IMAGE framework/Uncertainties

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Relevant overviews


Limitations of IMAGE and critical uncertainties that influence results are described in the IMAGE framework summary and for each IMAGE component separately in the model component pages (via Framework overview). Generic aspects of uncertainty in IMAGE are outlined below.

Structural key data uncertainty

Structural fundamental data uncertainty is due to incomplete knowledge of historical time series of model data, such as energy demand and supply, emissions, land use, and land-use change. Other critical input data, such as soil maps and temperature and precipitation maps, are uncertain, but data sets are continuously improved. This uncertainty is not addressed explicitly, but the best data available are used and harmonized with other modelling teams and partners.

Structural and methodological uncertainty

There is structural and methodological uncertainty (incomplete knowledge of relationships) in many parts of the IMAGE framework, for instance, the impact of climate change on crop yields and local climate change. This uncertainty can be addressed to some extent by alternative model formulations, such as for crop growth/natural vegetation, carbon cycle, land-use allocation, climate change (via climate sensitivity and temperature/precipitation patterns). Structural uncertainty can also be addressed in model inter-comparison studies and other multi-model studies to compare IMAGE results with the range of outcomes from other models and results for ranges found in literature and provide information on model functioning (see Applications. The overall model uncertainty arising from uncertain processes and data can be assessed in systematic sensitivity analyses. This has been done, for example, on the CO2 fertilisation factor in crop and natural vegetation growth (Brinkman et al., 2005) and for many parameters of the energy model TIMER (Van Vuuren, 2007).

Uncertainty in future scenario drivers

Uncertainty in future scenario drivers, such as population, economic growth, and technology, is mainly addressed by exploring variants in assumed reference pathways, such as high/low variants of population projections or by assuming contrasting future scenarios. Similarly, uncertainty in policy targets and societal trends is addressed by exploring alternative scenarios, varying one or more key input parameters, such as learning-by-doing parameters, the composition of human diets, and other lifestyle choices.

Level of aggregation

A distinct source of uncertainty arises from the level of aggregation, with socio-economic processes represented by 26 regions and the terrestrial biosphere modelled at 5 or 30-minute grid cells. Behaviour modelled at the regional level is mainly described for averages, such as energy supply or food consumption. This does not consider heterogeneity within a region (e.g., in income distribution, economy, farming systems or different countries). For some parameters, socio-economic heterogeneity is considered – different income groups and rural/urban population. Significant differences between countries in a world region are masked, and all future trends apply to the average, although countries may develop along different pathways. Thus, land use on a country or sub-country level is possible on a 5-minute map but must be interpreted cautiously.