IMAGE framework summary/Data uncertainties limitations
| Projects/Applications |
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| Key publications |
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Data, uncertainty and limitations
Data
Obviously, the many components of IMAGE rely on a range of different data sources. In the chapters 3 through 7 in this report these are described in more detail. Here, a selection of the most important data sources per category are:
- Energy: International Energy Agency Hoogwijk et al., Rogner
- Land use and agricultural production/consumption: FAO
- Emissions: EDGAR
- Climate data: DDC? ISIMIP?
Uncertainty analysis
Systematic uncertainty analyses have been performed at the level of individual submodels of IMAGE. In addition, the model has participated in many model comparison projects (EMF, AMPERE ). These also contribute to understanding and mapping uncertainty, as these project tend to be set up in the form of sensitivity experiments while comparison with other models provides a useful reference. Based on the results of the individual experiments the following key uncertainties can be identified.
Table 2: Overview of key uncertainties . Model component Uncertainty Drivers Overall population size, economic growth Food production and consumption Yield improvements, meat consumption, total consumption rates Energy system Preferences, energy policies, technology development, resources Emissions Emission factors – in particular those in energy system Land cover / carbon cycle Growth rates of different ecosystems N-cycle Water cycle Climate system Climate sensitivity, patterns of climate change Biodiversity Impact – biodiversity relationships
Limitations The IMAGE model is relatively strong in its representation of the physical world, i.e. both in terms of the earth system as well as the resource and technology selection in the human system. A high level of integration has been achieved for these systems, with key parameters being exchanged across the different parts of the model (e.g. bio-energy, temeperature feedbacks on crop production and the energy system, consistent treatment of climate policy and a consistent model for land cover and the carbon cycle). However, there are also important limitations to the model: • The economy is represented separately in different model components, and in most submodels financial feedbacks are poorly represented. This implies that the model is better adapted for long-term trends than for short-term questions and also that the model is not suitable to study more detailed economic impacts, such as for instance sector impacts) • The model runs start in 1970, which implies that 2010 is model output. Although the model is calibrated against historical data up to 2005, the representation of historical data is obviously not perfect, nor is the extension based on major trends until 2010 . This has implications for short-term applications. By design, the model is rather aggregated to allow for global coverage and a long time horizon, while keeping run times in check. Detailed, differentiated processes at local scale, and also national policies are typically represented as part of global region trends, ignoring country specific measures. • The physical orientation of the model implies that it is well adapted to study technical measures to achieve certain policy goals, but less well to study specific policies. Some can be represented such as a carbon tax, but others including R&D policies not. Also the model has no representation of governance systems, and these tend to be handled as exogenous (variant) scenario parameters serving as proxies.