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Indicator Assessment
Past trends: global temperature
Records of global average temperature show long-term warming trends since the end of the 19th century, which have been most rapid since the 1970s. Three independent analyses of global average temperature using near-surface observation records — HadCRUT4 [1], NOAA Global Temp [2], GISTEMP [3] by the NASA Goddard Institute for Space Studies and— show very similar amounts of warming. ERA-Interim reanalysis [4] dataset prepared by Copernicus Climate Change Service (C3S) (managed by European Centre for Medium-Range Weather Forecasts (ECMWF)) shows slightly higher increases in global temperature than datasets based only on in-situ observations. These differences arise from regions where there are few direct temperature measurements, especially over the Arctic and Antarctic where variability from year to year is high. They also arise from the adjustments needed to estimate sea surface temperature from measurements made at different depths and with different biases [5].
All datasets show warming compared with pre-industrial temperatures (using the earliest observations from the period 1850–1900 as a proxy) of between 0.87 and 0.92 °C for the decade 2007–2016 (Figure 1). This magnitude of warming corresponds to almost half of the 2 °C warming that is compatible with the global climate stabilisation target of the EU and the ultimate objective of the UNFCCC [6]. Similar estimations of warming have also been obtained through ‘climate reanalysis’. The year 2016 was the warmest on record according to different near-surface temperature observational analyses, with temperatures greater than 1.1 °C above pre-industrial levels [7] . The year 2015 was the second warmest on record. Note that such statements are always associated with some uncertainty, primarily because of spatial and temporal gaps in the data record and different interpolation methods [8].
Furthermore, the annual temperature anomalies are also strongly influenced by climate variability due to natural forcings (volcanic eruptions and solar activity) and by internal variability within the climate system (e.g. multi-annual climate fluctuations such as the ENSO, which influence the exchange of heat between the atmosphere and oceans) [9].
Global ocean heat content has been increasing continuously since the 1950s down to at least 2 000 m, with no sign of any slow-down. Furthermore, a recent study that uses new datasets of the sea surface temperature and more sophisticated interpolation methods for data-sparse regions such as the Arctic suggests that the increase in global average temperature since 1998 was higher than the increase in the observed near-surface temperature as used for the IPCC AR5 [10]. Changing the start and end years also has an effect on the calculated rate of change, but this is less than that seen from newly available data and different methods for interpolation.
Projections: global temperature
The global average temperature will continue to increase throughout this century as a result of projected further increases in greenhouse gas concentrations. The CMIP5 climate projections summarised in the IPCC AR5 project that global temperature will increase by mid-century (2046–2065 relative to 1986–2005) by 0.4–1.6 °C for RCP2.6, 0.9–2.0 °C for RCP4.5, 0.8–1.8 °C for RCP6.0 and 1.4–2.6 °C for RCP8.5; the warming projections for the end of the century (2081–2100) are 0.3–1.7 °C for RCP2.6, 1.1–2.6 °C for RCP4.5, 1.4–3.1 °C for RCP6.0 and 2.6–4.8 °C for RCP8.5. All projections show greater warming over land than over the oceans. Projected warming is strongest in the Arctic at about twice the global average. These patterns are consistent with the observations during the latter part of the 20th century [12].
The UNFCCC target of limiting global average warming to less than 2.0 °C above pre-industrial levels is projected to be exceeded between 2042 and 2050 by the three highest of the four RCP scenarios [13]. The lowest, RCP2.6, implies a strong reduction in greenhouse gas emissions over this century and negligible or even negative emissions at the end of the century [14].
[1] Morice et al., “Quantifying Uncertainties in Global and Regional Temperature Change Using an Ensemble of Observational Estimates: The HadCRUT4 Data Set,”Journal of Geophysical Research 117, no. D8 (April 17, 2012): D08101, doi:10.1029/2011JD017187.
[2] Karl et al., “Possible Artifacts of Data Biases in the Recent Global Surface Warming Hiatus,”Science 348, no. 6242 (June 26, 2015): 1469–72, doi:10.1126/science.aaa5632.
[3] Hansen et al., “GISS Analysis of Surface Temperature Change,”Journal of Geophysical Research: Atmospheres 104, no. D24 (December 27, 1999): 30997–22, doi:10.1029/1999JD900835.
[4] Dee et al., “The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System,”Quarterly Journal of the Royal Meteorological Society 137, no. 656 (April 1, 2011): 553–97, doi:10.1002/qj.828.
[5] Simmons et al., “A Reassessment of Temperature Variations and Trends from Global Reanalyses and Monthly Surface Climatological Datasets,”Quarterly Journal of the Royal Meteorological Society 143, no. 702 (January 1, 2017): 101–19, doi:10.1002/qj.2949.
[6] UNFCCC, “Report of the Conference of the Parties on Its Fifteenth Session, Held in Copenhagen from 7 to 19 December 2009” (Copenhagen: UNFCCC, 2009), http://unfccc.int/resource/docs/2009/cop15/eng/11a01.pdf.
[7] (WMO) World Meteorological Organization, “WMO Statement on the Status of the Global Climate” (WMO, 2017), http://library.wmo.int/opac/index.php?lvl=notice_display&id=97.
[8] Blunden J. and Arndt, “State of the Climate in 2014,”Bulletin of the American Meteorological Society 96, no. 7 (July 1, 2015): ES1–32, doi:10.1175/2015BAMSStateoftheClimate.1.
[9] IPCC,Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge; New York: Cambridge University Press, 2013), http://www.climatechange2013.org/.
[10] Karl et al., “Possible Artifacts of Data Biases in the Recent Global Surface Warming Hiatus”; John C. Fyfe et al., “Making Sense of the Early-2000s Warming Slowdown,”Nature Climate Change 6, no. 3 (March 2016): 224–28, doi:10.1038/nclimate2938.
[12] M. Collins et al., “Long-Term Climate Change: Projections, Commitments and Irreversibility,” inClimate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed. T. F. Stocker et al. (Cambridge; New York: Cambridge University Press, 2013), 1029–1136, http://www.climatechange2013.org/images/report/WG1AR5_Chapter12_FINAL.pdf.
[13] Robert Vautard et al., “The European Climate under a 2 °C Global Warming,”Environmental Research Letters 9, no. 3 (March 1, 2014): 034006, doi:10.1088/1748-9326/9/3/034006.
[14] Richard H. Moss et al., “The next Generation of Scenarios for Climate Change Research and Assessment,”Nature 463, no. 7282 (February 11, 2010): 747–56, doi:10.1038/nature08823.
Trends in annual temperature across Europe between 1960 and 2016
Note: Grid boxes outlined in solid black contain at least three stations and so are likely to be more representative of the grid box. A significant (at the 5 % level) long-term trend is shown by a black dot.
Projected changes in annual, summer and winter temperature
Note: Projected changes in annual (left), summer (middle) and winter (right) near-surface air temperature (°C) in the period 2071-2100, compared with the baseline period 1971-2000 for the forcing scenarios RCP 4.5 (top) and RCP 8.5 (bottom). Model simulations are based on the multi-model ensemble average of RCM simulations from the EURO-CORDEX initiative.
Past trends: European temperature
The average annual temperature over European land areas increased by 1.56 to 1.61 °C in 2007–2016 relative to the pre-industrial period; this increase is larger than the increase in global average temperature. This makes it the warmest decade on record (Figure 2). The grey shading shows the 95 % confidence interval, which reflects uncertainties owing to areas without observation stations, inhomogeneities in measurements and biases as a result of urbanisation [1]. The warmest year in Europe since instrumental records began was 2014 with 2015 and 2016 closely joint second, and these record temperatures were 35–80 times more likely because of anthropogenic climate change [2]. Moreover, climate reconstructions show that summer temperatures in Europe in the last three decades (1987–2016) have been the warmest for at least 2 000 years, and that they lie significantly outside the range of natural variability [3].
Based on the E-OBS dataset, all of Europe has warmed significantly since the 1960s [4]. Particularly large warming has been observed over the Iberian Peninsula, mostly in summer, across central and north-eastern Europe, and in mountainous regions (Figure 3). Warming since the 1960s has been strongest and most significant over Scandinavia, especially in winter.
Projections: European temperature
Temperatures across Europe are projected to continue to increase throughout this century. Projections from the EURO-CORDEX initiative suggest that European land areas will warm faster on average than global land areas [5]. According to the multi-model ensemble mean, European land areas are projected to warm in the range of 1-4.5 °C for the RCP4.5 scenario and in the range of 2.5 to 5.5 °C for RCP8.5 over the 21st century (2071–2100 compared with 1971–2000) (Figure 4). The strongest warming is projected over north-eastern Europe and Scandinavia in winter and over southern Europe in summer.
[1] van der Schrier, G. et al., 2013, Monitoring European Average Temperature Based on the E-OBS Gridded Data Set, Journal of Geophysical Research: Atmospheres 118, no. 11 (2013): 5120–35, doi:10.1002/jgrd.50444.
[2] EURO4M, 2014: Warmest Year on Record in Europe, Climate Indicator Bulletin (EURO4M, 2015), (http://cib.knmi.nl/mediawiki/index.php/2014_warmest_year_on_record_in_Europe); Kam, J. et al., 2015, Record Annual Mean Warmth over Europe, the Northeast Pacific, and the Northwest Atlantic during 2014: Assessment of Anthropogenic Influence, Bulletin of the American Meteorological Society 96, no. 12 (December 1, 2015): S61–65, doi:10.1175/BAMS-EEE_2014_ch13.1; EURO4M, 2017, European Climate in 2016, (http://cib.knmi.nl/mediawiki/index.php/European_climate_in_2016).
[3] Luterbacher, J. et al., 2016, European Summer Temperatures since Roman Times, Environmental Research Letters 11, no. 2: 024001, doi:10.1088/1748-9326/11/2/024001.
[4] Haylock, M.R. et al., 2008, A European Daily High-Resolution Gridded Data Set of Surface Temperature and Precipitation for 1950–2006, Journal of Geophysical Research 113, no. D20 (2008): D20119, doi:10.1029/2008JD010201.
[5] Jacob, D. et al., 2014, EURO-CORDEX: New High-Resolution Climate Change Projections for European Impact Research, Regional Environmental Change 14, no. 2 (2014): 563–78, doi:10.1007/s10113-013-0499-2.
Observed trends in warm days across Europe between 1960 and 2016
Note: 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 three stations and so are likely to be more representative of the grid box. A significant (at the 5 % level) long-term trend is shown by a black dot
Number of extreme heat waves in future climates under two different climate forcing scenarios
Note: The top maps show the median of the number of heat waves in a multi-model ensemble of the near future (2020–2052) and the latter half of the century (2068–2100) under the RCP4.5 scenario, and the lower maps are for the same time periods but under RCP8.5
Past trends: heat extremes
Observational data show a continued increase in heat extremes over land in the period 1997–2012) [1]. At the global scale, warm days and nights, as well as heat waves, have become more frequent in recent decades. The increase in maximum daily temperatures has generally been faster than the increase in annual average temperature [2]. In Europe, since the 1950s, large areas have experienced intense and long heat waves, with notable impacts on human health and socio-economic systems [3]. As a result, 500-year-old temperature records were broken over 65 % of Europe in the period 2003–2010 alone [4].
Indices for extreme temperatures, including the annual maximum value for daily maximum temperature (Txx), have shown significant upwards trends across Europe between 1951 and 2012 [5]. The number of unusually warm days (Tx90p) has increased by up to 10 days per decade between 1960 and 2016 in most of southern Europe and Scandinavia (Figure 1).
Although the air temperature averaged over the whole of Europe did not break any records in 2016, there were three extreme events that were exceptional and occurred over large areas across Europe [6]. The most severe heat waves have been characterised by the persistence of extremely high night-time temperatures [7]. A substantial fraction of the probability of recent extreme events can be attributed to human-induced climate change, and it is likely that, for temperature extremes occurring over previous decades, a fraction of their probability was attributable to anthropogenic influences [8].
Projections: heat extremes
Periods with extreme high temperatures are projected to become more frequent and to last longer across Europe during this century [9]. Projections based on a multi-model ensemble agree on increases in heat wave frequency and magnitude for most European regions during the 21st century under all RCP scenarios. Extreme summer heat waves, such as the ones experienced in different parts of Europe in 2003 and 2010, will become much more common in the future. Under the RCP8.5 scenario, very extreme heat waves ([10]) (much stronger than either the 2003 or the 2010 heat waves) are projected to occur as often as every two years in the second half of the 21st century (Figure 2). The projected frequency of heat waves is greatest in southern and south-eastern Europe [11]. According to a different analysis, at the end of the 21st century, 90 % of the summers in southern, central and north-western Europe will be warmer than any summer in the period 1920–2014 under the RCP8.5 scenario [12]. The most severe health risks are projected for low-altitude river basins in southern Europe and for the Mediterranean coasts, where many densely populated urban centres are located [13].
[1] Seneviratne, S.I. et al., 2014, No Pause in the Increase of Hot Temperature Extremes, Nature Climate Change 4, no. 3 (March 2014): 161–63, doi:10.1038/nclimate2145.
[2] IPCC, 2013, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
[3] García-Herrera, R. et al., 2010, A Review of the European Summer Heat Wave of 2003, Critical Reviews in Environmental Science and Technology 40, no. 4 (March 9, 2010): 267–306, doi:10.1080/10643380802238137; Russo, S., Sillmann, J., and Fischer, E.M., 2015, Top Ten European Heatwaves since 1950 and Their Occurrence in the Coming Decades, Environmental Research Letters 10, no. 12 (2015): 124003, doi:10.1088/1748-9326/10/12/124003.
[4] Barriopedro, D. et al., 2011, The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe, Science 332, no. 6026 (March 17, 2011): 220–24, doi:10.1126/science.1201224.
[5] Donat, M.G. et al., 2013, Global Land-Based Datasets for Monitoring Climatic Extremes, Bulletin of the American Meteorological Society 94, no. 7 (July 2013): 997–1006, doi:10.1175/BAMS-D-12-00109.1.
[6] EURO4M, 2016, European Climate in 2016.
[7] Sillmann, R. and Fischer E.M., Top Ten European Heatwaves since 1950 and Their Occurrence in the Coming Decades.
[8] King, A.D. et al., 2016, Emergence of Heat Extremes Attributable to Anthropogenic Influences, Geophysical Research Letters 43, no. 7 (April 16, 2016): 3438–43, doi:10.1002/2015GL067448.
[9] Fischer, E.M. and Schär, C., 2010, Consistent Geographical Patterns of Changes in High-Impact European Heatwaves, Nature Geoscience 3 (May 16, 2010): 398–403, doi:10.1038/ngeo866; Russo, S. et al., 2014, Magnitude of Extreme Heat Waves in Present Climate and Their Projection in a Warming World, Journal of Geophysical Research: Atmospheres 119, no. 22 (November 27, 2014): 12500–512, doi:10.1002/2014JD022098; Schoetter, R., Cattiaux, J., and Douville, H., 2014, Changes of Western European Heat Wave Characteristics Projected by the CMIP5 Ensemble, Climate Dynamics 45, no. 5–6 (December 12, 2014): 1601–16, doi:10.1007/s00382-014-2434-8.
[10] To assess changes in heat waves, the HWMI has been used. The HWMI is defined based on the magnitude and length of heat waves in a year, where heat waves are periods of at least three consecutive days with a maximum temperature above the threshold for the reference period 1981–2010. For details, including the definition of very extreme heat waves, see Russo et al. Magnitude of Extreme Heat Waves in Present Climate and Their Projection in a Warming World.
[11] Russo et al., Magnitude of Extreme Heat Waves in Present Climate and Their Projection in a Warming World.
[12] Lehner, F., Deser, C., and Sanderson, B.M., 2016, Future Risk of Record-Breaking Summer Temperatures and Its Mitigation, Climatic Change, February 16, 2016, doi:10.1007/s10584-016-1616-2.
[13] Fischer, E.M. and Schär, C., Consistent Geographical Patterns of Changes in High-Impact European Heatwaves.
This indicator shows absolute changes and rates of change in average near-surface temperature for the globe and for a region covering Europe. Near-surface air temperature gives one of the clearest and most consistent signals of global and regional climate change, especially in recent decades. It has been measured for many decades — even centuries at some locations — and a dense network of stations across the globe, especially in Europe, provides regular monitoring of temperature, using standardised measurements, quality control and homogeneity procedures.
Global average annual temperature deviations (anomalies) are discussed relative to a ‘pre-industrial’ period between 1850 and 1899 (the beginning of instrumental temperature records). During this time, anthropogenic greenhouse gases from the industrial revolution (between 1750 and 1850) are considered to have had a relatively small influence on the climate compared with natural influences. However, it should be noted that owing to earlier changes in the climate due to internal and forced natural variability, there was not one single pre-industrial climate and it is not clear that there is a rigorous scientific definition of the term ‘pre-industrial climate’.
Temperature changes also influence other aspects of the climate system that can have an impact on human activities, including sea level, intensity and frequency of floods and droughts, biota and food productivity, and infectious diseases. In addition to the global average target, seasonal variations and spatial distributions of temperature change are important, for example, to understand the risks that current climate poses to human and natural systems and to assess how these may be impacted by future climate change.
The units used in this indicator are degrees Celsius (°C) and degrees Celsius per decade (°C/decade).
Baseline period
Global average annual temperature is expressed here relative to a ‘pre-industrial’ baseline period of 1850 to 1899. This period coincides with the beginning of widespread instrumental temperature records. During this time, anthropogenic greenhouse gases from pre-1850 industrial activity had a relatively small influence on climate compared with natural influences. However, it should be noted that there is no rigorous scientific definition of the term ‘pre-industrial climate’ because climate also changed prior to 1850 due to internal and forced natural variability. Other studies sometimes use a different climatological baseline period, such as 1971-2000, used in parts of the IPCC Working Group One contribution to the Fifth Assessment Report (IPCC, 2013).
In April 2013, the European Commission (EC) presented the EU Adaptation Strategy Package. This package consists of the EU Strategy on adaptation to climate change (COM/2013/216 final) and a number of supporting documents. The overall aim of the EU Adaptation Strategy is to contribute to a more climate-resilient Europe.
One of the objectives of the EU Adaptation Strategy is 'Better informed decision-making', which will be achieved by bridging the knowledge gap and further developing the European climate adaptation platform (Climate-ADAPT) as the ‘one-stop shop’ for adaptation information in Europe. Climate-ADAPT has been developed jointly by the EC and the EEA to share knowledge on (1) observed and projected climate change and its impacts on environmental and social systems and on human health, (2) relevant research, (3) EU, transnational, national and subnational adaptation strategies and plans, and (4) adaptation case studies.
Further objectives include 'Promoting adaptation in key vulnerable sectors through climate-proofing EU sector policies' and 'Promoting action by Member States'. Most EU Member States have already adopted national adaptation strategies and many have also prepared action plans on climate change adaptation. The EC also supports adaptation in cities through the Covenant of Mayors for Climate and Energy initiative.
In September 2016, the EC presented an indicative roadmap for the evaluation of the EU Adaptation Strategy by 2018.
In November 2013, the European Parliament and the European Council adopted the Seventh EU Environment Action Programme (7th EAP) to 2020, ‘Living well, within the limits of our planet’. The 7th EAP is intended to help guide EU action on environment and climate change up to and beyond 2020. It highlights that ‘Action to mitigate and adapt to climate change will increase the resilience of the Union’s economy and society, while stimulating innovation and protecting the Union’s natural resources.’ Consequently, several priority objectives of the 7th EAP refer to climate change adaptation.
To avoid serious climate change impacts, in its Sixth Environmental Action Programme (6th EAP) — reaffirmed by the Environment Council and the European Council of 22-23 March 2005 (Presidency Conclusions, section IV (46)) and later in the Seventh Environmental Action Programme (7EAP, 2014) — the European Council proposed that the global average temperature increase should be limited to not more than 2 °C above pre-industrial levels. Furthermore, in the Copenhagen Accord (UNFCCC, 2009), the UNFCCC's 15th conference of the parties (COP15) recognised the scientific evidence for the need to keep global average temperature increase below 2 °C above pre-industrial levels. In addition, some studies have proposed a 'sustainable' target of limiting the rate of anthropogenic warming to 0.1-0.2°C per decade.
The target for absolute temperature change (i.e. 2 °C) was initially derived from the variation of global mean temperature during the Holocene — the period since the last ice age during which human civilisation developed. Further studies (IPCC, 2007;Vautard, 2014) have pointed out that even a global temperature increase that remains below the 2 °C target would still result in considerable impacts. Vulnerable regions across the world, in particular in developing countries (including the least developed countries, small developing island states and Africa), would be most strongly affected.
In December 2015 (UNFCCC, 2015), countries adopted the Paris Agreement, which includes a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the increase to 1.5 °C above pre-industrial levels, since this would significantly reduce risks and the impacts of climate change.
Mainstreaming climate change adaptation in EU policies is one of the pillars of the EU Adaptation strategy. In the Europe 2020 strategy for smart, sustainable and inclusive growth, the following is stated on combating climate change: 'We must also strengthen our economies, their resilience to climate risks, and our capacity for disaster prevention and response'.
Various data sets on trends in global and European temperature have been used for this indicator:
For global and European average seasonal and annual temperature three datasets are used.
1) The HadCRUT is a collaborative product of the Met. Office Hadley Centre (sea surface temperature) and the Climatic Research Unit (land temperature) at the University of East Anglia. The global mean annual temperature deviations are, in the original source, in relation to the base period 1961-1990. The annual deviations shown in the chart have been adjusted to be relative to the period 1880-1899 in order to better monitor the EU objective to not exceed 2 °C above pre-industrial levels.
2) The GISS surface temperature is a product of the Goddard Institute for Space Studies under NASA. The original source anomalies are calculated in relation to the 1951-1980 baseline period. Annual deviations shown on the chart are adjusted to the 1880-1899 period to better monitor the EU objective of a maximum 2 °C global temperature increase above pre-industrial levels. The indicator has been calculated as a combination of land and sea temperature.
3) The GlobalTemp surface temperature is a product of the National Centres for Environmental Information from the National Oceanic and Atmospheric Administration (NOAA). Datasets are available in monthly time steps as a gridded product from 1880 onwards. The dataset was created from station data using the Anomaly Method, a method that uses station averages during a specified baseline period from which the monthly/seasonal/annual departures can be calculated. Anomalies were calculated on a monthly basis for all adjusted stations having at least 20 years of data in the 1961–1990 baseline period. Station anomalies were then averaged within each 5° by 5° grid box to obtain the gridded anomalies. For those grid boxes without adjusted data, anomalies were calculated from the raw station data using the same technique.
4) The ERA-Interim dataset is a reanalysis product from the ECMWF. The datasets maintained under Copernicus Climate change Service (C3S). The reanalysis dataset covers the period from 1979 to the present. ERA-Interim combines information from meteorological observations with background information from a forecast model, using the data assimilation approach developed for numerical weather prediction. The atmospheric observing system underwent several improvements leading up to 1979.
Over land, values of surface air temperature from ERA-Interim are in effect determined quite directly from observational records for regions where plentiful observations of surface air temperature were made. Elsewhere, the background forecast model plays a stronger role, helping values of surface air temperature to be derived from other types of observation, such as of sea-surface temperatures and winds. Satellite data on the extent of sea-ice cover are important in winter, as surface air temperatures tend to be much warmer over open sea than over ice. Observations of conditions higher in the atmosphere provide some additional information.
For European average temperature over land, the same datasets are used. Europe is defined as the area between 35° to 70° Northern latitude, -25° to 30° Eastern longitude, plus Turkey (35° to 40° North, 30° to 45° East). The European anomalies are, in the original source, in relation to the 1961-1990 baseline period. The annual deviations shown in the chart have been adjusted to be relative to the 1850-1899 period.
Temperature trends in Europe are obtained by using data from the E-OBS database. E-OBS is a daily gridded observational dataset for precipitation, temperature and sea level pressure in Europe based on ECA&D information. The full dataset covers the period 1950-01-01 until 2016-08-31. It was originally developed and updated as part of the ENSEMBLES (EU-FP6) and EURO4M (EU-FP7) projects. Currently it is maintained and elaborated as part of the UERRA project (EU-FP7). Trends are calculated using a median of pairwise slopes algorithms.
Annual, winter (December, January, February) and summer (June, July, August) mean temperature deviations in Europe (oC). The lines in the chart refer to 10-year moving average European land.
Projected changes in annual (left), summer (middle) and winter (right) near-surface air temperature (°C) in the 2071-2100 period compared to the baseline period 1971-2000 for the forcing scenarios RCP 4.5 (top) and RCP 8.5 (bottom). Model simulations are based on the multi-model ensemble average of RCM simulations from the EURO-CORDEX initiative. EURO-CORDEX is the European branch of the international CORDEX initiative, a programme sponsored by the World Climate Research Program (WRCP) to organise an internationally coordinated framework to produce improved regional climate change projections for all land regions worldwide. The CORDEX results served as an input for climate change impact and adaptation studies within the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).
Global and European average time series for monthly temperature
In the original source, the long-term annual and monthly mean HadCRU global temperatures were calculated from measurements from 4 349 stations for the entire period of the record. There is an irregular distribution in the time and space of available stations (i.e. denser coverage over the more populated parts of the world and increased coverage after 1950). Maps and tables giving the density of coverage through time are provided for land regions by Jones (2003). The gridding method used was the climate anomaly method (CAM), which means the station temperature data has been converted to the anomalies according to the WMO standards (baseline period 1961-1990 and at least 15 years of station data in the period) and grid-box values have been produced by simple averaging of the individual station anomaly values within each grid box.
GISS surface temperatures were calculated using around 7 200 stations from the Global Historical Climatology Network, United States Historical Climatology Network (USHCN) data, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. Additionally satellite sea surface temperature has been included for the post 1890 period. Temperatures were transformed into anomalies using station normalisation based on the 1951-1980 baseline period. Gridding has been done with the reference station method using a 1 200 km influence circle (Hansen et al. 2006).
Mean surface temperature anomalies from the Global Historical Climatology Network-Monthly (GHCN-M) have been produced at the NCDC from 2 592 gridded data points based on a 5° by 5° grid for the entire globe. The gridded anomalies were produced from GHCN-M bias corrected data. Gridded data for every month from January 1880 to the most recent month is available. The data are temperature anomalies in degrees Celsius (Jones, 2003).
Other global climate datasets are used by the climate research community, often with a specific purpose or audience in mind, for example processed satellite Earth-observations, and climate re-analyses. Although these are not specifically constructed for climate indicator monitoring, they do show the same temperature trends described here. Recently one new global temperature dataset has been developed especially for understanding temperature trends. This is the Berkeley Earth temperature record: http://berkeleyearth.org/
Daily climate information
Although Europe has a long history of collecting climate information, datasets containing daily climate information across the continent are scarce. Furthermore, accurate climate analysis requires long term time series without artificial breaks. The objective of the ECA project was to compile such a data set, consisting of homogeneous, long-term daily climate information. To ensure a uniform analysis method and data handling, data were centrally collected from about 200 meteorological stations in most countries of Europe and parts of the Middle East. Furthermore, the data were processed and analysed at one institute (i.e. KNMI) (Klok et al., 2008).
In order to ensure the quality of the ECA&D climate data set:
Global and European average time series for monthly temperature
Grid values of HadCRUT, GISTEMP and GHCN data sets have been gridded using different interpolation techniques. Each grid-box value for the HadCRUT dataset is the mean of all available station anomaly values, except that station outliers in excess of five standard deviations are omitted (Brohan et al., 2005). GISTEMP temperature anomaly data are gridded into 8 000 grid cells using the reference station interpolation method with a 1 200 km influence circle (Hansen et al. 2006). GHCN monthly data consists of 2 592 gridded data points produced on a 5° by 5° basis for the entire globe (Jones, 2003).
The observed increase in average air temperature, particularly during recent decades, is one of the clearest signals of global climate change.
Temperature has been measured over the centuries. There is a range of different methodologies that give similar results suggesting that uncertainty is relatively low. Three data sets have been presented here for the global temperature indicator. Global temperatures from HadCRUT, GISTEMP, and GHCN have been homogenised to minimise the effects of changing measurement methodologies and location.
Each observation station follows international standards for taking observations set out by the World Meteorological Organisation. Each National Meteorological Service provides reports on how its data are collected and processed to ensure consistency. This includes recording information about the local environment around the observation station and any changes to that environment. This is important for ensuring the required data accuracy and performing homogeneity tests and adjustments. There are additional uncertainties because temperatures over large areas of the Earth are not observed as a matter of routine. These elements are taken into account by factoring the uncertainty into global average temperature calculations, thereby producing a temperature range rather than one unique, definite figure (WMO, 2013). The uncertainty of temperature data has decreased over recent decades due to the wider use of agreed methodologies and denser monitoring networks. Uncertainty of the temperature data comes from sampling error, temperature bias effect and from the effect of limited observation coverage. Annual values of global and European temperatures are approximately accurate to +/- 0.05 °C (two standard errors) for the period since 1951. They were about four times as uncertain during the 1850s, with the accuracy improving gradually between 1860 and 1950 except for temporary deteriorations during data-sparse, wartime intervals. Estimating accuracy is difficult as the individual grid-boxes are not independent of each other and the accuracy of each grid-box time series varies through time (although the variance adjustment has reduced this influence to a large extent). The issue is discussed extensively by Jones et al. (2003), Brohan et al. (2005), and Hansen et al. (2006).
According to the IPCC's 4th Assessment Report (IPCC, 2007), there is very high confidence that the net effect of human activities since 1750 has been one of warming. Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations. Moreover, it is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the a combination of the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings. The best estimate of the human-induced contribution to warming is similar to the observed warming over this period (IPCC, 2013).
For references, please go to https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature-4/assessment or scan the QR code.
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