Indicator Assessment

Global and European temperature - EEA

Indicator Assessment

Prod-ID: IND-78-en

Also known as: Outlook 021

Published 08 Jun 2009 Last modified 11 May 2021

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By 2100, global temperature change is expected to be well above the long-term sustainable objective set in the 6th EAP (bearing in mind the inherent scientific and analytical uncertainty characterising the assessment of climate change impacts).

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Baseline climate change outlook

The projected increase in global temperature above the pre-industrial level between 2000 and 2100 is about 3.1 C (see Figure 4.2). This is well above the long-term sustainable objective of a maximum of 2 C set in the 6th EAP: this is, of course, due to both European GHG emissions and non-European emissions which, for example, represent more than 90 % of global emissions in 2050. Within Europe, the highest warming is expected for the southern and north-eastern parts.

The low GHG emissions scenario

Based on the definition of the scenario (the EU long-term sustainable objective of not more than 2 C increase over pre-industrial levels, set in the 6th EAP) (117) global temperature over the 2000-2100 period is expected to reach 1.9 C over the pre-industrial level. This is about 1.2 C lower than in the baseline scenario. Within Europe, the avoided temperature increase is expected to be the largest in the south-west. In terms of global GHGs concentration, the scenario leads to a stabilisation at about 550 ppm CO2 eq. by 2100 (see Box 3.4), which is about 40 % lower than in the baseline scenario.

Global temperature change 2000-2100

Note: N/A

Data source:

EEA European Topic Centre on Air and Climate Change: National Technical University of Athens (NTUA) + Institute for Public Health and the Environment (RIVM), 2003-2004. Dataset: PRIMES model (LREM project) + FAIR/IMAGE models

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Supporting information

Indicator definition

Definition: The indicator shows trend in in annual average global surface temperature. The global average temperature change in the charts have been compared to be pre-industrial times.

Model used: IMAGE

Ownership: Netherlands Environmental Assessment Agency (MNP)

Temporal coverage: 2000 - 2100

Geographical coverage: global

Units

^oC (degrees Celcius)

Rationale

Justification for indicator selection

Temperature is directly linked to climate change and is a state variable that changes in response to the pressures of global warming. Surface air temperature gives one of the clearest signals of climate change, especially in recent decades. It has been measured for many decades or even centuries. There is mounting evidence that anthropogenic emissions of greenhouse gases are (mostly) responsible for the recently observed rapid increases in average temperature. Natural factors like volcanoes and solar activity could to a large extent explain the temperature variability up to middle of the 20th century, but can explain only a small part of the recent warming.

Absolute temperature changes and the rate of change are both important determinants of the possible effects of climate change. These include rising sea levels, floods and droughts, changes in biota and food productivity and increase of infectious diseases. Trends and projections of the annual average temperature are easy to understand and can be related to the targets. However, they do not show spatial and seasonal differences. Next to the global average target, is the rate and spatial distribution of temperature change is important, for example for determining the possibility of natural ecosystems adapting to climate change. Temperature in Europe exhibits large differences from the west (maritime) to east (continental), and from south (Mediterranean) to North (Arctic) and regional differences; winter/summer temperatures and cold/hot days illustrate temperature variations within a year.

Scientific references

CRU (2003) Global average temperature change 1856-2003 CRU (2003) Global average temperature change 1856-2003, file CruTempV2
Climate Change 2001, Synthesis Report, Summary for Policymakers, IPCC WGI report IPCC (2001). WMO/UNEP/IPCC Folland, C.K., T.R. Karl, J.R. Christy, R.A. Clarke, G.V. Gruza, J. Jouzel, M.E. Mann, J. Oerlemans, M.J. Salinger, S.W. Wang, et al.
Climate Protection Strategies for the 21st Century. Kyoto and Beyond WBGU - German Advisory Council on Global Change, Berlin, 2003.
Ecosystem vulnerability and climate protection goals Leemans, R. and R. Hootsmans (1998): Ecosystem vulnerability and climate protection goals, RIVM report 481508004, National Institute of Public Health and the Environment, Bilthoven, 118 pp.
Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. Jones, P.D. and D. Moberg (2003). Journal of Climate 16: 206-223
Oberved Climate Variability and Change Chapter 2 in Houghton, J.T., Y. Ding, D. Briggs, M. Noguer, P.J. van der Linden, P.J., X. Dai, X., K. Maskell, K., C.A. Johnson (Eds). Climate Change 2001: The scientific basis. IPCC WGI report, Cambridge University press, 99-181
EEA Core set of indicators (CSI)
EECCA core set of indicators Guidelines for the application of environmental indicators in Eastern Europe , Caucasus and Central Asia

Policy context and targets

Context description

Over a decade ago, most countries joined an international treaty -- the United Nations Framework Convention on Climate Change (UNFCCC) - to begin to consider what can be done to reduce global warming and to cope with whatever temperature increases are inevitable. Recently, a number of nations have approved an addition to the treaty: the Kyoto Protocol. The Kyoto Protocol, an international and legally binding agreement to reduce greenhouse gases emissions world wide, entered into force on February 16th 2005. The 1997 Kyoto Protocol shares the Convention's objective, principles and institutions, but significantly strengthens the Convention by committing Annex I Parties to individual, legally-binding targets to limit or reduce their greenhouse gas emissions.

To date 40 countries in the Pan-European region ratified the Kyoto Protocol, notably: Annex I: Belarus, Bulgaria, Croatia, Romania, Russian Federation, Ukraine, EU 25. Non-Annex I countries: Albania, Armenia, Azerbaijan, Georgia, Kyrgyzstan, Former Yugoslavian Republic Macedonia, Republic of Moldova, Turkmenistan, and Uzbekistan.

Kazakhstan has signed but not ratified the protocol. It expects to enter into quantitative GHG reduction obligations for the period of 2008-2012 and expects to become a full participant of the three Kyoto mechanisms. (Strategy of the Republic of Kazakhstan on Climate Change). Bosnia and Herzegovina, Serbia and Montenegro, Tajikistan and Turkey have no commitments as they did not sign or ratify the Protocol.

At the EU level the aims for reduction of the impact on Climate is expressed in the EC 6th Environmental Action Programme, EC 2006 Green paper on energy and a number of Council Decisions (see policy targets).

EECCA Environmental Strategy emphasizes importance of measure in energy and transport sectors in order to reduce Climate Change.

Targets

Global level

The UNFCC Convention and the Kyoto Protocol do not put individual targets for global temperature but they provide general policy context.

EU level

To avoid serious climate change impacts, the European Council proposed in its sixth environmental action programme (6EAP, 2002), reaffirmed by the Environment Council and the European Council of 22-23 March 2005 (Presidency Conclusions, section IV (46)), that the global average temperature increase should be limited to not more than 2 degrees C above pre-industrial levels (about 1.3 degrees C above current global mean temperature). In addition, some studies have proposed a 'sustainable' target of limiting the rate of anthropogenic warming to 0.1 to 0.2 degrees C per decade (Leemans and Hootsman, 1998, WBGU, 2003).

The targets for both absolute temperature change (i.e. 2 degrees C) and rate of change (i.e. 0.1-0.2 degrees C per decade) were initially derived from the migration rates of selected plant species and the occurrence of past natural temperature changes. Although studies have indicated that such changes might still result in impacts in various vulnerable regions, both targets have been confirmed as (a) suitable (target) from both a scientific and a political perspective (e.g. Leemans and Hootsmans, 1998, WBGU, 2003).

EECCA level
EECCA Environmental Strategy does not set any specific targets which can be measured with the help of this indicator.

Methodology

Methodology for indicator calculation

The global surface temperature change since pre-industrial times (1765) is calculated in the upwelling-diffusion climate model (UDCM) which is included as a one of the main components into the IMAGE 2.2. SRES Scenarios Model. In the UDCM four boxes are distinguished: land in northern and southern Hemisphere and ocean in northern and southern hemisphere. The temperature change of each of these boxes is based on the heat-absorbing capacity of the 40 oceanic layers. This heat-absorbing capacity is modelled for each oceanic box with an upwelling-diffusion energy-balance model. Therefore, each box has a different profile of temperature change. The global surface temperature is calculated as a weighted mean of the four boxes. The weights depend on the area within each box.

This indicator shows the most striking differences between low climate sensitivity runs (B1_low and A1F_low), high climate sensitivity runs (B1_high and A1F_high) and the main scenario runs (with medium climate sensitivity) (see uncertainties).

Overveiw of the UDCM model

The Upwelling-Diffusion Climate Model (UDCM) of IMAGE 2.2 represents the core-model of the Atmospheric Ocean System (AOS). UDCM converts the concentrations of the different greenhouse gases and SO2 emissions into radiative forcings and successively into temperature changes of the global-mean surface and the ocean. UDCM is based on the MAGICC-model of Climate Research Unit (CRU) (Hulme et al., 2000). The MAGICC model is the most widely used simple climate model within the IPCC (2001). More details on MAGICC can be found in Raper et al. (1996) and Hulme et al. (2000). The implementation of MAGICC in IMAGE 2.2 and the calculation of the radiative forcings is described by Eickhout et al. (2001).

The model input and output of UDCM are presented in under data sets.

More detailed information about the model can be found in the CD IMAGE 2.2 implementation of the SRES scenarios: A comprehensive analysis of emissions, climate change and impacts in the 21st century.

The detailed information about the IMAGE 2.2. Aproach and use of scenarios see in the methodology description of the indicator outlook 015 - GHG emissions - outlook from IMAGE model.

Methodology for gap filling

Historical data for the 1765-1995 period are used to initialise the carbon cycle and climate system. IMAGE 2.2 simulations cover the 1970-2100 period. Data for 1970-1995 are used to calibrate EIS and TES. Simulations up to the year 2100 are made on the basis of scenario assumptions on, for example, demography, food and energy consumption and technology and trade. Models are used for projections and gap fillings.

Methodology references

IMAGE 2.2 implementation of the SRES scenarios: A comprehensive analysis of emissions, climate change and impacts in the 21st century. This CD-ROM allows the user to explore the implementation of the IPCC-SRES scenarios (A1B, A1T, A1F, A2, B1 and B2) with the IMAGE 2.2 model for the 1995-2100 period. In contrast to the original SRES scenarios, the scenarios on this CD-ROM do not focus solely on emissions but also describe the possible environmental impacts of these scenarios. The scenarios can be visualized, analyzed and compared with the IMAGE 2.2 User Support System (USS). Hence, the user cannot run the IMAGE 2.2 model. Documentation is provided on all the IMAGE submodels, scenario assumptions and indicators. Furthermore, a guided tour offers an introduction to the USS and the documentation. The scenarios presented on the main disc do not reflect the uncertainties in the climate system. Some of the major uncertainties in the causal chain of climate change center on the climate sensitivity and regional climate-change patterns. The scenarios are based on one value for the climate sensitivity (the median of the IPCC (2001) range), and on one default GCM run (HADCM2). To illustrate uncertainties in climate sensitivity and climate-change patterns, the following additional simulations were made: the main disc ( IMAGE team, 2001a ) provides IMAGE 2.2 runs with changed climate sensitivity for the A1F and B1 scenarios (A1F low, A1F high, B1 low, B1 high). These scenarios span the full range of emissions of the SRES scenarios and therefore adequately illustrate the uncertainty of different climate sensitivities. a supplementary disc ( IMAGE team, 2001b ) provides IMAGE 2.2 runs with five different climate-change patterns for the A1F, B1 and A2 scenarios to illustrate the uncertainties in SRES climate-change scenarios resulting from differences in GCMs. The GCMs used include: ECHAM4, CGCM1, GFDL-LR15-a, HADCM2 and CSIRO-MK2. Another supplementary disc (forthcoming) will deal with the IMAGE 2.2 implementation of mitigation scenarios and analyses of emission burdens with the FAIR model (in preparation, IMAGE team, RIVM-CD-ROM Publication 481508020).

Uncertainties

Methodology uncertainty

Many unknowns and uncertainties in the climate system are not reflected in the IMAGE scenarios. Some of the major uncertainties in the causal chain are the climate sensitivity and regional climate-change patterns. The direct effects of a changed climate are changes in carbon uptake by the biosphere and oceans and in the distribution and productivity of crops, as well as shifts in ecosystems. Indirectly, many other processes are influenced, which can lead to the concentrations of greenhouse gases in the atmosphere being built up differently and to different land-use patterns. IMAGE simulates the consequences of these changes in an integrated fashion, accounting for interactions and feedbacks. The outcome is thus not necessarily a linear function of climate sensitivity.

These climate uncertainties were addressed by providing additional simulations to illustrate the uncertainty in the climate sensitivity and in the regional climate-change patterns.

Climate sensitivity

Climate sensitivity refers to long-term (equilibrium) change in global mean surface temperature following a doubling of the atmospheric concentration in CO2 equivalents. According to IPCC, this climate sensitivity is between 1.5oC and 4.5oC. In earlier versions of IMAGE, the climate sensitivity generated by the climate model was 2.4oC. Due to the rigid structure of these earlier versions, we were unable to change this and assess the consequences of such a change.

In IMAGE 2.2 a simpler climate model MAGICC (see Upwelling-Diffusion Climate Model) is incorporated, allowing to define the climate sensitivity. The default value for IMAGE runs is 2.5, which is the median value of the IPCC range (median differs from mean because the range is logarithmic).

To test the uncertainty related to the climate sensitivity, runs with respectively a low (1.5oC) and high (4.5oC) climate sensitivity were created. A pattern-scaling procedure is used to obtain regional and seasonal climate-change patterns using the calculated increase in global mean temperature.

Runs with changed climate sensitivity are provided for the A1F (A1F low, A1F high) and B1 (B1 low, B1 high) scenarios on the main disc (IMAGE team 2001a). These scenarios span the full range of the SRES emission scenarios and therefore adequately illustrate the uncertainty of different climate sensitivities.

Regional climate-change patterns

Climate-change patterns are not simulated explicitly in IMAGE. The global mean temperature increase, as calculated by IMAGE, is linked with the climate patterns generated by a general circulation model (GCM) for the atmosphere and oceans. This linking takes place using the standardized IPCC pattern-scaling approach (Carter et al., 1994) and additional pattern-scaling for the climate response to sulphate aerosols forcing (Schlesinger et al., 2000; see Geographical Pattern Scaling, GPS). GCMs are currently the best tools available for simulating the physical processes that determine global climate dynamics and regional climate patterns.

GCMs simulate climate over a continuous global grid with a spatial resolution of a few hundred kilometres and a temporal resolution of less than an hour.

Most GCMs agree on the global patterns of climate change:

temperature increases above land are faster than above the oceans
high latitudes warm up more sharply than low latitudes
winter warms up more sharply than summers
total precipitation increases with increasing temperature
maritime regions generally get wetter
continental regions could get dryer.

Regionally, however, there are large differences between the different GCMs, especially in precipitation-change patterns.

IMAGE 2.2 runs with five different climate-change patterns are provided on the supplementary disc (IMAGE team 2001b, RIVM CD-ROM publication 481508019) for the A1F, B1 and A2 scenarios. The aim of this material is to illustrate the uncertainties in SRES climate-change scenarios resulting from these differences in GCMs. The first two scenarios span the full range of the SRES emission scenarios, the latter being based on a highly different narrative with different demographic and socio-economic assumptions. The three scenarios therefore adequately illustrate the uncertainty of different climate patterns. Differences in the runs for each scenario indicate some of the uncertainty caused by regional variation in climate-change patterns (not the global mean).

The scenarios for five different GCM runs from the IPCC data centre were implemented, which comprised:

ECHAM4 of the Deutsches Klimarechenzentrum DKRZ in Germany
CGCM1 of the Canadian Centre for Climate Modelling and Analysis in Canada
GFDL-LR15-a of the Geophysical Fluid Dynamics Laboratory in the USA
HADCM2 of the Hadley Centre for Climate Prediction and Research in the UK
CSIRO-MK2 of Commonwealth Scientific and Industrial Research Organisation in Australia

Data sets uncertainty

The input data to the UDCM model is atmospheric concentrations of greenhouse gases and emissions of SO2, which by itself is calculated on the basis of the other IMAGE 2.2. Models and bare all uncertainties related to those models (see more in methodology uncertainly).

Rationale uncertainty

The observed increase in average air temperature, particularly during the recent decades, is one of the clearest signals of global climate change

The indicator shows trends in temperature data over time. Temperature is directly linked to the question of climate change and is a state variable that changes in response to the pressures of global warming.

There is growing evidence that anthropogenic emissions of greenhouse gases are (mostly) responsible for the recently observed fast increases in average temperature. Natural factors like volcanoes and sun activity could explain to a large extent the temperature variability up to mid of the 20th century, but they can explain only a small part of the recent warming.