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Indicator Specification
Surface air temperature gives one of the clearest signals of global and regional climate change. It has been measured for many decades, centuries even at some locations. For this reason it has been chosen as the indicator to monitor the "ultimate target" of the United Nations Framework Convention on Climate Change. Anthropogenic influence, mainly through 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 global mean temperature change time series, 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 the 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).
This indicator shows absolute changes and rates of change in average near-surface temperature for the globe and for a region covering Europe. Near-surface air temperature gives one of the clearest and most consistent signals of global and regional climate change, especially in recent decades. It has been measured for many decades — even centuries at some locations — and a dense network of stations across the globe, especially in Europe, provides regular monitoring of temperature, using standardised measurements, quality control and homogeneity procedures.
Global average annual temperature deviations (anomalies) are discussed relative to a ‘pre-industrial’ period between 1850 and 1899 (the beginning of instrumental temperature records). During this time, anthropogenic greenhouse gases from the industrial revolution (between 1750 and 1850) are considered to have had a relatively small influence on the climate compared with natural influences. However, it should be noted that owing to earlier changes in the climate due to internal and forced natural variability, there was not one single pre-industrial climate and it is not clear that there is a rigorous scientific definition of the term ‘pre-industrial climate’.
Temperature changes also influence other aspects of the climate system that can have an impact on human activities, including sea level, intensity and frequency of floods and droughts, biota and food productivity, and infectious diseases. In addition to the global average target, seasonal variations and spatial distributions of temperature change are important, for example, to understand the risks that current climate poses to human and natural systems and to assess how these may be impacted by future climate change.
The units used in this indicator are degrees Celsius (°C) and degrees Celsius per decade (°C/decade).
Baseline period
Global average annual temperature is expressed here relative to a ‘pre-industrial’ baseline period of 1850 to 1899. This period coincides with the beginning of widespread instrumental temperature records. During this time, anthropogenic greenhouse gases from pre-1850 industrial activity had a relatively small influence on climate compared with natural influences. However, it should be noted that there is no rigorous scientific definition of the term ‘pre-industrial climate’ because climate also changed prior to 1850 due to internal and forced natural variability. Other studies sometimes use a different climatological baseline period, such as 1971-2000, used in parts of the IPCC Working Group One contribution to the Fifth Assessment Report (IPCC, 2013).
In April 2013, the European Commission (EC) presented the EU Adaptation Strategy Package. This package consists of the EU Strategy on adaptation to climate change (COM/2013/216 final) and a number of supporting documents. The overall aim of the EU Adaptation Strategy is to contribute to a more climate-resilient Europe.
One of the objectives of the EU Adaptation Strategy is 'Better informed decision-making', which will be achieved by bridging the knowledge gap and further developing the European climate adaptation platform (Climate-ADAPT) as the ‘one-stop shop’ for adaptation information in Europe. Climate-ADAPT has been developed jointly by the EC and the EEA to share knowledge on (1) observed and projected climate change and its impacts on environmental and social systems and on human health, (2) relevant research, (3) EU, transnational, national and subnational adaptation strategies and plans, and (4) adaptation case studies.
Further objectives include 'Promoting adaptation in key vulnerable sectors through climate-proofing EU sector policies' and 'Promoting action by Member States'. Most EU Member States have already adopted national adaptation strategies and many have also prepared action plans on climate change adaptation. The EC also supports adaptation in cities through the Covenant of Mayors for Climate and Energy initiative.
In September 2016, the EC presented an indicative roadmap for the evaluation of the EU Adaptation Strategy by 2018.
In November 2013, the European Parliament and the European Council adopted the Seventh EU Environment Action Programme (7th EAP) to 2020, ‘Living well, within the limits of our planet’. The 7th EAP is intended to help guide EU action on environment and climate change up to and beyond 2020. It highlights that ‘Action to mitigate and adapt to climate change will increase the resilience of the Union’s economy and society, while stimulating innovation and protecting the Union’s natural resources.’ Consequently, several priority objectives of the 7th EAP refer to climate change adaptation.
To avoid serious climate change impacts, in its Sixth Environmental Action Programme (6th EAP) — reaffirmed by the Environment Council and the European Council of 22-23 March 2005 (Presidency Conclusions, section IV (46)) and later in the Seventh Environmental Action Programme (7EAP, 2014) — the European Council proposed that the global average temperature increase should be limited to not more than 2 °C above pre-industrial levels. Furthermore, in the Copenhagen Accord (UNFCCC, 2009), the UNFCCC's 15th conference of the parties (COP15) recognised the scientific evidence for the need to keep global average temperature increase below 2 °C above pre-industrial levels. In addition, some studies have proposed a 'sustainable' target of limiting the rate of anthropogenic warming to 0.1-0.2°C per decade.
The target for absolute temperature change (i.e. 2 °C) was initially derived from the variation of global mean temperature during the Holocene — the period since the last ice age during which human civilisation developed. Further studies (IPCC, 2007;Vautard, 2014) have pointed out that even a global temperature increase that remains below the 2 °C target would still result in considerable impacts. Vulnerable regions across the world, in particular in developing countries (including the least developed countries, small developing island states and Africa), would be most strongly affected.
In December 2015 (UNFCCC, 2015), countries adopted the Paris Agreement, which includes a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the increase to 1.5 °C above pre-industrial levels, since this would significantly reduce risks and the impacts of climate change.
Mainstreaming climate change adaptation in EU policies is one of the pillars of the EU Adaptation strategy. In the Europe 2020 strategy for smart, sustainable and inclusive growth, the following is stated on combating climate change: 'We must also strengthen our economies, their resilience to climate risks, and our capacity for disaster prevention and response'.
Various data sets on trends in global and European temperature have been used for this indicator:
For global and European average seasonal and annual temperature three datasets are used.
1) The HadCRUT is a collaborative product of the Met. Office Hadley Centre (sea surface temperature) and the Climatic Research Unit (land temperature) at the University of East Anglia. The global mean annual temperature deviations are, in the original source, in relation to the base period 1961-1990. The annual deviations shown in the chart have been adjusted to be relative to the period 1880-1899 in order to better monitor the EU objective to not exceed 2 °C above pre-industrial levels.
2) The GISS surface temperature is a product of the Goddard Institute for Space Studies under NASA. The original source anomalies are calculated in relation to the 1951-1980 baseline period. Annual deviations shown on the chart are adjusted to the 1880-1899 period to better monitor the EU objective of a maximum 2 °C global temperature increase above pre-industrial levels. The indicator has been calculated as a combination of land and sea temperature.
3) The GlobalTemp surface temperature is a product of the National Centres for Environmental Information from the National Oceanic and Atmospheric Administration (NOAA). Datasets are available in monthly time steps as a gridded product from 1880 onwards. The dataset was created from station data using the Anomaly Method, a method that uses station averages during a specified baseline period from which the monthly/seasonal/annual departures can be calculated. Anomalies were calculated on a monthly basis for all adjusted stations having at least 20 years of data in the 1961–1990 baseline period. Station anomalies were then averaged within each 5° by 5° grid box to obtain the gridded anomalies. For those grid boxes without adjusted data, anomalies were calculated from the raw station data using the same technique.
4) The ERA-Interim dataset is a reanalysis product from the ECMWF. The datasets maintained under Copernicus Climate change Service (C3S). The reanalysis dataset covers the period from 1979 to the present. ERA-Interim combines information from meteorological observations with background information from a forecast model, using the data assimilation approach developed for numerical weather prediction. The atmospheric observing system underwent several improvements leading up to 1979.
Over land, values of surface air temperature from ERA-Interim are in effect determined quite directly from observational records for regions where plentiful observations of surface air temperature were made. Elsewhere, the background forecast model plays a stronger role, helping values of surface air temperature to be derived from other types of observation, such as of sea-surface temperatures and winds. Satellite data on the extent of sea-ice cover are important in winter, as surface air temperatures tend to be much warmer over open sea than over ice. Observations of conditions higher in the atmosphere provide some additional information.
For European average temperature over land, the same datasets are used. Europe is defined as the area between 35° to 70° Northern latitude, -25° to 30° Eastern longitude, plus Turkey (35° to 40° North, 30° to 45° East). The European anomalies are, in the original source, in relation to the 1961-1990 baseline period. The annual deviations shown in the chart have been adjusted to be relative to the 1850-1899 period.
Temperature trends in Europe are obtained by using data from the E-OBS database. E-OBS is a daily gridded observational dataset for precipitation, temperature and sea level pressure in Europe based on ECA&D information. The full dataset covers the period 1950-01-01 until 2016-08-31. It was originally developed and updated as part of the ENSEMBLES (EU-FP6) and EURO4M (EU-FP7) projects. Currently it is maintained and elaborated as part of the UERRA project (EU-FP7). Trends are calculated using a median of pairwise slopes algorithms.
Annual, winter (December, January, February) and summer (June, July, August) mean temperature deviations in Europe (oC). The lines in the chart refer to 10-year moving average European land.
Projected changes in annual (left), summer (middle) and winter (right) near-surface air temperature (°C) in the 2071-2100 period compared to the baseline period 1971-2000 for the forcing scenarios RCP 4.5 (top) and RCP 8.5 (bottom). Model simulations are based on the multi-model ensemble average of RCM simulations from the EURO-CORDEX initiative. EURO-CORDEX is the European branch of the international CORDEX initiative, a programme sponsored by the World Climate Research Program (WRCP) to organise an internationally coordinated framework to produce improved regional climate change projections for all land regions worldwide. The CORDEX results served as an input for climate change impact and adaptation studies within the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).
Global and European average time series for monthly temperature
In the original source, the long-term annual and monthly mean HadCRU global temperatures were calculated from measurements from 4 349 stations for the entire period of the record. There is an irregular distribution in the time and space of available stations (i.e. denser coverage over the more populated parts of the world and increased coverage after 1950). Maps and tables giving the density of coverage through time are provided for land regions by Jones (2003). The gridding method used was the climate anomaly method (CAM), which means the station temperature data has been converted to the anomalies according to the WMO standards (baseline period 1961-1990 and at least 15 years of station data in the period) and grid-box values have been produced by simple averaging of the individual station anomaly values within each grid box.
GISS surface temperatures were calculated using around 7 200 stations from the Global Historical Climatology Network, United States Historical Climatology Network (USHCN) data, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. Additionally satellite sea surface temperature has been included for the post 1890 period. Temperatures were transformed into anomalies using station normalisation based on the 1951-1980 baseline period. Gridding has been done with the reference station method using a 1 200 km influence circle (Hansen et al. 2006).
Mean surface temperature anomalies from the Global Historical Climatology Network-Monthly (GHCN-M) have been produced at the NCDC from 2 592 gridded data points based on a 5° by 5° grid for the entire globe. The gridded anomalies were produced from GHCN-M bias corrected data. Gridded data for every month from January 1880 to the most recent month is available. The data are temperature anomalies in degrees Celsius (Jones, 2003).
Other global climate datasets are used by the climate research community, often with a specific purpose or audience in mind, for example processed satellite Earth-observations, and climate re-analyses. Although these are not specifically constructed for climate indicator monitoring, they do show the same temperature trends described here. Recently one new global temperature dataset has been developed especially for understanding temperature trends. This is the Berkeley Earth temperature record: http://berkeleyearth.org/
Daily climate information
Although Europe has a long history of collecting climate information, datasets containing daily climate information across the continent are scarce. Furthermore, accurate climate analysis requires long term time series without artificial breaks. The objective of the ECA project was to compile such a data set, consisting of homogeneous, long-term daily climate information. To ensure a uniform analysis method and data handling, data were centrally collected from about 200 meteorological stations in most countries of Europe and parts of the Middle East. Furthermore, the data were processed and analysed at one institute (i.e. KNMI) (Klok et al., 2008).
In order to ensure the quality of the ECA&D climate data set:
Global and European average time series for monthly temperature
Grid values of HadCRUT, GISTEMP and GHCN data sets have been gridded using different interpolation techniques. Each grid-box value for the HadCRUT dataset is the mean of all available station anomaly values, except that station outliers in excess of five standard deviations are omitted (Brohan et al., 2005). GISTEMP temperature anomaly data are gridded into 8 000 grid cells using the reference station interpolation method with a 1 200 km influence circle (Hansen et al. 2006). GHCN monthly data consists of 2 592 gridded data points produced on a 5° by 5° basis for the entire globe (Jones, 2003).
The observed increase in average air temperature, particularly during recent decades, is one of the clearest signals of global climate change.
Temperature has been measured over the centuries. There is a range of different methodologies that give similar results suggesting that uncertainty is relatively low. Three data sets have been presented here for the global temperature indicator. Global temperatures from HadCRUT, GISTEMP, and GHCN have been homogenised to minimise the effects of changing measurement methodologies and location.
Each observation station follows international standards for taking observations set out by the World Meteorological Organisation. Each National Meteorological Service provides reports on how its data are collected and processed to ensure consistency. This includes recording information about the local environment around the observation station and any changes to that environment. This is important for ensuring the required data accuracy and performing homogeneity tests and adjustments. There are additional uncertainties because temperatures over large areas of the Earth are not observed as a matter of routine. These elements are taken into account by factoring the uncertainty into global average temperature calculations, thereby producing a temperature range rather than one unique, definite figure (WMO, 2013). The uncertainty of temperature data has decreased over recent decades due to the wider use of agreed methodologies and denser monitoring networks. Uncertainty of the temperature data comes from sampling error, temperature bias effect and from the effect of limited observation coverage. Annual values of global and European temperatures are approximately accurate to +/- 0.05 °C (two standard errors) for the period since 1951. They were about four times as uncertain during the 1850s, with the accuracy improving gradually between 1860 and 1950 except for temporary deteriorations during data-sparse, wartime intervals. Estimating accuracy is difficult as the individual grid-boxes are not independent of each other and the accuracy of each grid-box time series varies through time (although the variance adjustment has reduced this influence to a large extent). The issue is discussed extensively by Jones et al. (2003), Brohan et al. (2005), and Hansen et al. (2006).
According to the IPCC's 4th Assessment Report (IPCC, 2007), there is very high confidence that the net effect of human activities since 1750 has been one of warming. Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations. Moreover, it is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the a combination of the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings. The best estimate of the human-induced contribution to warming is similar to the observed warming over this period (IPCC, 2013).
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For references, please go to https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature-4 or scan the QR code.
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