IMAGE framework/Uncertainties: Difference between revisions
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|Reference=Van Vuuren, 2007; | |||
|Description=Obviously, the scientific quality as described above, and treatment of uncertainty are a constant and important concern. While we describe uncertainties and limitations of all IMAGE modules in [[chapters 3-8]], and for the entire model in the [[IMAGE framework summary]], we reflect here on the various aspects of uncertainty, and how they are treated in IMAGE: | |||
* 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 on land use and land-use change. Also other key input data like soil maps, temperature and precipitation maps are uncertain, although continuously improved data sets are prepared. Mostly, this uncertainty is not addressed explicitly, but the best available data are used and eventually harmonized with other modelling teams and partners. Structural, methodological uncertainty (incomplete knowledge of relationships) is still large for many parts of the IMAGE model, e.g. for the impact of Climate change on crop yields, or for local climate change. This uncertainty can be addressed to a certain extent by the possibility to accommodate alternative model formulations, e.g. for crop growth/natural vegetation, carbon cycle, land-use allocation, climate change (via climate sensitivity and temperature/precipitation patterns). While IMAGE strives to include state-of-the art models for all parts of the framework, the size of the uncertainties might rather call for reduced-form models being able to capture the uncertainty range (see further below). | |||
* Another way to address structural uncertainty is by participating in model inter-comparison studies and other multi-model studies, to gain insights on how IMAGE results compare to those from other models. Whether they are well inside the range of outcomes, or at the high or low end, provides information on the functioning of the model and how representative the results are of ranges found in literature. | |||
* The overall model uncertainty arising from uncertain processes and data can be assessed by performing more or less systematic sensitivity analyses. This was done among others for the CO2-fertilization factor in crop- and natural vegetation growth and for many parameters of the energy model [[TIMER model|TIMER]] ([[Van Vuuren, 2007]]). | |||
* Uncertainty in future scenario drivers like population, economic growth, and technology is most commonly addressed by exploring variants around assumed reference pathways, e.g. high/low variants of population projections, or by assuming contrasting future scenarios (without a specific reference). Likewise, uncertainty in policy targets and societal trends are addressed by exploring alternative scenarios, varying one or more key input parameters such as learning-by-doing parameters composition of human diets, or other lifestyle choices. | |||
* A specific aspect of uncertainty is presented by the level of aggregation, with socio-economic processes being represented by 26 regions, and terrestrial biosphere being modelled at 5 or 30 minute grid cells. At both levels, the region and the grid cell, all behaviour is average behaviour, ignoring all heterogeneity within a region (on income distribution, economy, farming systems, etc), or at a grid cell (on climate, soils, landscape composition). Big differences between countries within a specific world region are masked and all future trends apply to the average, although countries may develop on different pathways. Therefore, interpretation of land use on a country or sub-country level, although possible from a 5 min map must be done with due care. | |||
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Revision as of 11:42, 12 December 2013
Parts of IMAGE framework/Uncertainties
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