Global and European temperature

Indicator Specification
Indicator codes: CSI 012 , CLIM 001
Created 19 Jun 2015 Published 29 Jun 2016 Last modified 29 Jun 2016, 03:51 PM
Topics: ,
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. This indicator provides guidance for the following policy-relevant questions:  Will the global average temperature increase stay within the UNFCCC policy target of 2 °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?   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 a relatively small influence on the 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 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.

Assessment versions

Published (reviewed and quality assured)
  • No published assessments

Rationale

Justification for indicator selection

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).

Scientific references

  • No rationale references available

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 - 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.

This indicator provides guidance for the following policy-relevant questions:

  •  Will the global average temperature increase stay within the UNFCCC policy target of 2 °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?

 

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 a relatively small influence on the 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 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.

Units

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 (GHGs) from pre-1850 industrial activity 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 1971-2000, 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 guidance for the following policy-relevant questions:

  • Can global average temperature increase stay below the EU and UNFCCC policy target of 2 °C above pre-industrial levels?
  • Can the rate of global average temperature increase stay within the proposed indicative target of 0.2 °C increase per decade?

 

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 - including the hydrosphere, containing oceans, and the cryosphere - that can have an impact on human activities.

Targets

To avoid serious climate change impacts, 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) - 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 °C - 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”.

Related policy documents

  • 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 later. This webportal provides information on all adaptation activities of the European Commission.
  • 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 will enhance the preparedness and capacity of all governance levels to respond to the impacts of climate change.
  • European Commission related policy documents
    European Commission related policy documents
  • UNFCCC related policy documents
    UNFCCC 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: Three datasets are used. 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.
  • 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 oC global temperature increase above pre-industrial levels. The indicator has been calculated as a combination of land and sea temperature.
  • The GHCN surface temperature is a product of the National Climate Data Centre (NCDC) 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.
  • European average annual and monthly temperature:  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.
  • 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. .
  • Observed changes in warm spells ; 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 1961-1990 period) by 5 °C. 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
  • Observed number in warm days. Number of warm days are number of days when Tmax is above 90th percentile for each month.
  • 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).
  • The HWMI is defined as the maximum magnitude of the heat waves in a year, where a heat wave is the period >= 3 consecutive days with maximum temperature above the daily threshold for the reference period 1981 -2010. This threshold is the 90th percentile of the daily maximum centred on a 31 day window (Russo et al. 2014). RCP 8.5 scenario has been used.

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 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 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 is 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:

  • Statistical homogeneity tests have been applied to detect breaks in the time series;
  • the meta-information accompanying the data has been intensively 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, such as the aforementioned data set of the Climatic Research Unit; and
  • findings of the different exercises have been discussed during workshops with country representatives.


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).

Methodology references

  • 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 .
  • KNMI Climate Explorer This web site gives the opportunity to explore monthly mean climate time series and the relationships between them.
  • 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)
  • 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)
  • 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)
  • 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.
  • The Copenhagen Accord (2009) United Nations Framework convention on Climate Change. UNFCCC
  • The 7th Environment Action Programme (7th EAP). 2014 The 7th Environment Action Programme (7th EAP). 2014. European Union
  • 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)
  • 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

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 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.

Data sets uncertainty

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 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'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).

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.

General metadata

Identification

Indicator code
CSI 012
CLIM 001
Specification
Version id: 3
Primary theme: Climate change Climate change

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Frequency of updates

Updates are scheduled once per year

Classification

DPSIR: State
Typology: Performance indicator (Type B - Does it matter?)

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Data references used

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