This indicator shows observed and projected changes in annual average near-surface temperature globally and for Europe. Europe is defined here as the land area in the range 34° to 72° northern latitude and -25° to 45° eastern longitude.
Temperature anomalies are presented relative to a ‘pre-industrial’ period between 1850 and 1899 (the beginning of instrumental temperature records). During this period, greenhouse gases from the industrial revolution are considered to have had a relatively small influence on the global climate compared with natural influences.
Methodology for indicator calculation
The following global meteorological datasets have been used to compute the time series of global mean temperature and European land temperature:
· HadCRUT5: This dataset is a collaborative product of the Met Office Hadley Centre and the Climatic Research Unit (CRU) of the University of East Anglia. HadCRUT5 is a combination of sea-surface temperature (SST) measurements over the ocean from ships and buoys and near-surface air temperature measurements from weather stations over the land surface.
· NOAA Global Temp v5 : This dataset is a product of the National Centre for Environmental Information of the U.S. National Oceanic and Atmospheric Administration (NOAA).
· GISTEMP v4: This dataset is a product of the NASA Goddard Institute for Space Studies (GISS).
The temperature anomalies from the original datasets were adjusted here to the ‘pre-industrial’ period between 1850 and 1899.
· Berkeley Earth: temperature dataset produced by Berkeley Earth; an independent U.S. non-profit organization focused on environmental data science.
· ERA5 Dataset produced by ECMWF and available in the Climate Data Store. ERA5
is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset.
· JRA-55: The Japan Meteorological Agency (JMA) conducted the second Japanese global atmospheric reanalysis, called the Japanese 55-year Reanalysis or JRA-55. It covers the period from 1958, when regular radiosonde observations began on a global basis.
These datasets were provided by the Copernicus Climate Change Service (C3S,2020), following the developed methodology to relate recent global temperature to 1850-1900, a period taken to represent the pre-industrial level. New and updated global temperature datasets have been recently published, which has resulted in a new estimate, as outlined in the IPCC AR6 WGI report 'Climate Change 2021: The Physical Science Basis'. Based on this estimate a new approach for monitoring global temperature change since the 1850-1900 period is being used in the WMO statements on “The state of the global climate” from the Preliminary Statement for 2021 onwards (see details there, under “Datasets and methods – Global temperature data”). This approach results in a best estimate of 0.69°C with an uncertainty range (0.54 to 0.78°C) to relate the standard WMO reference period 1981-2010 to 1850-1900. Extending this approach to the WMO reference period 1991-2010 gives a best estimate of 0.88°C with an uncertainty range (0.72-0.99°C). In the datasets here provided by C3S this method has been used, by calculating the anomalies of each dataset relative to its own average for 1981-2010 and adding the offset of 0.69°C, to then relate it to 1850-1900.
For Europe there is no such 'consensus' estimate for relating datasets to 1850-1900, thus the method employed here is the same as is used for the C3S European temperature indicator release in April 2020 (C3S, 2020): https://climate.copernicus.eu/climate-indicators/temperature
This means that the datasets are first normalised to 1981-2010. That average uses HadCRUT5 and Berkeley Earth for 1850-1879 and these two plus GISTEMP and NOAAGlobalTemp for 1880-1900. This is not what was done in the AR6 method to produce the consensus estimate for the global value, as it used only datasets that went back to 1850. The minimum and maximum values for the HadCRUT5 ensemble are included. Otherwise, HadCRUT5 refers to the ensemble mean dataset downloadable from the Hadley Centre.
Spatially explicit temperature trends in Europe are derived from E-OBS v20.0e. E-OBS is a daily gridded observational data set for precipitation, temperature and sea level pressure in Europe based on ECA&D information. The ECA&D project maintained by KNMI has collected homogeneous, long-term daily climate information from about 200 meteorological stations in most countries of Europe and parts of the Middle East. The dataset covers the period from 1950 on. Trends are calculated using a median of pairwise slopes algorithm. The underlying stations timeseries used as input to the interpolation of E-OBS are usually not corrected for inhomogeneities (except for E-OBSv19.0eHOM). Also, the station density is not constant through time. Trend analyses should therefore be treated with caution. In Figure 2 a the cross out areas indicate areas with very loww trends (< 0.1) that are artifacts of the station desnity and inhomogeneities.
The projected changes in European near-surface air temperature (°C) are based on the multi-model ensemble average of GCM simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) initiative . CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Further information on all these datasets is available from the cited publications.
Near-surface air temperature gives one of the clearest signals of global and regional climate change. Anthropogenic influence, mainly through emissions of greenhouse gases, is responsible for most of the observed increase in global mean temperature (GMT) in recent decades. For these reasons, GMT has been chosen as the indicator to monitor the 'ultimate objective' of the United Nations Framework Convention on Climate Change (UNFCCC).
The Paris Agreement adopted in December 2015 defines the long-term goal to 'hold the increase in the global average temperature to well below 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels, since this would significantly reduce risks and the impacts of climate change’. The need to limit the increase in GMT in accordance with the goals of the UNFCCC is also recognised in the Sendai Framework for Disaster Risk Reduction 2015-2030 and in Goal 13 of the 2030 Agenda for Sustainable development .
Rising mean temperatures are also increasing the frequency and severity of heatwaves globally and in Europe.
- C3S, 2020, European state of the climate 2019, Climate Bulletin, Copernicus Climate Change Service (https://climate.copernicus.eu/ESOTC/2019) accessed 7 September 2020.
- Cornes, R. C., et al., 2018, ‘An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets’, Journal of Geophysical Research: Atmospheres123(17), pp. 9391-9409 (DOI: 10.1029/2017JD028200).
- Karl, T. R., et al., 2015, ‘Possible artifacts of data biases in the recent global surface warming hiatus’, Science348(6242), pp. 1469-1472 (DOI: 10.1126/science.aaa5632).
- Lenssen, N. J. L., et al., 2019, ‘Improvements in the GISTEMP Uncertainty Model’, Journal of Geophysical Research: Atmospheres124(12), pp. 6307-6326 (DOI: 10.1029/2018JD029522).
- Morice, C. P., et al., 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).
- UN, 2015, Resolution adopted by the General Assembly on 25 September 2015 - Transforming our world: the 2030 Agenda for Sustainable Development (A/RES/70/1).
- UNDRR, 2015, Sendai Framework for Disaster Risk Reduction 2015-2030, United Nations Office for Disaster Risk Reduction, Geneva (http://www.unisdr.org/we/inform/publications/43291) accessed 23 November 2017.
- UNFCCC, 2016, ‘The Paris Agreement’ (http://unfccc.int/paris_agreement/items/9485.php) accessed 4 January 2017.
- WMO, 2019, The Global Climate in 2015–2019, No JN 191303, World Meteorological Organization, Geneva (https://library.wmo.int/index.php?lvl=notice_display&id=21522#.XeesozJ7lpg) accessed 4 December 2019.
- Zhang, H.-M., et al., 2019, 'Updated Temperature Data Give a Sharper View of Climate Trends', Eos100 (DOI: 10.1029/2019EO128229).
Data sets uncertainty