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Indicator Assessment
Global
Europe
Trends in annual temperature across Europe
Note: How to read the map: 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.)
Trends in warm days across Europe
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 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.
Trends in cool nights across Europe
Note: 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.
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.
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
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 (Morice et al. 2012); NOAA-NCEI (Smith et al. 2008); and NASA-GISS (Hansen et al. 2012), — show similar amounts of warming in 2005 to those in 2014. Relative to pre-industrial temperatures (using the earliest observations at the end of the 19th century as a proxy), records show increases of 0.82°C [0.76, 0.88], 0.80°C and 0.84°C, respectively (Fig. 1). This magnitude of warming corresponds to more than one third of the 2°C warming that is compatible with the global climate stabilisation target of the EU and the ultimate objective of UNFCCC. Global average temperature has warmed since 1850, but some comparatively short cooling periods have also occurred (Fig. 2). The warming rate was between 0.13 and 0.24°C per decade for all 20-year periods since 1986, which is close to the indicative limit of 0.2°C per decade proposed by some scientific studies (WBGU, 2003; van Vliet and Leemans, 2006).
Over the 2005-2014 decade, the rise in global average surface temperature has been between 0.08 and 0.12°C per decade, which is slower than in previous decades. This slow-down is due in roughly equal measure to a reduced trend in radiative forcing from natural factors (volcanic eruptions and solar activity) and to a cooling contribution from internal variability within the climate system (in particular increased heat uptake by the oceans). Increase in the heat content by the oceans is clearly observed in the upper 700m over the last 60 years, and unlike the surface air temperature does not show a slow-down. Recent observations show warming also of the deeper ocean between 700m and 2000m depth and below 3000m depth (IPCC, 2013). The study by Karl et al, 2015 based on newly available data which include additional measurements from Arctic showed that slow-down in global average temperature rise might be overestimated and the global temperature trends are actually higher than those reported by the IPCC (IPCC, 2013).
Global average surface temperature is also influenced significantly by the El Niño southern oscillation (ENSO) - a change in southern Pacific ocean temperature and wind direction. The warm peak in global surface temperatures in 1998 coincided with El Niño event. Over the recent decade the opposite phase - La Niña was mainly observed. La Niña has a cooling effect on global temperatures.
Projections
The global average temperature will continue to increase throughout this century as a result of projected further increases in greenhouse gas concentrations. The IPCC analysed climate projections (IPCC, 2013) have shown the central estimate for the warming by mid-century (2046–2065), to be between +1.0°C and +2.0°C, and finally by the end of the century (2081–2100), to be between +1.0°C and +3.7°C compared to 1986–2005. When model uncertainty is included, the likely range is from 0.3–1.7°C for the lowest scenario (RCP2.6) and 2.6–4.8°C for the highest scenario (RCP8.5) by the end of the century (2081–2100). The low-end RCP scenarios imply a reduction in emissions over this century to well below the levels of emissions seen in recent decades.
The EU 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 RCPs (Vautard et al., 2014). These projections show greatest warming over land (roughly twice the global average warming) and at high northern latitudes. These trends are consistent with the observations during the latter part of the 20th century (IPCC, 2013).
In addition to RCP-based climate projections for this century, several studies have projected climate change up to 2300 based on the so-called extended concentration pathways (ECPs). Simulations using the ECPs suggest central estimates for global mean temperature increase by 2300, relative to pre-industrial levels, of between 1.1°C for the extension of RCP2.6 to 8.0°C for the extension of RCP8.5 (Meinshausen et al, 2011).
Mean temperature
Past trends
2014 was the hottest year on record in Europe with the annual mean European land temperature nearly 0.17˚C above the previous record set in 2007 (EURO4M- CIB, 2015). The decadal average annual temperature over European land areas increased by approximately 1.5°C (± 0.1 °C) between pre-industrial times and the 2005-2014 decade (Fig. 3). The estimated uncertainties (grey interval) are due to errors introduced by spatial interpolation over areas without observation stations, inhomogeneities in measurements and biases due to urbanisation (van der Schrier et al., 2013).
The exceptional warmth of 2014 was mainly due to the very mild winter. The winter of 2014 (Fig. 4 middle) was warmer by approximately 2.6˚C compared with the pre-industrial period, while the average summer temperature (Fig. 4 lower) was approximately 1.3˚C higher than during the pre-industrial period.
Particularly large warming has been observed in the past 50 years over the Iberian Peninsula, across central and north-eastern Europe, and in mountainous regions (Fig. 5). According to the E-OBS data set (Haylock et al., 2008), warming was strongest over Scandinavia, especially in winter, whereas the Iberian Peninsula over the past 30 years warmed mostly in summer.
Projections
The average temperature over Europe is projected to continue increasing throughout this century. According to projections from the EURO-CORDEX study (Jacob et al, 2013), the increase in annual average European land temperature will be greater than the global average for land temperature. According to the multi-model ensemble mean, the annual temperature for Europe is projected to increase by around 2.4°C for the RCP4.5 scenario and 4.1°C for RCP8.5 (between 2071–2100 and 1971–2000) (Fig. 6). The warming is projected to be greatest in north-eastern Europe and Scandinavia in winter and over southern Europe in summer.
Temperature extremes
Past trends
Analysis based on observational data shows a continued increase in hot extremes over land (Seneviratne et al., 2014). The number of warm days and nights, as well as heat waves, have become more frequent, while cool days and nights, cold spells, and frost days, have become less frequent (IPCC, 2013; IPCC, 2014).
During the last decade, 500-year-old records in heat waves were broken over 65% of Europe (Barriopedro et al, 2011). Since 1960, significant increases in the number of warm days (Fig. 7), and decreases in the number of cool nights have been observed throughout Europe (Fig. 8).
The number of warm days increased by up to 10 days per decade between 1960 and 2014 in southern Europe and by up to 8 days per decade in Scandinavia (Fig. 7). Over the same time period, the number of cool nights in Europe decreased by between 2 and 9 days per decade. The Iberian Peninsula, land areas to the south and east of the Mediterranean, north-western Europe and Scandinavia have shown the largest decreases in cool nights with decreases of around 6 days per decade between 1960 and 2014 (Fig. 8).
The historic records show clear long-term warming trends across Europe, but it is normal to observe considerable variability between and within years and regions. For example, in 2014 Scandinavia and eastern Europe experienced an above average number of warm days, with eastern Europe having the highest increase in the region, but northern Italy and the Balkans show a below average number of warm days during the summer 2014 (EURO4M – CIB, 2015).
Projections
Extreme high temperatures (commonly called heat waves) are projected to become more frequent and last longer across Europe during this century (Fischer and Schär 2010, IPCC 2013, Russo et al. 2014). These changes are consistent both with observed warming trends over recent decades and with projections of future average warming. Since the 1970s, Europe has experienced extreme heatwaves (1976, 2003, 2006, 2007, and 2010) and all regions show increasing occurrences of high temperatures (IPCC, 2012 and 2014). However, these rare heat waves will become more common in the second half of the 21st century (Fig. 9, Russo et al. 2014). Under the high forcing scenario RCP8.5, southern and south-eastern Europe are projected to experience more than three heat waves in the 2020-2052 period, while between 2068 and 2100, the majority of Europe will experience more than 15 extreme heatwaves (Russo et al, 2014).
In terms of health impacts, the heat wave projections are most severe for low-altitude river basins in southern Europe and for the Mediterranean coasts, affecting many densely populated urban centres (Fischer and Schär, 2010).
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 or even centuries at some locations and a dense network of stations across the globe, and especially in Europe, provide regular monitoring of temperature, using standardised measurements, quality control and homogeneity procedures.
This indicator provides guidance for the following policy-relevant questions:
Temperature changes also influence other aspects of the climate system which can impact on human activities, such as sea level rise, intensity and frequency of floods and droughts, biota and crop productivity and vector-borne diseases. In consideration with the target of limiting the rise in global average temperature, understanding seasonal variations and spatial distributions of temperature change are important, for example to manage the risks that current climate poses to human and natural systems and to assess how these may be impacted by future climate change.
Units are degrees Celsius (°C), degrees Celsius per decade (°C/decade), and number of warm and cool days per year
Baseline period
Global average annual temperature is expressed here relative to a ‘pre-industrial’ baseline period of 1850 to 1899, and this period coincides with the beginning of widespread instrumental temperature records. Reconstructions of global or regional temperatures before the instrumental period are based on proxy data and contain a measure of uncertainty. Other studies sometimes use a different climatological baseline period, such as the 1961-1990 and 1986-2005 periods used in parts of the IPCC Working Group One contribution to the Fifth Assessment Report (IPCC, 2013).
This indicator provides information on the absolute change and rate of change in global average temperature which both important indicators of the severity of global climate change. Indicators describes also changes in annual, winter and summer European land temperatures and changes in temperature extremes - like changes in heatwave frequencies in Europe.
To avoid serious climate change impacts, the European Council proposed in its Sixth Environmental Action Programme (6EAP), 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) , that the global average temperature increase should be limited to not more than 2 0 C above pre-industrial levels. Furthermore the UNFCCC 15th conference of the parties (COP15) recognised in the Copenhagen Accord (UNFCCC, 2009) the scientific evidence for the need to keep global average temperature increase below 2 0C above pre-industrial levels. In addition, some studies have proposed a 'sustainable' target of limiting the rate of anthropogenic warming to 0.1 to 0.2 0 C per decade.
The target for absolute temperature change (i.e. 2 0C) was initially derived from the variation of global mean temperature during the Holocene, which is the period since the last ice age during which human civilization has developed. Further studies (IPCC, 2007;Vautard, 2014) have pointed out that even a global temperature change of below the 2 0C target would still result in considerable impacts. Vulnerable regions across the world, in particular in developing countries (including least developed countries, small developing island states and Africa), would be most strongly affected. The UNFCCC Copenhagen Accord (2009) therefore foresees a review in 2015 of the scientific evidence for revising the global temperature target to 1.5°C.
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, its 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:
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 4349 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/tables giving the density of coverage through time are given for land regions by Jones (2003). The gridding method was climate anomaly method (CAM), which means the station temperature data have 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 7200 stations from Global Historical Climatology Network, United States Historical Climatology Network (USHCN) data, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. Additionally satellite SST has been included for the period after 1980. Temperatures were transformed into anomalies using station normalisation based on the 1951 to 1980 baseline period. Gridding has been done with reference station method using 1200 km influence circle (Hansen et al. 2006).
Surface temperature mean anomalies from Global Historical Climatology Network-Monthly (GHCN-M) has been produced at the NCDC from 2,592 gridded data points based on a 5° by 5° grids 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 reanalyses. 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 in 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 8000 grid cells using reference station interpolation method with 1200 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 which 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 homogenized to minimise the effects of changing measurement methodologies and location.
Each observation station follows international standards for taking observations set out by WMO. 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 uniquely definite figure (WMO, 2013). The uncertainty of temperature data has decreased over recent decades due to 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 the limited observation coverage. Annual values of global and European temperature are approximately accurate to +/- 0.05 degrees C (two standard errors) for the period since 1951. They are 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 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 GHG 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 anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together. 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-1/assessment or scan the QR code.
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