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Indicator Specification
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 target 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).
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:
Global average annual temperature deviations, ‘anomalies’, are discussed relative to a ‘pre-industrial’ period between 1850 and 1899 (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 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 which can 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 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, and this period coincides with the beginning of widespread instrumental temperature records. During this time anthropogenic GHGs (greenhouse gases) from industrial activity before 1850 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 the 1971-2000 period used in parts of the IPCC Working Group One contribution to the Fifth Assessment Report (IPCC, 2013).
This indicator provides guidance for the following policy-relevant questions:
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 which can impact on human activities, including the hydrosphere with oceans and the cryosphere.
To avoid serious climate change impacts, the European Council proposed in its Sixth Environmental Action Programme (6EAP), reaffirmed by the Environment Council and the European Council of 22-23 March 2005 (Presidency Conclusions, section IV (46)) and later in the Seventh Environmental Action Programme (7EAP, 2014) , that the global average temperature increase should be limited to not more than 2 0 C above pre-industrial levels. Furthermore the UNFCCC 15th conference of the parties (COP15) recognised in the Copenhagen Accord (UNFCCC, 2009) the scientific evidence for the need to keep global average temperature increase below 2 0C above pre-industrial levels. In addition, some studies have proposed a 'sustainable' target of limiting the rate of anthropogenic warming to 0.1 to 0.2 0 C per decade.
The target for absolute temperature change (i.e. 2 0C) was initially derived from the variation of global mean temperature during the Holocene, which is the period since the last ice age during which human civilization has developed. Further studies (IPCC, 2007;Vautard, 2014) have pointed out that even a global temperature change of below the 2 0C target would still result in considerable impacts. Vulnerable regions across the world, in particular in developing countries (including least developed countries, small developing island states and Africa), would be most strongly affected. The UNFCCC Copenhagen Accord (2009) therefore foresees a review in 2015 of the scientific evidence for revising the global temperature target to 1.5°C.
Mainstreaming climate change adaptation in EU policies is one of the pillars of the EU Adaptation strategy. In the Europe 2020 strategy for smart, sustainable and inclusive growth, the following is stated on combating climate change: “We must also strengthen our economies, its resilience to climate risks, and our capacity for disaster prevention and response”.
Various data sets on trends in global and European temperature have been used for this indicator:
Global and European average time series for monthly temperature
In the original source the long-term annual and monthly mean HadCRU global temperatures were calculated from 4349 stations for the entire period of the record. There is an irregular distribution in the time and space of available stations (i.e .denser coverage over the more populated parts of the world and increased coverage after 1950). Maps/tables giving the density of coverage through time are given for land regions by Jones (2003). The gridding method was climate anomaly method (CAM), which means the station temperature data have been converted to the anomalies according to the WMO standards (baseline period 1961-1990 and at least 15 years of station data in the period) and grid-box values have been produced by simple averaging of the individual station anomaly values within each grid box.
GISS surface temperatures were calculated using around 7200 stations from Global Historical Climatology Network, United States Historical Climatology Network (USHCN) data, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. Additionally satellite SST has been included for the period after 1980. Temperatures were transformed into anomalies using station normalisation based on the 1951 to 1980 baseline period. Gridding has been done with reference station method using 1200 km influence circle (Hansen et al. 2006).
Surface temperature mean anomalies from Global Historical Climatology Network-Monthly (GHCN-M) has been produced at the NCDC from 2,592 gridded data points based on a 5° by 5° grids for the entire globe. The gridded anomalies were produced from GHCN-M bias corrected data. Gridded data for every month from January 1880 to the most recent month is available. The data are temperature anomalies in degrees Celsius (Jones, 2003).
Other global climate datasets are used by the climate research community, often with a specific purpose or audience in mind, for example processed satellite Earth-observations, and climate reanalyses. Although these are not specifically constructed for climate indicator monitoring, they do show the same temperature trends described here. Recently one new global temperature dataset has been developed especially for understanding temperature trends. This is the Berkeley Earth temperature record: http://berkeleyearth.org/
Daily climate information
Although Europe has a long history in collecting climate information, datasets containing daily climate information across the continent are scarce. Furthermore, accurate climate analysis requires long term time series without artificial breaks. The objective of the ECA project was to compile such a data set, consisting of homogeneous, long-term daily climate information. To ensure a uniform analysis method and data handling, data were centrally collected from about 200 meteorological stations in most countries of Europe and parts of the Middle East. Furthermore the data were processed and analysed at one institute (i.e. KNMI) (Klok et.al. , 2008).
In order to ensure the quality of the ECA&D climate data set:
Global and European average time series for monthly temperature
Grid values of HadCRUT, GISTEMP and GHCN data sets have been gridded using different interpolation techniques. Each grid-box value for the HadCRUT dataset is the mean of all available station anomaly values, except that station outliers in excess of five standard deviations are omitted (Brohan et al., 2005). GISTEMP temperature anomaly data are gridded into 8000 grid cells using reference station interpolation method with 1200 km influence circle (Hansen et al. 2006). GHCN monthly data consists of 2,592 gridded data points produced on a 5° by 5° basis for the entire globe (Jones, 2003).
No methodology references available.
The observed increase in average air temperature, particularly during recent decades, is one of the clearest signals of global climate change.
Temperature has been measured over the centuries. There is a range of different methodologies which give similar results suggesting that uncertainty is relatively low. Three data sets have been presented here for the global temperature indicator. Global temperatures from HadCRUT, GISTEMP, and GHCN have been homogenized to minimise the effects of changing measurement methodologies and location.
Each observation station follows international standards for taking observations set out by WMO. Each National Meteorological Service provides reports on how its data are collected and processed to ensure consistency. This includes recording information about the local environment around the observation station and any changes to that environment. This is important for ensuring the required data accuracy and performing homogeneity tests and adjustments. There are additional uncertainties because temperatures over large areas of the Earth are not observed as a matter of routine. These elements are taken into account by factoring the uncertainty into global average temperature calculations, thereby producing a temperature range rather than one uniquely definite figure (WMO, 2013). The uncertainty of temperature data has decreased over recent decades due to wider use of agreed methodologies and denser monitoring networks. Uncertainty of the temperature data comes from sampling error, temperature bias effect and from the effect of the limited observation coverage. Annual values of global and European temperature are approximately accurate to +/- 0.05 degrees C (two standard errors) for the period since 1951. They are about four times as uncertain during the 1850s, with the accuracy improving gradually between 1860 and 1950 except for temporary deteriorations during data-sparse, wartime intervals. Estimating accuracy is difficult as the individual grid-boxes are not independent of each other and the accuracy of each grid-box time series varies through time (although the variance adjustment has reduced this influence to a large extent). The issue is discussed extensively by Jones et al. (2003), Brohan et al. (2005), and Hansen et al. (2006).
According to the IPCC 4th Assessment Report (IPCC, 2007), there is very high confidence that the net effect of human activities since 1750 has been one of warming. Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic GHG concentrations. Moreover, it is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together. The best estimate of the human-induced contribution to warming is similar to the observed warming over this period (IPCC, 2013).
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For references, please go to https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature or scan the QR code.
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