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You are here: Home / Data and maps / Indicators / Global and European temperature / Global and European temperature (CSI 012/CLIM 001) - Assessment published May 2011

Global and European temperature (CSI 012/CLIM 001) - Assessment published May 2011

Indicator Assessment Created 24 Mar 2011 Published 23 May 2011 Last modified 13 Jan 2015, 09:45 AM
Topics: ,

Generic metadata

Topics:

Climate change Climate change (Primary topic)

Tags:
climate | thematic assessments | climate change | soer2010 | temperatures | understanding climate change | csi
DPSIR: State
Typology: Performance indicator (Type B - Does it matter?)
Indicator codes
  • CSI 012
  • CLIM 001
Dynamic
Temporal coverage:
1850-2098
Geographic coverage:
Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kazakhstan, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia (FYR), Malta, Moldova, Monaco, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Russia, San Marino, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, United Kingdom
 
Contents
 

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 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:

  •  Will the global average temperature increase stay within the UNFCCC policy target of 2.0°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 (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

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).

 


Specific policy question: What is the trend and rate of change in the European annual and seasonal temperature?

Observed changes in frost days indices 1976-2010 (in days per decade)

Note: How to read the map: Frost day is defined as a day with an average temperature below 0 ºC. Stations with positive trends are in blue and stations with negative trend are in red colour. When stations are in green colour trends are not statistically significant at 25% level.

Data source:

KNMI (http://eca.knmi.nl/ensembles); Haylock et al, 2009

Klein Tank, A.M.G. et al, 2002. Daily dataset of 20th-century surface

air temperature and precipitation series for the European Climate Assessment.

Int. J. of Climatol., 22, 1441-1453.

 

Downloads and more info

Annual, winter (December, January, February) and summer (June, July, August) mean temperature deviations in Europe, 1860-2010 (°C)

Note: The lines refer to 10-year moving average European land.

Data source:

EEA 2010, KNMI  (http://climexp.knmi.nl/), based on Climate Research Unit (CRU) gridded datasets HadCrut3 (land and ocean) and CruTemp3 (land only) from http://www.cru.uea.ac.uk/cru/data/temperature/

Downloads and more info

Projected average number of summer days exceeding the apparent temperature

Note: The maps show the number of summer days in Europe exceeding the apparent temperature (heat index) threshold of 40.7 °C as simulated by five ENSEMBLES Regional Climate Models for the IPCC SRES A1B emission scenario. The apparent temperature (often referred to as the heat index) represents heat stress on the human body by accounting for temperature

Data source:

EU-FP6 project ENSEMBLES; (http://eca.knmi.nl/ensembles); Haylock et al, 2008 ;

(http://www.ensembles-eu.org/) van der Linden and Mitchell, 2009, Fischer and Schaer, 2010.

 

Downloads and more info

European annual average temperature deviations, 1850-2010, relative to the 1850-1899 average (in °C).

Note: The lines refer to 10-year moving average, the bars to the annual 'land only' European average. The source of the original data is the Climatic Research Unit of the University of East Anglia. The European mean annual temperature deviations are in the 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 1850-1899 to better monitor the EU objective not to exceed 2°C above pre-industrial values. Over Europe average annual temperatures during the real pre-industrial period (1750-1799) were very similar to those during 1850-99. 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 resulting temperature anomalies were obtained using KNMI's climate explorer.

Data source:

EEA 2010, KNMI  (http://climexp.knmi.nl/), based on Climate Research Unit (CRU) gridded datasets HadCrut3 (land and ocean) and CruTemp3 (land only) from http://www.cru.uea.ac.uk/cru/data/temperature/

Downloads and more info

Observed changes in warm spells indices 1976-2010 (in days per decade)

Note: How to read the map: Warm spell duration index is defined as a period (number of days) of six consecutive days with the mean daily temperature exceeding 90th percentile of the baseline temperature (average daily temperature the 1961-1990 period). Stations with negative trends are blue and stations with positive trends are in red colour. When stations are in green colour trends are not statistically significant at 25% level.

Data source:

KNMI (http://eca.knmi.nl/ensembles); Haylock et al, 2009

Klein Tank, A.M.G. et al, 2002. Daily dataset of 20th-century surface

air temperature and precipitation series for the European Climate Assessment.

Int. J. of Climatol., 22, 1441-1453.

Downloads and more info

Specific assessment

For Europe, temperature anomalies are shown for both 'land & ocean' and for 'land-only'. The first enables a comparison with the global average, the second shows temperature changes that the European citizens experience. All data are based on the datasets maintained by the Met Office Hadley Centre and Climatic Research Unit, University of East Anglia (CruTEM3 and HadSST2 for 'land & ocean' combined and CruTEM3 for 'land only').

Annual and seasonal average in Europe

The decadal average temperature has increased by 1.2 °C for the European land area (using the CruTEM3 dataset) and 1.0 °C for the European land & ocean area (combining the CruTEM3 and HadSST2 datasets),  when comparing the period 2001 - 2010 with pre-industrial times (1850 - 1899)  (Figure 3). Europe has thus warmed more than the global average (i.e. 1.0 - 1.2 °C compared to 0.81 - 0.89 °C). Considering the European land, 8 out of the last 13 years (1998 and 2010) were among the warmest years since 1850s in Europe with 2007 as warmest year (1.5 °C higher than 1850 - 1899 average temperature). The year 2010 was less warm in Europe than other recent years, ranking 24th warmest year on record with about 1.78 °C higher than the pre-industrial.  Geographically, particularly significant warming has been observed in the past 50 years over the Iberian Peninsula, in central and north - eastern Europe and in mountainous regions (Haylock, 2008). In the past 30 years, warming was the strongest over Scandinavia, especially in winter, whereas the Iberian Peninsula warmed in summer.
On average, Europe warmed more in winter than in summer (Figure 4).  The 2009 winter was relatively cold in most of Europe (e.g. temperatures dropped down to -40 °C in some locations in Scandinavia whereas in northern Italy experienced temperature of -17 °C) with also extensive snowfall in many places. The spring and summer season of 2009 was warmer than the long-term (1961-1990) average, particularly over southern Europe. Spain had the third warmest summer after the very hot summers of 2003 and 2005. Autumn, in contrary, was cold again (WMO, 2010).
Similar to the global temperature the average temperature over Europe is also projected to continue increasing over the next century. According to the ENSEMBLES project (van der Linden, 2009) the annual average temperature will increase more than global temperature, considering the A1B emission scenario (which is one of the six IPCC SRES scenarios).
In addition most of the Regional Climate Models (RCMs) results show, that the warming is projected to be the greatest over north-eastern Europe and Scandinavia in winter (December to February), and in the Mediterranean in summer (June to August) (van der Linden, 2009;). Summer temperature are projected to increase by up to 7 °C in Southern Europe and 5 °C in the Northern Europe comparing the period 2080 - 2100 with the 1961 -  1990 average. (van der Linden, 2009).
These results have been obtained from 25 different Regional Climate Models (RCMs) performing at 25 km spatial resolution with boundary conditions from five Global Climate Models (GCMs), all using the IPCC SRES A1B emission scenario.

Temperature extremes in Europe

 

High - temperature extremes like summer days, tropical nights, and heat waves  have become more frequent, while low - temperature extremes (e.g. cold spells, frost days) have become less frequent in Europe (IPCC, 2007a, Figure 5, Figure 6).

The average length of summer heat waves over Western Europe doubled over the last 100 years and the frequency of hot days almost tripled (Della - Marta et al., 2007). In the period 1976 - 2009 increase of heat wave duration was observed in whole Europe (Fig. 5), however the trend is not significant at the majority of stations. In contrary, the significant decrease in number of frost days has been observed in most of the European area (Fig. 6).Extreme high temperature events across Europe, along with the overall warming, are projected to become more frequent, intense and longer this century (Tebaldi et al., 2006, IPCC, 2007a,b; Beniston et al., 2007; Haylock et al, 2008, van der Linden et al, 2009, Fig. 7). Geographically, the maximum temperature during summer is projected to increase far more in southern and central Europe than in northern Europe, whereas the largest reduction in the occurrence of cold extremes is projected for northern Europe (Kjellstroem et al., 2007; Beniston et al., 2007; Sillman and Roekner; 2008; Haylock et al, 2008; van der Linden, 2009; Fischer and Schaer, 2010). According to the ENSEMBLES RCM scenarios for 2071 - 2100 (van der Linden et al, 2009) the number of days with apparent temperature exceeding 40.7 °C (heat index) will double in most parts of southern Europe.

 

 

Specific policy question: Answer to unknown question

Observed global annual average temperature deviations in the period 1850–2010 (in ºC)

Note: In blue, the source of the original anomalies is the combined UK Met Office Hadley Centre and Climate Research Unit dataset, HadCRUT3. The global mean annual temperature deviations are in relation to the base period 1961-1990. In red, the source of the original anomalies is NASA's GISS dataset. The anomalies are in the source in relation to the base period 1951-1980. The global mean annual temperature deviations have been adjusted to be relative to the period 1850-1899 (HadCRUT3) and 1880 - 1899 (NASA's GISS). All original data is rounded to the nearest 2 decimal places. The trend lines show the 10-year centred moving average of the original anomalies for both datasets relative to the period 1880-1899. The dotted lines show the annual anomalies of the HadCRUT3 (blue) data set and GISS (red) respectively.

Data source:

EEA, based on NASA's GISS mean land-ocean temperature anomalies and the combined UK Met Office Hadley Centre and Climate Research Unit HadCRUT3 dataset.

Downloads and more info

Rate of change of global average temperature, 1850-2010 (in ºC per decade)

Note: Lines refer to the decadal rate of change of the global temperature anomalies. Sources of the data are NASA’s GISS mean land-ocean temperature anomalies and the Hadley Center’s HadCRUT3 dataset

Data source:

EEA, based on NASA's GISS mean land-ocean temperature anomaly and Hadley Center's HadCRUT3 dataset

Downloads and more info

Specific assessment

 

 

In the statement on the status of the global climate (WMO, 2010), World Meteorological Organisation (WMO) has shown temperature anomalies from three datasets. For the indicator we have used the two most independent datasets, maintained seperately by the Met Office Hadley Centre and Climatic Research Unit, University of East Anglia in the United Kingdom (HadCRUT3) and by the Goddard Institute for Space Studies (GISS) operated by the National Aeronautics and Space Administration (NASA) in the United States (GISTEMP).

Global assessment

The Earth has experienced considerable temperature increases in the last 100 years, especially in the most recent decades. These changes are unusual in terms of both magnitude and rate of change. The global average temperature increase between 1850 and 2010 was 0.81 °C for the HadCRUT3 dataset and between 1880 and 2010 it was 0.89 °C for the GISTEMP dataset, compared to the 1850 - 1899 average temperature (HadCRUT3) or 1880-1899 (GISTEMP). This is about one third of the EU 'sustainable' target of limiting global average warming to not more than 2 °C above the level in the pre-industrial period as defined for the purpose of this indicator (Figure 1). On the decadal scale, the last decade (2001 - 2010) was warmer in both of these records than the 1990s (1991 - 2000), which in turn was warmer than the 1980s (1981 - 1990) and earlier decades (WMO, 2010). Compared to pre-industrial times (1850-1899 for the HadCRUT3 dataset and 1880-1899 for GISTEMP), global air temperature was on average 0.74 °C (for HadCRUT3) and 0.81 °C (for GISTEMP) warmer during the last decade (2001-2010). Furthermore, 11 out of the last 13 years (1998 - 2010) rank as the warmest years in these instrumental records, and 2010 was either the warmest (for GISTEMP) or second warmest (for HadCRUT3) year globally in the records.

The rate of change in the global average temperature (representing land and ocean areas) has been accelerating from 0.06 °C per decade (for both the GISTEMP and HadCRUT3 datasets) over the last 100 years, to 0.09 °C and 0.10 °C per decade over the past 50 years and 0.22 °C and 0.18 °C when comparing the 2000-2010 decade with the 1990-2000 decade for the GISTEMP and HadCRUT3 datasets respectively (Figure 2). Thus these rates of change are now close to 0.2 °C per decade, the indicative limit proposed by some scientific studies. 


The global average temperature is projected to continue to increase. Globally, the projected increase in this century is between 1.8 and 4.0 0C (best estimate), and is considered likely (66 % probability) to be between 1.1 and 6.4 0C for the six IPCC SRES scenarios and multiple climate models (IPCC, 2007a), comparing the 2080 - 2100 average with the 1961 - 1990 average. These scenarios assume that no additional policies to limit greenhouse gas emissions are implemented (IPCC, 2007). The range results from the uncertainties in future socio-economic development and in climate models. The EU and UNFCCC Copenhagen Accord target of limiting global average warming to not more than 2.0 0C above pre - industrial levels is projected to be exceeded between 2040 and 2060, for all six IPCC scenarios.

Data sources

Policy context and targets

Context description

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

  • Can the global average temperature increase stay below the EU and UNFCCC policy target of 2.0°C above pre-industrial levels?
  • Can the rate of global average temperature increase stay within the indicative proposed 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 which can impact on human activities, including the hydrosphere with oceans and the cryosphere.

Targets

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”.

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

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: 3 datasets are used. The HadCRUT  is collaborative products 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 to 1899 in order to better monitor the EU objective not to exceed 2oC above pre-industrial values.
  • The GISS surface temperature is a product of the Goddard Institute for Space Studies under NASA. The original source anomalies are calculated in the relation to the 1951 to 1980 base period. Annual deviations shown on the chart are adjusted to the 1880 to 1899 period to better monitor the EU objective, of a maximum 2 oC global temperature increase above the pre-industrial values. The indicator has been calculated as a combination of land and sea temperature.
  • The GHCN surface temperature is product of the National Climate Data Centre (NCDC) from National Oceanic and Atmospheric Administration (NOAA). Datasets are available as gridded product from 1880 onwards in monthly time step.  Dataset was created from station data using the Anomaly Method, a method that uses station averages during a specified base 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 base 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: The source of the data is the latest version of the gridded CRUTEM (land only). 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 base period 1961-1990. The annual deviations shown in the chart have been adjusted to be relative to the period 1850-1899. Data source: EEA, based on CRUTEM dataset
  • Annual, winter (December, January, February) and summer (June, July, August) mean temperature deviations in Europe, 1860 to 2008 (oC). The lines in the chart refer to 10-year moving average European land. Data source: EEA, based on CRUTEM datasets.
  • Observed changes in warm spells and frost days indices in the period 1976 to 2009; 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 to 1990 period) by 5 oC. 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
  • Modelled number of heat index over Europe during summer. Heat index is defined as days with an apparent temperature above 40.7 oC. Apparent temperature is determined as a human-perceived equivalent temperature caused by the combined effects of air temperature and relative humidity. (left chart: 1961 to 1990 average; middle: 2021 to 2050 average, right: 2071 to 2100 average). Data source: van der Linden P., and J.F.B. Mitchell (eds.) 2009: ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK. 160pp.
  • Projected changes in annual (left), summer (middle) and winter (right) near-surface air temperature (°C) in the period 2071-2100 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,  which is a program sponsored by the World Climate Research Program (WRCP) to organize an internationally coordinated framework to produce improved regional climate change projections for all land regions world-wide. 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).

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 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:

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


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).

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 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.

Data sets uncertainty

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).

Rationale uncertainty

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).

More information about this indicator

See this indicator specification for more details.

Dates

Frequency of updates

Updates are scheduled once per year
European Environment Agency (EEA)
Kongens Nytorv 6
1050 Copenhagen K
Denmark
Phone: +45 3336 7100