IMAGE framework summary/Data uncertainties limitations

Projects/Applications

Roads from Rio+20 (2012) project

Relevant overviews

Framework overview Key policy issues overview Policy interventions and components overview

Key publications

PBL, 2012 Stehfest et al., 2014

References

FAO, 2013a Hoogwijk, 2004 IEA, 2012 IPCC-DDC, 2007 JRC/PBL, 2012 Lucas et al., 2007 Mulders et al., 2006 USGS, 2000

Data, uncertainty and limitations

Data

Many IMAGE components rely on a number of key data sources. The main data sources are listed below and described in the pages for the respective IMAGE components.

Main data sources for the IMAGE model

Categories and main data sources

• Energy (see (see Component Drivers):
  • International Energy Agency (IEA, 2012);
  • fossil fuel resources (USGS, 2000; Mulders et al., 2006);
  • renewable energy resources (Hoogwijk, 2004);
  • various sources on technology assumption.
• Land use and agricultural production/consumption:
  • Data on national crop and livestock production, agricultural yields, and land resources from FAO
• Emissions:
  • EDGAR database (JRC/PBL, 2012)
• Climate data (Historic climate data):
  • CRU global climate dataset;
  • AR4 data repository (IPCC-DDC, 2007)
• Costs data climate policy (other than energy)
  • Lucas et al., 2007

Uncertainties

Systematic uncertainty analyses have been performed on the individual IMAGE models. In addition, IMAGE has been assessed in model comparison projects (e.g., AGMIP via MAGNET (Von Lampe et al., 2014). These studies also contribute to understanding key uncertainties, as the experiments in these projects tend to be set up in the form of sensitivity runs, in which comparison with other models provides useful insights. An overview of key uncertainties in the IMAGE framework is presented below.

Overview of key uncertainties in the IMAGE framework

Model components and uncertainty

• Drivers:
  • Overall population size, economic growth;
• Agricultural systems:
  • Yield improvements, meat consumption, total consumption rates;
• Energy systems:
  • Preferences, energy policies, technology development, resources;
• Emissions:
  • Emission factors, in particular those in energy system;
• Land cover / carbon cycle:
  • Intensification versus expansion, effect of climate change on soil respiration, CO₂ fertilization effect;
• N-cycle:
  • Nutrient use efficiencies;
• Water cycle:
  • Groundwater use, patterns of climate change;
• Climate system:
  • Climate sensitivity, patterns of climate change;
• Biodiversity:
  • Biodiversity effect values, effect of infrastructure and fragmentation,

Limitations

The IMAGE model is relatively strong in the representation of the physical world in the Earth system, and the resource and technology selection in the Human system. A high level of integration has been achieved of these systems, with key parameters exchanged in different parts of the model (e.g. bioenergy, temperature feedbacks on crop production and the energy system, consistent treatment of climate policy and a consistent model for land cover and the carbon and water cycle).

However, there are also several limitations to the model:

• The economy is represented separately in different model components, notably in the agriculture and energy models with monetary feedback not well represented in the energy model. This implies that the model is better adapted for long-term trends than for short-term issues, and is not suitable to assess detailed economic impacts, such as sector level impacts.
• A model run starts in 1970, which implies that 2010 is model output. The model is calibrated against historical data up to 2005 and to 2010, depending on the module, which has implications for applications that use IMAGE output for the 2010-2020 period (for instance, evaluation of 2020 policies by FAIR).
• By design, the model is aggregated to allow for global coverage and a long time horizon, while keeping run times in check. Detailed, differentiated processes at local scale and national policies are represented as part of global region trends, without taking into account country-specific measures and processes.
• The physical orientation implies that the model is well adapted to study technical measures to achieve policy goals, but less so to study specific policies. Some policies, such as a carbon tax, can be represented but others, such as R&D policies, cannot. The model has no representation of governance systems, which tend to be handled as exogenous (variant) scenario parameters serving as proxies.