Global and European temperature

Indicator Assessment
Prod-ID: IND-4-en
Also known as: CSI 012 , CLIM 001
Created 11 May 2016 Published 01 Aug 2016 Last modified 01 Aug 2016, 09:54 AM
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According to three different observational records of the annual global average near-surface (land and ocean) temperature, the decade from 2006 to 2015 was 0.83 °C to 0.89 °C warmer than the pre-industrial average. This makes it the warmest decade on record. 15 of the 16 warmest years on record have occurred since 2000, and 2015 was the warmest year on record - around 1 °C warmer than the pre-industrial period. Over the decade 2006-2015, the rate of change in global average surface temperature was between 0.10 and 0.24 °C per decade. This is close to the indicative limits of 0.2 °C/decade. The average annual temperature of the European land area, for the decade from 2006–2015, was around 1.5 °C above the pre-industrial level. This makes it the warmest decade on record. Moreover, 2014 and 2015 were the joint warmest years in Europe since instrumental records began. Climate models project further increases in global average temperature over the 21 st century. For the period 2081-2100 (relative to 1986-2005), increases of between 0.3 °C and 1.7 °C for the lowest emissions scenario (RCP2.6 (Representative Concentration pathway)), and between 2.6 °C and 4.8 °C for the highest emissions scenario (RCP8.5) are estimated. The EU and UNFCCC target of limiting global average temperature increase to less than 2 °C above pre-industrial levels is projected to be exceeded between 2042 and 2050 by the three highest of the four RCPs. By the end of this century (2071-2100 relative to 1971-2000), annual average land temperature over Europe is projected to increase in the range of 1 °C to 4.5 °C under RCP4.5, and 2.5 °C to 5.5 °C under RCP8.5. This is more than the global average. The strongest warming is projected over northeastern Europe and Scandinavia in winter and southern Europe in summer. The number of warm days (those exceeding the 90 th percentile threshold of a baseline period) have almost doubled since 1960 across the European land area. Europe has experienced several extreme heatwaves since the year 2000 (2003, 2006, 2007, 2010, 2014 and 2015). Under a high emissions scenario (RCP8.5), very extreme heat waves as strong as those or even stronger are projected to occur at least every three years in the second half of the 21 st century.

Key messages

  • According to three different observational records of the annual global average near-surface (land and ocean) temperature, the decade from 2006 to 2015 was 0.83 °C to 0.89 °C warmer than the pre-industrial average. This makes it the warmest decade on record. 15 of the 16 warmest years on record have occurred since 2000, and 2015 was the warmest year on record - around 1 °C warmer than the pre-industrial period.
  • Over the decade 2006-2015, the rate of change in global average surface temperature was between 0.10 and 0.24 °C per decade. This is close to the indicative limits of 0.2 °C/decade.
  • The average annual temperature of the European land area, for the decade from 2006–2015, was around 1.5 °C above the pre-industrial level. This makes it the warmest decade on record. Moreover, 2014 and 2015 were the joint warmest years in Europe since instrumental records began.
  • Climate models project further increases in global average temperature over the 21st century. For the period 2081-2100 (relative to 1986-2005), increases of between 0.3 °C and 1.7 °C for the lowest emissions scenario (RCP2.6 (Representative Concentration pathway)), and between 2.6 °C and 4.8 °C for the highest emissions scenario (RCP8.5) are estimated.
  • The EU and UNFCCC target of limiting global average temperature increase to less than 2 °C above pre-industrial levels is projected to be exceeded between 2042 and 2050 by the three highest of the four RCPs.
  • By the end of this century (2071-2100 relative to 1971-2000), annual average land temperature over Europe is projected to increase in the range of 1 °C to 4.5 °C under RCP4.5, and 2.5 °C to 5.5 °C under RCP8.5. This is more than the global average. The strongest warming is projected over northeastern Europe and Scandinavia in winter and southern Europe in summer.
  • The number of warm days (those exceeding the 90th percentile threshold of a baseline period) have almost doubled since 1960 across the European land area.
  • Europe has experienced several extreme heatwaves since the year 2000 (2003, 2006, 2007, 2010, 2014 and 2015). Under a high emissions scenario (RCP8.5), very extreme heat waves as strong as those or even stronger are projected to occur at least every three years in the second half of the 21st century.

Will the increase in global average temperature stay below the EU policy target of not more than 2°C above pre-industrial levels, and will the rate of increase in global average temperature stay below the proposed target of not more than 0.2°C per decade?

Global average near surface temperatures relative to the pre-industrial period

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Rate of change of global average temperature

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Past trends

Records of global average temperature show long-term warming trends since the end of the 19th century. These have been most rapid since the 1970s. Three different analyses of global average temperature, using near-surface observation records — HadCRUT4 (Morice et al., 2012), NOAAGlobalTemp (Karl et al., 2015) and NASA-GISS (Hansen et al., 2010) — show similar amounts of warming. Using the earliest observations from the period 1850-1900 as a proxy, they show warming compared to pre-industrial temperatures of between 0.83 oC and 0.89 oC for the decade 2006-2015. This magnitude of warming corresponds to almost one 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 (UNFCCC, 2009).

Global average temperature has increased since 1850 and the most recent decade has been the warmest (Figure 1). Furthermore, 2015 was the warmest year on record, according to different near-surface temperature observational analyses, with anomalies of around 1 oC compared to pre-industrial times (WMO, 2016). However, such a statement is always associated with some uncertainty, primarily due to spatial and temporal gaps in the data record and different interpolation methods (Blunden and Arndt, 2015).

Between 2006 and 2015, the rise in global average surface temperature was between 0.10 °C and 0.24 °C per decade (Figure 2). This is close to the indicative limit of 0.2°C proposed by some scientific studies (see e.g. van Vliet and Leemans, 2006). However, the rate of change is strongly influenced by climate variability due to natural forcings (volcanic eruptions and solar activity) and due to internal variability within the climate system (e.g. multi-annual climate fluctuations, such as the El Niño Southern Oscillation (ENSO), which influence the rate of heat uptake by the oceans) (IPCC, 2013). Global ocean heat content up to a depth of at least 2 000 m, has been increasing continuously over the last 60 years, without any slow-down. Furthermore, a recent study that uses new Sea Surface Temperature (SST) datasets 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 IPCC AR5 reports (Karl et al., 2015, Fyfe et al., 2016). Changing the start and end year also has an effect on the rate of change, although less than the use of newly available data and interpolation methods do (Karl et al., 2015).

Projection

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 report, project continuous warming. By mid-century (2046–2065 relative to 1986–2005), these models project increases of 0.4 °C - 1.6 °C for RCP2.6, 0.9 °C – 2.0 °C for RCP4.5, 0.8 °C– 1.8  °C for RCP6.0 and 1.4 °C - 2.6 °C for RCP8.5. By the end of the century (2081-2100), the same models predict warming of 0.3 °C - 1.7 °C for RCP2.6, 1.1 °C – 2.6 °C for RCP4.5, 1.4 °C – 3.1 °C for RCP6.0 and 2.6 °C - 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 warming. These patterns are consistent with observations during the latter part of the 20th century (Collins, et al., 2013).

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 RCPs (Vautard, et al., 2014). The lowest, RCP2.6, implies strong reduction in greenhouse gas emissions over this century and negligible or even negative emissions at the end of the century (Moss, et al., 2010).

Several studies have projected climate change beyond 2100 based on the so-called extended concentration pathways (ECPs). The central estimates for global mean temperature increase by 2200, relative to pre-industrial levels, are between 1.3 °C for ECP2.6 and 7.1 °C for ECP8.5 (Meinshausen, et al., 2011; Collins, et al., 2013).

What is the trend and rate of change in the European annual and seasonal temperature?

European average temperatures over land areas relative to the pre-industrial period

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European seasonal temperature anomalies over land areas

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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 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.

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Observed trends in warm days across Europe between 1960 and 2015

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

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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

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Mean temperature

Past trends
Between 2006 and 2015, the average annual temperature over the European land area increased by 1.45 °C to 1.59 °C, relative to the pre-industrial period. This increase is larger than that for global average temperature and makes it the warmest decade on record (Figure 3). The grey interval shows uncertainties (between 2.5 and 97.5 percentiles ) introduced by spatial interpolation over areas without observation stations, non-uniform measurements and biases due to urbanisation (van der Schrier, et al., 2013). The inter-annual temperature variability over Europe is generally much higher in winter than in summer (Figure 4). The relatively rapid warming trend since the 1980s is most clearly evident in the summer.

The years 2014 and 2015 were the joint warmest calendar years in Europe since instrumental records began. Anthropogenic climate change made these temperature records 35–80 times more likely (Kam, et al., 2015; EURO4M 2015). Moreover, climate reconstructions show that summer temperatures in Europe in recent decades are the warmest in at least 2000 years and that they lie significantly outside the range of natural variability (Luterbacher, et al., 2016).

Since 1960, particularly strong and significant warming has been observed over the Iberian Peninsula, across central and northeastern Europe, and in mountainous regions (Figure 5). Based on the E-OBS data set (Haylock, et al., 2008), warming since the 1960s has been strongest and most significant over Scandinavia, especially in winter, whereas the Iberian Peninsula has significantly warmed mostly in summer (IPCC, 2014).

Projections

Temperatures across Europe are projected to continue to increase throughout this century. Projections from the EURO-CORDEX initiative suggest that the European land area will warm faster on average than global land areas (Jacob, et al., 2014). According to the multi-model ensemble mean, the European land area is projected to warm in the range of 1 °C to 4.5 °C for the RCP4.5 scenario and in the range of 2.5 °C to 5.5 °C for RCP8.5 over the 21st century (between 2071–2100 and 1971–2000) (Figure 6). The strongest warming is projected over northeastern Europe and Scandinavia in winter and over southern Europe in summer.


Heat extremes

Past trends

Observational data show a continued increase in hot extremes over land in the last decade (Seneviratne, et al., 2014). Globally, the number of warm days and nights, and 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 (IPCC 2013). In Europe, since the 1950s, large areas have experienced long and intense heatwaves with notable impacts on human health and socio-economic systems (García-Herrera, et al., 2010; Russo, et al., 2015). As a result, 500-year-old temperature records were broken over 65 % of Europe in the period 2003–2010 alone (Barriopedro, et al., 2011).

Indices for extreme temperatures, including the annual maximum of daily maximum temperatures (Txx) show a significant upward trends across Europe since the 1950s (Donat, et al., 2013). The number of unusually warm days (Tx90p) has increased by up to 10 days per decade since 1960 in most of southern Europe and Scandinavia (Figure 7). Based on the daily Heat Wave Magnitude Index (HWMId), Europe has experienced 11 long and intense heat waves between 1950 and 2015, most of which occurred after 2000 (in 2003, 2006, 2007, 2010, 2014, and 2015) (Russo, et al., 2015). The most severe heat waves have been characterised by the persistence of extremely high night-time temperatures (Russo, et al., 2015).

The observed changes in summer hot extremes in Europe have been governed by large-scale thermodynamic changes, as well as recent changes in the frequency, persistence and maximum duration of regional circulation patterns (Horton, et al., 2015). A substantial fraction of the probability of recent extreme events may be attributed to human-induced climate change, and it is likely that for temperature extremes occurring over previous decades, a fraction of their probability could also be attributed to anthropogenic influences (King, et al., 2016).


Projections

Periods with extreme high temperatures are projected to become more frequent and to last longer across Europe during this century (Fischer, et al., 2010; Russo, et al., 2014; Schoetter, et al., 2014). 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 future. Under the high RCP8.5 scenario, very extreme heat waves (which are much stronger than either the 2003 or the 2010 heatwave), are projected to occur as often as every two years in the second half of the 21st century (Figure 8). The projected frequency of heatwaves is strongest in southern and south-eastern Europe (Russo, et al., 2014). 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 (Fischer, et al., 2010).

 

 

Scientific references

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Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A. J. and Wehner, M., 2013, 'Long-term Climate Change: Projections, Commitments and Irreversibility', in: Stocker, T. F., Qin, D., Plattner, G.-K., et al. (eds),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 University Press, Cambridge, United Kingdom and New York, NY, USA, chapter 12.

Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R. and Caesar, J., 2013, 'Global Land-Based Datasets for Monitoring Climatic Extremes',Bulletin of the American Meteorological Society94(7), 997–1006 (DOI: 10.1175/BAMS-D-12-00109.1).

EURO4M, 2015,2014 warmest year on record in Europe, Climate Indicator Bulletin (19 January 2015), EURO4M.

Fischer, E. and Schär, C., 2010, 'Consistent geographical patterns of changes in high-impact European heatwaves',Nature Geoscience3, 398–403 (DOI: 10.1038/ngeo866).

Fyfe, J. C., Meehl, G. A., England, M. H., Mann, M. E., Santer, B. D., Flato, G. M., Hawkins, E., Gillett, N. P., Xie, S.-P., Kosaka, Y. and Swart, N. C., 2016, 'Making sense of the early-2000s warming slowdown',Nature Climate Change6(3), 224–228 (DOI: 10.1038/nclimate2938).

García-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J. and Fischer, E. M., 2010, 'A Review of the European Summer Heat Wave of 2003',Critical Reviews in Environmental Science and Technology40(4), 267–306 (DOI: 10.1080/10643380802238137).

Hansen, J., Ruedy, R. and Sato, M., 2010, 'Global surface temperature change',Reviews of Geophysics48(RG4004), 1–29 (DOI: 10.1029/2010RG000345).

Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D. and New, M., 2008, 'A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006',Journal of Geophysical Research113(D20), D20119 (DOI: 10.1029/2008JD010201).

Horton, D. E., Johnson, N. C., Singh, D., Swain, D. L., Rajaratnam, B. and Diffenbaugh, N. S., 2015, 'Contribution of changes in atmospheric circulation patterns to extreme temperature trends',Nature522(7557), 465–469 (DOI: 10.1038/nature14550).

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, Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S. et al., 2014, 'EURO-CORDEX: new high-resolution climate change projections for European impact research',Regional Environmental Change14(2), 563–578 (DOI: 10.1007/s10113-013-0499-2).

Kam, J., Knutson, T. R., Zeng, F. and Wittenberg, A. T., 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 Society96(12), S61–S65 (DOI: 10.1175/BAMS-EEE_2014_ch13.1).

Karl, T. R., Arguez, A., Huang, B., Lawrimore, J. H., McMahon, J. R., Menne, M. J., Peterson, T. C., Vose, R. S. and Zhang, H.-M., 2015, 'Possible artifacts of data biases in the recent global surface warming hiatus',Science348(6242), 1469–1472 (DOI: 10.1126/science.aaa5632).

Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C. and Senior, C. A., 2014, 'Heavier summer downpours with climate change revealed by weather forecast resolution model',Nature Climate Change4(7), 570–576 (DOI: 10.1038/nclimate2258).

King, A. D., Black, M. T., Min, S.-K., Fischer, E. M., Mitchell, D. M., Harrington, L. J. and Perkins-Kirkpatrick, S. E., 2016, 'Emergence of heat extremes attributable to anthropogenic influences',Geophysical Research Letters43(7), 2015GL067448 (DOI: 10.1002/2015GL067448).

Luterbacher, J., Werner, J. P., Smerdon, J. E., Fernández-Donado, L., González-Rouco, F. J., Barriopedro, D., Ljungqvist, F. C., Büntgen, U., Zorita, E., Wagner, S., Esper, J., McCarroll, D., Toreti, A., Frank, D., Jungclaus, J. H., M Barriendos, Bertolin, C., Bothe, O., Brázdil, R. et al., 2016, 'European summer temperatures since Roman times',Environmental Research Letters11(2), 024001 (DOI: 10.1088/1748-9326/11/2/024001).

Meinshausen, M., Smith, S., Calvin, K., Daniel, J., Kainuma, M., Lamarque, J.-F., Matsumoto, K., Montzka, S., Raper, S., Riahi, K., Thomson, A., Velders, G. and van Vuuren, D. P., 2011, 'The RCP greenhouse gas concentrations and their extensions from 1765 to 2300',Climatic Change109(1), 213–241 (DOI: 10.1007/s10584-011-0156-z).

Morice, C. P., Kennedy, J. J., Rayner, N. A. and Jones, P. D., 2012, 'Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set',Journal of Geophysical Research117(D8) (DOI: 10.1029/2011JD017187).

Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P. and Wilbanks, T. J., 2010, 'The next generation of scenarios for climate change research and assessment',Nature463(7282), 747–756 (DOI: 10.1038/nature08823).

Mueller, B. and Seneviratne, S. I., 2012, 'Hot days induced by precipitation deficits at the global scale',Proceedings of the National Academy of Sciences109(31), 12398–12403 (DOI: 10.1073/pnas.1204330109).

Russo, S., Dosio, A., Graversen, R. G., Sillmann, J., Carrao, H., Dunbar, M. B., Singleton, A., Montagna, P., Barbola, P. and Vogt, J. V., 2014, 'Magnitude of extreme heat waves in present climate and their projection in a warming world',Journal of Geophysical Research: Atmospheres119(22), 12,500–12,512 (DOI: 10.1002/2014JD022098).

Russo, S., Sillmann, J. and Fischer, E. M., 2015, 'Top ten European heatwaves since 1950 and their occurrence in the coming decades',Environmental Research Letters10(12), 124003 (DOI: 10.1088/1748-9326/10/12/124003).

Schoetter, R., Cattiaux, J. and Douville, H., 2014, 'Changes of western European heat wave characteristics projected by the CMIP5 ensemble',Climate Dynamics45(5-6), 1601–1616 (DOI: 10.1007/s00382-014-2434-8).

van der Schrier, G., van den Besselaar, E. J. M., Klein Tank, A. M. G. and Verver, G., 2013, 'Monitoring European average temperature based on the E-OBS gridded data set',Journal of Geophysical Research: Atmospheres118(11), 5120–5135 (DOI: 10.1002/jgrd.50444).

Seneviratne, S. I., Donat, M. G., Mueller, B. and Alexander, L. V., 2014, 'No pause in the increase of hot temperature extremes',Nature Climate Change4(3), 161–163 (DOI: 10.1038/nclimate2145).

Stott, P. A., Jones, G. S., Christidis, N., Zwiers, F. W., Hegerl, G. and Shiogama, H., 2011, 'Single-step attribution of increasing frequencies of very warm regional temperatures to human influence',Atmospheric Science Letters12(2), 220–227 (DOI: 10.1002/asl.315).

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Indicator specification and metadata

Indicator definition

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.

This indicator provides guidance for the following policy-relevant questions:

  •  Will the global average temperature increase stay within the UNFCCC policy target of 2 °C above pre-industrial levels?
  •  Will the rate of global average temperature increase stay below the indicative proposed target of 0.2 °C increase per decade?

 

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 a relatively small influence on the climate compared to 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.

Units

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 (GHGs) from pre-1850 industrial activity had a relatively small influence on climate compared to 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).

 


Policy context and targets

Context description

This indicator provides guidance for the following policy-relevant questions:

  • Can global average temperature increase stay below the EU and UNFCCC policy target of 2 °C above pre-industrial levels?
  • Can the rate of global average temperature increase stay within the proposed indicative target of 0.2 °C increase per decade?

 

The absolute change and rate of change in global average temperature are both important indicators of the severity of global climate change. Temperature changes also influence other components of the climate system - including the hydrosphere, containing oceans, and the cryosphere - that can have an impact on human activities.

Targets

To avoid serious climate change impacts, 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) - 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 °C - 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”.

Related policy documents

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  • EU Adaptation Strategy Package
    In April 2013 the European Commission adopted an EU strategy on adaptation to climate change which has been welcomed by the EU Member States. The strategy aims to make Europe more climate-resilient. By taking a coherent approach and providing for improved coordination, it will enhance the preparedness and capacity of all governance levels to respond to the impacts of climate change.
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Methodology

Methodology for indicator calculation

Various data sets on trends in global and European temperature have been used for this indicator:

  • Global average seasonal and annual temperature: Three datasets are used. 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.
  • 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 oC global temperature increase above pre-industrial levels. The indicator has been calculated as a combination of land and sea temperature.
  • The GHCN surface temperature is a product of the National Climate Data Centre (NCDC) 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.
  • European average annual and monthly temperature:  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.
  • 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. .
  • Observed changes in warm spells ; changes in the duration of warm spells in summer (days per decade) and frequency of frost days in winter (days per decade). Warm spells are defined as a period of at least six consecutive days where the mean daily temperature exceeds the baseline temperature (average daily temperature during the 1961-1990 period) by 5 °C. Frost days are defined as a day with an average temperature below 0 °C. Positive values indicate an increase in frequency and negative values a decrease in frequency. Data source: http://eca.knmi.nl/ensembles
  • Observed number in warm days. Number of warm days are number of days when Tmax is above 90th percentile for each month.
  • 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).
  • The HWMI is defined as the maximum magnitude of the heat waves in a year, where a heat wave is the period >= 3 consecutive days with maximum temperature above the daily threshold for the reference period 1981 -2010. This threshold is the 90th percentile of the daily maximum centred on a 31 day window (Russo et al. 2014). RCP 8.5 scenario has been used.

Methodology for gap filling

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 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 is 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:

  • Statistical homogeneity tests have been applied to detect breaks in the time series;
  • the meta-information accompanying the data has been intensively analysed, e.g. to check whether observed trends were not triggered by, for example, movements of stations;
  • the final data set has been compared with other data sets, such as the aforementioned data set of the Climatic Research Unit; and
  • findings of the different exercises have been discussed during workshops with country representatives.


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).

Methodology references

  • European Climate Assessment & Dataset project ECA&D  ECA&D is initiated by the European Climate Support Network ECSN , and is supported by the Network of European Meteorological Services EUMETNET .
  • KNMI Climate Explorer This web site gives the opportunity to explore monthly mean climate time series and the relationships between them.
  • Updated and extended European dataset of daily climate observations Klok, E.J. and A.M.G. Klein Tank: Updated and extended European dataset of daily climate observations. Int. J. Climatol (2008)
  • Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850 P. Brohan, J. J. Kennedy, I. Harris, S. F. B. Tett & P. D. Jones. Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J. Geophys. Res., 111, D12106 (2005)
  • Global temperature change Hansen, J., Mki. Sato, R. Ruedy, K. Lo, D.W. Lea, and M. Medina-Elizade: Global temperature change. Proc. Natl. Acad. Sci., 103, 14288-14293, doi:10.1073/pnas.0606291103 (2006)
  • WMO statement on the status of the global climate in 2012 WMO statement on the status of the global climate in 2012. WMO, 2012 WMO statement on the status of the global climate in 2011; World Meteorological Organization, Geneva, Switzerland.
  • The Copenhagen Accord (2009) United Nations Framework convention on Climate Change. UNFCCC
  • The 7th Environment Action Programme (7th EAP). 2014 The 7th Environment Action Programme (7th EAP). 2014. European Union
  • IPCC (2007) Climate Change 2007 The Physical Science Basis. IPCC (2007) Climate Change 2007: The Physical Science Basis. eds. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor MMB & Miller HL),. Working Group 1 Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Chapters 3 (Observations: Surface and Atmospheric Climate Change), 10 (Global Climate Projections),11 (Regional Climate Projections)
  • IPCC, 2013: Summary for Policymakers 2013 IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

Uncertainties

Methodology uncertainty

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.

Data sets uncertainty

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 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).

Rationale uncertainty

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).

Data sources

Generic metadata

Topics:

Climate change Climate change (Primary topic)

Tags:
heat waves | climate | temperature | surface temperature | climate change | air temperature | global warming | scenarios | temperatures | anomalies | key climate variables
DPSIR: State
Typology: Performance indicator (Type B - Does it matter?)
Indicator codes
  • CSI 012
  • CLIM 001
Dynamic
Temporal coverage:
1850-2015, 2020-2052, 2068-2100
Geographic coverage:
Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Earth, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kosovo, Kosovo (UNSCR 1244/99), Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia, Malta, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, The Former Yugoslav Republic of Macedonia, Turkey, United Kingdom

Dates

Frequency of updates

Updates are scheduled once per year
European Environment Agency (EEA)
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