of climate change on forest growth and forest conditions Impacts and consequences of climate change on forest growth and forest conditions Table shows the impacts and consequences of climate change on forest growth and forest conditions.
Burnt forest area in five southern European countries - outdated Burnt forest area in five southern European countries - outdated Burnt forest area (in ha) in five southern European countries from 1980 to 2010.
European urban population trends European urban population trends The graph shows the proportion of urban population of the overall population in Europe and the absolute numbers of urban population over time
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.
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.
Potential environmental and economic impact of climate change Potential environmental and economic impact of climate change (Environmental) Combined potential impacts of changes in summer and winter precipitation, heavy rainfall days, annual mean temperature, summer days, frost days, snow cover days and annual mean evaporation on soil erosion, soil organic carbon content, protected natural areas and forest fire sensitivity. (Economic) Combined potential impacts of changes in annual mean evaporation, summer days, snow cover days, frost days, changes in inundation heights of a 100 year river flood event and a sea level rise adjusted 100 year coastal storm surge event on agriculture, forestry, summer and winter tourism, energy supply and demand.
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).
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 impact of climate change on the potential distribution of reptiles and amphibians in 2050 Projected impact of climate change on the potential distribution of reptiles and amphibians in 2050 Projected data based on the Generalised Linear Model map using the HadCM3 A2 scenario for the 2050s are compared with the current situation.
Water scarcity and drought events in Europe during the last decade Water scarcity and drought events in Europe during the last decade The map shows the main drought events in Europe in 2002 - 2011
Water temperature of large European rivers and lakes Water temperature of large European rivers and lakes Annual average water temperature in River Rhine and River Meuse (1911–2010); River Danube (1901–1998), Lake Võrtsjärv (1947–2011), and average water temperature in August in Lake Saimaa, Finland (1924–2011).
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.
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).
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).
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).
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.
Areas affected by three recurrent negative precipitation anomalies (2000, 2003 and 2006) Areas affected by three recurrent negative precipitation anomalies (2000, 2003 and 2006) (upper) Areas affected by three recurrent negative precipitation anomalies (as indicated in the graph: 2000, 2003 and 2006 during the period 1999–2010 based on the standardised precipitation index, a statistical correlation to recurrent anomalies through principal component analysis (PCA) of time series climatic data). The prevalence of recurrent negative precipitation anomalies in the Mediterranean area is very apparent. Such anomalies can also affect areas with traditionally humid climates. (lower) The effect of these events on ecosystem productivity based on a change index: the steadiness index. This index addresses both the long-term trend and the net change of primary production calculated from satellite time series over the period in which the recurrent negative precipitation anomalies occurred (1999–2010). The areas in red denote regions with a stronger likelihood of decreased productivity relative to the ecosystem capacity.
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).
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.
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.
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.
Natural disasters in EEA member countries Natural disasters in EEA member countries Natural disasters in EEA member countries from 1980 to 2011. Events can occur in several countries; events are counted country-wise.
Contributions to the sea level budget since 1972 Contributions to the sea level budget since 1972 Table showing the yearly contributions to the sea level budget
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)
Trend in heating degree days in the EU-27 Trend in heating degree days in the EU-27 The map shows the trend in heating degree days in the EU-27 (1980-2009)
Annual mean ozone concentrations by station type Annual mean ozone concentrations by station type Annual mean ozone concentrations by station type
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.
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.
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.
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.
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.
Observed change in duration of lake and river ice cover Observed change in duration of lake and river ice cover Ice break-up dates and freezing dates of a) Danube River, at Budapest, 1876–2011 (5-year running average) and b) Lake Kallavesi, Finland, 1833–2011.
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.
Coastline dynamics in Europe Coastline dynamics in Europe The map and the graph show the coastal erosion patterns in Europe (2004)
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.
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
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
Location of Argo floats in 2012 Location of Argo floats in 2012 The broad-scale global array of temperature/salinity profiling floats, known as Argo, has already grown to be a major component of the ocean observing system. Argo is a standard to which other developing ocean observing systems can look to. For example, Argo offers ideas on various topics such as how to collaborate internationally, how to develop a data management system and how to change the way scientists think about collecting data. Deployments began in 2000 and continue today at the rate of about 800 per year.
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).
Decapoda larvae abundance and phenology in the central North Sea Decapoda larvae abundance and phenology in the central North Sea Left: Decapoda larvae abundance (number of individuals) in the central North Sea (1958–2009). Right: Phenology shown as average month of peak decapoda abundancein the central North Sea (1958–2009).
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.
Ocean heat content calculated based on observations made in the upper 700 m of the water column Ocean heat content calculated based on observations made in the upper 700 m of the water column Ocean heat content is defined as the integrated temperature change times the density of seawater, times specific heat capacity from the surface down to the deep ocean.
Decline in pH measured at the Aloha station as part of the Hawaii Ocean time-series Decline in pH measured at the Aloha station as part of the Hawaii Ocean time-series Aloha station pH time series. Changes here are similar to those that are observed at a much shorter time scale in Europe.
European summer temperatures for 1500-2010 European summer temperatures for 1500-2010 Statistical frequency of the european temperature
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−1), statistical significance above 0.95 is shown by black dots.
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)
Number of people affected by flooding per million population in the WHO European Region Number of people affected by flooding per million population in the WHO European Region Number of people affected by flooding per million population in the WHO European Region (annual average 2000–2011). ‘People affected’, as defined in EM-DAT, are people who require immediate assistance during a period of emergency, including displaced or evacuated people. EM-DAT/CRED and the Dartmouth Flood Observatory were analysed to determine the flooded countries in the WHO European Region and the impact of these floods.
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 20-year maximum precipitation in summer and winter Projected changes in 20-year maximum precipitation in summer and winter Projected changes in 20-year maximum daily precipitation in summer (left) and winter (right) from 1961–1990 to 2071–2100 based on the ensemble mean using a regional climate model (RCM) nested in 6 general circulation model (GCMs). Changes that approximately lie outside of ± 10 % for the ensemble average are significant at the 10 % significance level.
Trends in consecutive dry days and consecutive wet days Trends in consecutive dry days and consecutive wet days High confidence in a long-term trend is shown by a black dot (if the 5th to 95th percentile slopes are of the same sign). Boxes which have a thick outline contain at least three stations. Area averaged annual time series of percentage changes and trend lines are shown below each map for one area in northern Europe (blue line, 5.6 to 16.9 °E and 56.2 to 66.2 °N) and one in south-western Europe (red line, 350.6 to 1.9 °E and 36.2 to 43.7 °N).
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.
Trends in annual precipitation across Europe Trends in annual precipitation across Europe The trends are calculated using a median of pairwise slopes algorithm. Black dots represent high confidence in the sign of the long-term trend in the box (if the 5th to 95th percentile slopes are of the same sign). Boxes which have a thick outline contain at least three stations. Area averaged annual time series of percentage changes and trend lines are shown below each map for one area in northern Europe (blue line, 5.6 to 16.9 °E and 56.2 to 66.2 °N) and one in south‑western Europe (red line, 350.6 to 1.9 °E and 36.2 to 43.7 °N).
Climatic suitability for the mosquitos Aedes aegypti and Aedes albopictus in Europe Climatic suitability for the mosquitos Aedes aegypti and Aedes albopictus in Europe This figure shows the climatic suitability for the mosquitos Aedes aegypti (left) and Aedes albopictus (right) in Europe. Darker to lighter green indicates conditions not suitable for the vector whereas yellow to red colours indicate conditions that are increasingly suitable for the vector. Grey indicates that no prediction is possible.
Change in the distribution of Aedes albopictus in Europe Change in the distribution of Aedes albopictus in Europe Areas marked as ‘2011’ indicate that the tiger mosquito was detected in 2011 for the first time. They include areas of known geographical expansion of A. albopictus in France, northern Italy and Spain where vector surveillance has been in place since 2008 but also areas in Albania, Greece, and central and southern Italy, where the first detection of the vector in 2011 could be the result of increased vector surveillance rather than actual geographical expansion. ‘2008–2011’ refers to all areas where the vector has been present before 2011. Indoor presence corresponds to the presence recorded in greenhouses.
European distribution of Borrelia burgdorferi in questing I. ricinus ticks European distribution of Borrelia burgdorferi in questing I. ricinus ticks The risks described in this figure are relative to each other according to a standard distribution scale. Risk is defined as the probability of finding nymphal ticks positive for Borrelia burgdorferi. For each prevalence quartile, associated climate traits were used to produce a qualitative evaluation of risk according to Office International des Epizooties (OIE) standards at five levels (high, moderate, low, negligible, and null), which directly correlate with the probability of finding nymphal ticks with prevalence in the four quartiles.
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.
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.
Change in global mean sea level Change in global mean sea level The figure shows the global mean sea level from 1860 to 2009 as estimated from coastal and island sea-level data (1880 – 2009, blue) and from satellite altimeter data (1993 – 2009, grey).
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 change in dates of flowering and maturation for winter wheat Projected change in dates of flowering and maturation for winter wheat This figure shows the model estimated mean change in dates of flowering and full maturation for winter wheat for the period 2031–2050 compared with 1975–1994 for the RACMO (KNMI) and HadRCM3 (Hadley Centre.HC) projections under the A1B emission scenario.
Projected changes in water-limited crop yield Projected changes in water-limited crop yield This figure shows the mean relative changes in water-limited crop yield simulated by the ClimateCrop model for the 2050s compared with 1961–1990 for 12 different climate models projections under the A1B emission scenario.
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.
Projected changes in effective solar radiation from two climate models Projected changes in effective solar radiation from two climate models The map shows the mean changes in effective solar radiation (MJ m-2), which is an indicator for water-limited crop productivity, for the period 2031–2050 compared with 1975–1994 for the RACMO (KNMI) and HadRCM3 (Hadley Centre.HC) projections under the A1B emission scenario.
Projected change in water availability for irrigation in the Mediterranean region Projected change in water availability for irrigation in the Mediterranean region This figure shows the relative change in water availability for irrigation as projected under the A1B emission scenario by the HIRHAM (DMI) regional climate model for 2071-2100 relative to 1961-1990. Light yellow areas indicate no change in water availability.
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.
Change in the number of frost-free days per year Change in the number of frost-free days per year This figure shows the rate of change in the growing season length (defined as the number of frost-free days per year) during the period January 1975 – December 2010.
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.
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 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 changes in the climate niche space of the Small Tortoise shell Projected changes in the climate niche space of the Small Tortoise shell This figure shows the future distribution of climate niche space of the Small Tortoise shell (Aglais urticae) under the A2 climate change scenario and two future time periods (2021-2050 left, 2051-2080 right). Dark grey areas show space that remains suitable, magenta areas space that is lost and green areas show space that could be gained under full dispersal. Northern parts of Europe are expected to remain suitable for the Small Tortoiseshell under all scenarios, but large areas of central Europe would become unsuitable. The worst case loss is 55% of its climatic niche by 2080 under no dispersal or 46% loss under full dispersal.
Projected spatial mismatches of the Portuguese Dappled White butterfly and its host plants Projected spatial mismatches of the Portuguese Dappled White butterfly and its host plants This figure shows spatial mismatches of the Portuguese Dappled White butterfly (Euchloe tagis) and its host plants under the BAMBU scenario (climate: A2) for 2050-2080. Green, suitable climate space for the host plants;orange, suitable climate space for the butterfly; red, suitable area for both butterfly and host plants; open circles, currently observed distribution. BAMBU: Business-As-Might-Be-Usual scenario.
Projected changes in mammalian species richness Projected changes in mammalian species richness Projected changes in in mammalian species richness by 2100 under emissions scenarios B1 (left) and A2 (right) in a 10’ resolution. Units in percentage.
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.
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
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).
Trend in Arctic Sea ice extent in March and September Trend in Arctic Sea ice extent in March and September This figure shows the time series of Arctic Sea ice extent from 1979 to 2012. Trend lines and observation points for March (the month of sea ice extent maximum) and September (the month of sea ice extent minimum) have been indicated.
Trend in March snow mass in Europe Trend in March snow mass in Europe The figure (left) shows anomalies for March snow mass in the EEA region (excluding mountain areas) and the 30-year linear trend. The map (right) shows a snapshot of snow cover on 15 February 2009.
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.
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
Trend in annual temperature across Europe Trend in annual temperature across Europe 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 map 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).
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.
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.
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.
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.
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).
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).
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.
Global average air temperature anomalies (1850 to 2011) in degrees Celsius (°C) relative to a pre-industrial baseline period Global average air temperature anomalies (1850 to 2011) in degrees Celsius (°C) relative to a pre-industrial baseline period 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.
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).
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).
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
Variations in topsoil organic carbon content across Europe Variations in topsoil organic carbon content across Europe The map shows the percentage of organic carbon content in the surface horizon of soils in Europe. The darker regions correspond to soils with high values of organic carbon. The darkest colours, especially in Estonia, Fennoscandinavia, Ireland and the United Kingdom, denote peatlands.
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)
Trend in egg-laying dates of the Pied flycatcher across Europe Trend in egg-laying dates of the Pied flycatcher across Europe This map shows the trend in egg-laying dates of the Pied flycatcher across Europe (1980-2004). Dots: weather stations used to calculate changes in local egg-laying dates (derived from temperature data); triangles: location of Pied flycatcher laying date time series.
Projected change in daily average river flow for four rivers Projected change in daily average river flow for four rivers Projected river flow 2071-2100 (green line) and the observed river flow 1961-1990 (orange line)
Temperature-mortality relationship in 15 European cities Temperature-mortality relationship in 15 European cities Figure shows relationship between daily maximum apparent temperature (Barcelona: mean apparent temperature) and natural mortality (blue) and 95% confidence interval (grey).
Modelled change in tropospheric ozone concentrations over Europe Modelled change in tropospheric ozone concentrations over Europe The modelled changes shown are only due to climate variability and climate change
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.

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