GHG Concentrations - outlook from MNP

Indicator definition

Definition: The indicator shows the measured trends and projections of greenhouse gas concentrations. The various greenhouse gases have been grouped in three different ways. In all cases the effect of greenhouse gas concentrations on the enhanced greenhouse effect is presented as CO2-equivalent concentration. Global annual averages are considered.

Model used: IMAGE

Ownership: European Environment Agency

Temporal coverage: 2000 - 2100

Geographical coverage:  global

Units

Policy context and targets

Context description

Global policy context

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.

EU policy context

The indicator is aimed at supporting assessment of progress towards the EU long-term target to limit global temperature increase to below 2 degrees C above pre-industrial levels, and, derived from this, stabilisation of GHG concentrations at well below 450 ppm CO₂-equivalent (

EECCA policy context

There are no specific policies concerning atmospheric greenhouse gas concentrations in this region. However, in EECCA Environmental Strategy reduction of GHGs are defined as one of the aims.

Targets

Global level

• to achieve 'stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system. Such a level should be achieved within a time-frame sufficient to allow ecosystems to adapt naturally to climate change, to ensure that food production is not threatened and to enable economic development to proceed in a sustainable manner' (The ultimate objective of the United Nations Framework Convention on Climate Change (UNFCCC)

EU level

• Limiting global temperature rise to a maximum of 2 °C compared with pre-industrial levels (6th EAP)
• Global GHG concentrations may need to be stabilised at much lower levels, e.g. 450 ppm CO2-equivalent. Stabilisation of concentrations as well below 450 ppm CO2-equivalent may be needed and global GHG emissions would have to peak within two decades, followed by substantial reductions by 2050 compared with 1990 levels (the Environment Councils of 20 December 2004 and 22-23 March 2005)
• To achieve stabilisation in an equitable manner, developed countries should reduce emissions by about 15-30% by 2020 and 60-80% by 2050, below the base year levels (1990) (The EU Environment Council (March 2005))

EECCA level

no targets were set at this subregional level

Related policy documents

• Council Decision (2002/358/EC) of 25 April 2002
  Council Decision (2002/358/EC) of 25 April 2002 concerning the approval, on behalf of the European Community, of the Kyoto Protocol to the United Nations Framework Convention on Climate Change and the joint fulfilment of commitments thereunder.
• Greenhouse gas monitoring mechanism Decision
  Decision No 280/2004/EC of the European Parliament and of the Council of 11 February 2004 concerning a mechanism for monitoring Community greenhouse gas emissions and for implementing the Kyoto Protocol
• Kyoto Protocol to the UN Framework Convention on Climate Change
  Kyoto Protocol to the United Nations Framework Convention on Climate Change; adopted at COP3 in Kyoto, Japan, on 11 December 1997
• Sixth Environment Action Programme (decision No 1600/2002/EC)
  DECISION No 1600/2002/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 22 July 2002 laying down the Sixth Community Environment Action Programme
• UNFCCC
  UNFCCC reporting guidelines on annual inventories

Methodology

Methodology for indicator calculation

The concentrations are calculated in the Atmospheric Chemistry Model (ACM) of IMAGE 2.2 on the basis of the emissions generated by the TIMER emissions model (TEM) and Land Use Emissions Model (LUEM). The global mean concentrations are used as input of the Upwelling-Diffusion Climate Model (UDCM) of IMAGE 2.2.  The AEA technology approach was used for methane.

Overview of the IMAGE Scenarios Model

The Integrated Model to Assess the Global Environment (IMAGE) developed by the National Institute for Public Health and the Environment (RIVM), is a dynamic integrated assessment modeling framework for global change. The main objectives of IMAGE are to contribute to scientific understanding and support decision-making by quantifying the relative importance of major processes and interactions in the society-biosphere-climate system. To accomplish this, IMAGE provides:

• dynamic and long-term perspectives on the systemic consequences of global change
• insights into the impacts of global change
• a quantitative basis for analyzing the relative effectiveness of various policy options to address global change.

More detailed description of the IMAGE model can be found here.

The IMAGE model has a range of submodels which calculate different parameters for the model. Therefore, GHG's concentrations are included in the Atmospheric Chemistry Model (ACM).

ACM sub-mode's calculations

Thus the change in concentrations depends on the change in both emissions and the atmospheric removal, determined by its atmospheric lifetime. However, for N2O, CCl3, the CFCs, bromocarbons and PFCs, the chemical lifetime is assumed constant, as adopted in most simple climate models currently used ( Harvey et al., 1997). The calculation of the CO concentration is slightly modified from the approach used in IMAGE 2.0 (Krol and Van der Woerd, 1994). On the basis of recent literature, the CO yield factor for NMVOC emissions is 0.4; the lifetime of CO due to soil uptake and stratospheric loss is 1.10 year ( M?and Brasseur, 1995).

ACM covers such kinds of emissions and their further concentrations as:

• emissions of CH4, N2O, NOx, CO, NMVOCs, CFCs, CCs, HCFCs, bromocarbons, PFCs, SF6 and HFCs;
• concentration of CH4, N2O, CO, tropospheric ozone, CFCs, CCs, HCFCs, bromocarbons, PFCs, SF6, HFCs

Overview of the AEA technology approach (for methane)

to be included

 Key model assumptions

Baseline scenario

The baseline scenario follows a conventional definition and expands on current expectations regarding macro-economic, sectoral, technological and societal developments, as well as including those policies that have been implemented and/or adopted, which typically refer to pieces of legislation such as EU directives or political agreements.

EEA's outlooks across the various sectors and themes use a common reference set of assumptions for the key driving forces to ensure consistency across the board and facilitate cross-cutting analysis. This reference set builds on the socio-economic assumptions developed for the DG TREN baseline projections 'European energy and transport trends to 2030', which are also being used within the Clean Air for Europe (CAFE, DG ENV) programme. Within this framework, assumptions have been developed as a consistent set and cover the following key driving forces:

• population
• macro and macro-economic activity
• household expenditure
• number of households
• average household size
• energy flows.

Population:

The European population is expected to stabilize, but gradually to become an ageing society. Main demographical trends are presented in the Table 1.

Table 1 Demography - population development 1990 - 2030

The age distribution in the EU is a growing concern, particularlyin connection with pension and health expenditure and working life-time. While the accession of the 10 new Member States in 2004 has somewhat rejuvenated the EU population, it failed to reserve the trend of increasing old age dependency from 30% in the 1960s to 39% today in the EU-25.

This trend is expected to continue over the 2000-2030 period, with the share of people of 65 years and older in the total population increasing from 15% to 25% in the EU-15, and from 10% to 22% in the New-10.

 The macro-economic assumptions.

The macro-economic assumptions for Europe are moderately optimisticand entail challengingtrade-offs in light of achieving sustainableeconomic development. Average annual economic growth in the EU is expected to be 2.4% and 3.5% in the New-10.

GDP assumptions are presented in the table 2.

Table 2 Income - GDP growth 2000 - 2030

 Technological developments:

Technological progress is moderate but essential in key area such as energy, agriculture and water, but no technological breakthroughs are assumed.

More detailed information concerning technology can be found in the European Environment Outlook N4/2005 (pp. 22-23).

 Sectoral developments

The service sector is expectedto retain its predominance in the European economy and be instrumental insustaing economic growth. The baseline scenario uses specific technological assumptions at the sectoral level, which directly affect most of European environmental concerns. The explanationsof such asumptions are available in the European Environment Outlook N4/2005 (pp. 23-24).

 'Low GHG emission' scenario

The scenario has been developed as an alternative scenario, which aims at identifying the implications of the EU's long term sustainable objective as stated in the 6th EAP for future GHG patterns, sectoral developments and the costs of policies. The scenario bears similarities with current initiatives, studies and political debate across the EU for far-reaching climate change policies.

One of the main assumptions are shifts induced by the low GHG emissions scenario. It is concerning the power generation sector in terms of fuel inputs. In particular, the share of solids is significantly reduced, and there is a greater deployment of renewables.

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

Rationale uncertainty

Atmospheric concentrations of greenhouse gases are a well-established indicator of changes in atmospheric composition, which causes changes of the global climate system. To answer the key question of the indicator (will GHG concentrations stay within sustainable limits) it is not enough to know only current concentration trends - projection techniques should be applied to warn on early stages how the situation is developing.