IMAGEIntegrated Model to Assess the Global Environment.

IMAGE framework/Uncertainties

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Uncertainties

Uncertainties and limitations with regard to IMAGE are described in 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 key data uncertainty is due to incomplete knowledge of historical time series of model data, for example on energy demand and supply, emissions, and land use and land-use change. Other key 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 harmonised 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 with results for ranges found in literature, and to 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 mostly 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, 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. At region and grid cell, all behaviour is average behaviour, not taking into account heterogeneity within a region (e.g., in income distribution, economy, farming systems), and at grid cell (e.g., climate, soils, and landscape composition). Major 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 with caution.