ity of a grid cell for agricultural expansion include potential crop yield (which changes over time as a result of climate change and technology development), proximity to other agricultural areas and proximity to water bodies. The land cover model also includes a modified version of the BIO ME model (Prentice et al., 1992) to compute changes in poten­tial vegetation. The potential vegetation is the equilibrium vegetation that should eventually develop under a given cli­mate. The shifts in vegetation zones, however, do not occur instantaneously. In IMAGE 2.4 such dynamic adaptation is modelled explicitly according to the algorithms developed by Van Minnen et al. (2000). This allows for assessing the consequences of climate change for natural vegetation (Lee-mans and Eickhout, 2004).The land use system is modelled on a 0.5 by 0.5 degree grid.
     Both changes in energy consumption and land use pat­terns give rise to emissions that are used to calculate changes in the atmospheric concentration of greenhouse gases and some atmospheric pollutants such as nitrogen and sulphur oxides (Strengers et al., 2004). Changes in the concentration of greenhouse gases, ozone precursors and species involved in aerosol formation form the basis for calculating climatic change (Eickhout et al., 2004). Next, changes in climate are calculated as global mean changes which are downscaled to the 0.5 by 0.5 degree grids using patterns generated by a General Circulation Model (GCM). Through this approach, different GCM patterns can be used to downscale the glo­bal-mean temperature change, allowing for the assessment of uncertainties in regional climate change (Eickhout et al., 2004). An important aspect of IMAGE is that it accounts for crucial feedbacks within the system, such as among tem­perature, precipitation and atmospheric CO2 on the selec­tion of crop types and the migration of ecosystems. This allows for calculating changes in crop and grass yields and as a consequence the location of different types of agricul­ture, changes in net primary productivity and migration of natural ecosystems (Leemans et al., 2002).

A.5.2.3 Application
The IMAGE model has been applied to a variety of global studies. The specific issues and questions addressed in these studies have inspired the introduction of new model fea­tures and capabilities, and in turn, the model enhancements and extensions have broadened the range of applications that IMAGE can address. Since the publication of IMAGE 2.1 (Alcamo et al., 1998), subsequent versions and interme­diate releases have been used in most of the major global assessment studies and other international analyses, like the IPCC Special Report on Emissions Scenarios (Nakicenovic et al., 2000), UNEP's Third and Fourth Global Environment Outlook (UNEP, 2002; 2007 ), The Millennium Ecosystem Assessment (MA, 2006), the Second Global Biodiversity Outlook (SCBD/MNP, 2007) and Global Nutrients from Watersheds (Seitzinger et al., 2005).

A.5.2.4 Uncertainty
As a global Integrated Assessment model, the focus of IM­AGE is on large scale, mostly first order drivers of global environmental change. This obviously introduces some im­portant limitations to its results, and in particular the in­terpretation of its accuracy and uncertainty. An important method for handling some of the uncertainties is by using a scenario approach. A large number of relationships and model drivers whose linkages and values are either current­ly not known or depend on human decisions are varied in these scenarios. To explore their uncertainties, see IMAGE Team, 2001. In 2001 a separate project was performed to evaluate the uncertainties in the energy model using both quantitative and qualitative techniques. With this analy­sis the model's most important uncertainties were seen to be linked to its assumptions for technological improve­ment in the energy system, and how human activities are translated into a demand for energy (including human life­styles, economic sector change and energy efficiency, seen in Table A.5.3).