Assessment versions
Published (reviewed and quality assured)
Rationale
Justification for indicator selection
Surface air temperature gives one of the clearest signals of global and regional climate change, and it has been measured for many decades or even centuries at some locations. For this reason it has been chosen as the indicator to monitor the “ultimate objective of the United Nations Framework Convention on Climate Change.
Anthropogenic influence, mainly emissions of greenhouse gases, is responsible for most of the observed increase in global average temperature in recent decades (IPCC, 2013). Natural factors, such as volcano eruptions and variations in solar activity, contribute to variations in global average temperature but they cannot explain the substantial warming during the past 50 years.
The World Meteorological Organisation defines a climate normal period as 30 years. This definition reflects the substantial climate variability on shorter time scales due to natural factors (e.g. changes in system components like the El Niño Southern Oscillation, volcanic eruptions and the solar cycle). When interpreting the time series of global mean temperature change, it is important to note that the observed record shows the combination of the long-term climate change signal and substantial year to year variability. An apparent trend in the temperature record over a few consecutive years is therefore not necessarily indicative of the long term temperature trend, which requires observations over several decades.
Global average temperature changes and the rate of change are both important determinants of the magnitude of possible effects of climate change. Furthermore, trends and projections of the annual global average temperature are easy to understand and can be related to a global target.
Understanding the spatial and seasonal distribution of climate change is important for assessing the potential impacts of climate change and associated adaptation needs. For example, temperature in Europe exhibits large differences from west (maritime) to east (continental), and from south (Mediterranean) to north (Arctic).
Scientific references
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ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project
van der Linden P., and J.F.B. Mitchell (eds.): ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK. 160pp (2009).
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European daily high-resolution gridded dataset of surface temperature and precipitation
M. R. Haylock, N. Hofstra, A. M. G. Klein Tank, E. J. Klok,P. D. Jones, and M. New. (2008). European daily high-resolution gridded dataset of surface temperature and precipitation.Journal of Geophysical Research, vol. 113, d20119, doi:10.1029/2008JD010201
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HadCRUT3: Met Office Hadley Centre temperature observations datasets
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GISTEMP: GISS (Goddard Institute for Space Studies) surface temperature datasets
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NOAA - Global Historical Climatology Network (GHCN-M) version 3 Global surface temperature datasets
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Global surface temperature dataset from NCDC
Jones, P. D., and A. Moberg, 2003: Hemispheric and Largescale Surface Air Temperature Variations: An extensive Revision and an Update to 2001, J. Climate, 16, 206–223.
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Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006)
Smith, T.M., R.W. Reynolds, Thomas C. Peterson, and Jay Lawrimore 2008: Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006). Journal of Climate, 21, 2283-2293.
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WBGU, 2003
WBGU, 2003. climate Prediction strategies for the 21st Century: Kyoto and Beyond. Berlin
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Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 dataset
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 dataset. Journal of Geophysical Research, 117, D08101, doi:10.1029/2011JD017187
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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 [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, 1535 pp.
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The European climate under a 2°C global warming
Vautard, R., Gobiet, A., Sobolowski, S., Kjellström, E., Stegehuis, A., Watkiss, P., Mendlik, T., Landgren, O, Nikulin, G.,Teichmann, C., Jacob, D. (2014). The European climate under a 2°C global warming.
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Global surface temperature dataset from NCDC
Jones, P. D., and A. Moberg, 2003: Hemispheric and Largescale Surface Air Temperature Variations: An extensive Revision and an Update to 2001, J. Climate, 16, 206–223.
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GISS analysis of surface temperature change
J. Hansen, R. Ruedy, J. Glascoe, M. Sato, 2012. GISS analysis of surface temperature change. Journal of Geophysical Research: Atmospheres, 104, 2156-2202, Wiley
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The next generation of scenarios for climate change research and assessment
Moss, R. H. et al. (2010) Nature 463, 747-756.
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The RCP greenhouse gas concentrations and their extensions from 1765 to 2300
M. Meinshausen et al. 2011. Climatic Change (2011) 109:213-241
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Consistent geographical patterns of changes in high-impact European heatwaves
E. M. Fischer and C. Schär. 2010. Nature Geoscience 3, 398 – 403.
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EURO-CORDEX: new high-resolution climate change projections for European impact research
D. Jacob et al. 2014. Regional Environmental Change, Volume 14, Issue 2, pp 563-578.
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The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe
D Barriopedro et al. 2011. Science Vol. 332 no. 6026 pp. 220-224.
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IPCC 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation.
A Special Report of Working Groups I and II of the ,Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker,D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK.
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IPCC 2014: WG II AR5
IPCC 2014: WG II AR5. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Chapter 23: Europe.
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Rapid Species’ Responses to Changes in Climate Require Stringent Climate Protection Targets
van Vliet, A. and Leemans, R. (2006). Rapid Species’ Responses to Changes in Climate Require Stringent Climate Protection Targets. In Schellnhuber, H.J. (Ed. in Chief) Avoiding Dangerous Climate Change. Cambridge University Press, Cambridge, pp135-141.
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KNMI- CIB
KNMI-CIB 2015, Climate Indicators Bulletin, last accessed 03-06-2015
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Magnitude of extreme heat waves in present climate and their projection in a warming world
Russo, S. et al. Magnitude of extreme heat waves in present climate and their projection in a warming world. J. Geophys. Res. Atmospheres119, 12,500–12,512 (2014).
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Possible artifacts of data biases in the recent global surface warming hiatus
Thomas R. Karl, Anthony Arguez, Boyin Huang1,Jay H. Lawrimore,James R. McMahon2,Matthew J. Menne,Thomas C. Peterson, Russell S. Vose, Huai-Min Zhang, Science 26 June 2015 Vol. 348 no. 6242 pp. 1469-1472 , DOI: 10.1126/science.aaa5632
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No pause in the increase of hot temperature extremes
Sonia I. Seneviratne, Markus G. Donat, Brigitte Mueller,Lisa V. Alexander. Nature Climate Change, 4,161–163 (2014) doi:10.1038/nclimate2145
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 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:
- Will the global average temperature increase stay within the UNFCCC policy target of 2.0°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?
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
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).
Policy context and targets
Context description
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.
Targets
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”.
Related policy documents
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DG CLIMA: Adaptation to climate change
Adaptation means anticipating the adverse effects of climate change and taking appropriate action to prevent or minimise the damage they can cause, or taking advantage of opportunities that may arise. It has been shown that well planned, early adaptation action saves money and lives in the future. This web portal provides information on all adaptation activities of the European Commission.
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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 enhances the preparedness and capacity of all governance levels to respond to the impacts of climate change.
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European Commission related policy documents
European Commission related policy documents
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UNFCCC and related policy documents
UNFCCC and related policy documents
Key policy question
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?
Specific policy question
What is the trend and rate of change in the European annual and seasonal temperature?
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: 3 datasets are used. HadCRUT4 is collaborative products 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 to 1899 in order to better monitor the EU objective not to exceed 2 oC above pre-industrial values.
- The GISS surface temperature is a product of the Goddard Institute for Space Studies under NASA. The original source anomalies are calculated in the relation to the 1951 to 1980 base period. Annual deviations shown on the chart are adjusted to the 1880 to 1899 period to better monitor the EU objective, of a maximum 2 oC global temperature increase above the pre-industrial values. The indicator has been calculated as a combination of land and sea temperature.
- The GHCN surface temperature is product of the National Climate for Environmental Information (NCEI) from National Oceanic and Atmospheric Administration (NOAA). Datasets are available as gridded product from 1880 onwards in monthly time step. Dataset was created from station data using the Anomaly Method, a method that uses station averages during a specified base 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 base 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: The source of the data is the latest version of the gridded CRUTEM (land only). 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 base period 1961-1990. The annual deviations shown in the chart have been adjusted to be relative to the period 1850-1899. Data source: EEA, based on CRUTEM dataset
- Annual, winter (December, January, February) and summer (June, July, August) mean temperature deviations in Europe, 1860 to 2008 (oC). The lines in the chart refer to 10-year moving average European land. Data source: EEA, based on CRUTEM datasets.
- Observed changes in warm spells and frost days indices in the period 1976 to 2009; 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 to 1990 period) by 5 oC. 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
- Modelled number of heat index over Europe during summer. Heat index is defined as days with an apparent temperature above 40.7 oC. Apparent temperature is determined as a human-perceived equivalent temperature caused by the combined effects of air temperature and relative humidity. (left chart: 1961 to 1990 average; middle: 2021 to 2050 average, right: 2071 to 2100 average). Data source: van der Linden P., and J.F.B. Mitchell (eds.) 2009: ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK. 160pp.
- 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. EURO-CORDEX is the European branch of the international CORDEX initiative, which is a program sponsored by the World Climate Research Program (WRCP) to organize an internationally coordinated framework to produce improved regional climate change projections for all land regions world-wide. 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).
- Projected changes in number of extreme heatwaves presented with Heat wave magnitude index under RCP4.5 and RCP8.5 in periods 2020-2052 and 2068-2100, respectively. Refer to Russo et al., 2014 for detailed information on methodology.
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 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:
- Statistical homogeneity tests have been applied to detect breaks in the time series;
- the meta-information accompanied with the data has intensively been 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, like the aforementioned data set of Climatic Research Unit; and
- findings of the different exercises have been discussed during workshops with representatives of countries.
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).
Methodology references
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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 .
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KNMI Climate Explorer
This web site gives the opportunity to explore monthly mean climate time series and the relationships between them.
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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)
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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)
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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)
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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.
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The Copenhagen Accord (2009)
United Nations Framework convention on Climate Change. UNFCCC
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The 7th Environment Action Programme (7th EAP). 2014
The 7th Environment Action Programme (7th EAP). 2014. European Union
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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)
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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
Data specifications
EEA data references
- No datasets have been specified here.
External data references
Data sources in latest figures
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 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.
Data sets uncertainty
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).
Rationale uncertainty
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).
Further work
Short term work
Work specified here requires to be completed within 1 year from now.
Long term work
Work specified here will require more than 1 year (from now) to be completed.
Identification
Indicator code
CSI 012
CLIM 001
Specification
Version id: 3
First draft created:
Publish date:
Last modified:
Frequency of updates
Updates are scheduled once per year
Classification
DPSIR: State
Typology: Performance indicator (Type B - Does it matter?)
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