Indicator Specification

Global and European temperatures

Indicator Specification

Indicator codes: CSI 012 , CLIM 001

Published 30 Sep 2020 Last modified 04 Dec 2020

19 min read

Topics: Climate change adaptation

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. Time series of global and European land temperatures in Figure 1 are provided both as annual values (top) and as decadal averages (bottom).

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Assessment versions

Published (reviewed and quality assured)

30 Sep 2020, 04:10 PM
- Global and European temperatures

Rationale

Justification for indicator selection

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).

Rising mean temperatures are also increasing the frequency and severity of heatwaves globally and in Europe.

Scientific references

Allen, M., et al., 2018, ‘Summary for policymakers’, in: Global warming of 1.5°C. An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, Cambridge University Press, Cambridge.
C3S, 2020, European state of the climate 2019, Climate Bulletin, Copernicus Climate Change Service (https://climate.copernicus.eu/ESOTC/2019) accessed 7 September 2020.
Collins, M., et al., 2013, ‘Long-term climate change: projections, commitments and irreversibility’, in: Stocker, T. F. 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; New York, pp. 1029-1136.
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).
Jacob, D., et al., 2013, ‘EURO-CORDEX: New high-resolution climate change projections for European impact research’, Regional Environmental Change 14(2), pp. 563-578 (DOI: 10.1007/s10113-013-0499-2)
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).
Meinshausen, M., et al., 2011, ‘The RCP greenhouse gas concentrations and their extensions from 1765 to 2300’, Climatic Change109(1), pp. 213-241 (DOI: 10.1007/s10584-011-0156-z).
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).

Indicator definition

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.

Time series of global and European land temperatures in Figure 1 are provided both as annual values (top) and as decadal averages (bottom).

Units

The units used in this indicator are degrees Celsius (°C) and degrees Celsius per decade (°C/decade).

Temperature anomalies are presented relative to a ‘pre-industrial’ period between 1850 and 1899 (the beginning of instrumental temperature records).

Policy context and targets

Context description

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’ (UNFCCC, 2016). 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 (UNDRR, 2015; UN, 2015).

Targets

Key policy question

Aggregated level assessment

Specific policy question

Disaggregate level assessment

Methodology

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:

HadCRUT4 (Morice et al., 2012): This dataset is a collaborative product of the Met Office Hadley Centre and the Climatic Research Unit (CRU) of the University of East Anglia.
NOAA Global Temp v5 (Karl et al., 2015; Zhang et al., 2019): This dataset is a product of the National Centre for Environmental Information of the U.S. National Oceanic and Atmospheric Administration (NOAA).
GISTEMP v4 (Lenssen et al., 2019): 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.

Spatially explicit temperature trends in Europe are derived from E-OBS v20.0e (Cornes et al., 2018). 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 projected changes in European near-surface air temperature (°C) are based on the multi-model ensemble average of RCM simulations from the EURO-CORDEX initiative (Jacob et al., 2013). EURO-CORDEX is the European branch of the CORDEX initiative, a programme sponsored by the World Climate Research Program (WRCP) to produce improved regional climate change projections for all land regions worldwide.

Further information on all these datasets is available from the cited publications.