11.10.2 Quantifying Uncertainties

11.10.2.1 Sources of Regional Uncertainty

Most sources of uncertainty at regional scales are similar to those at the global scale (Section 10.5 and Box 10.2), but there are both changes in emphasis and new issues that arise in the regional context. Spatial inhomogeneity of both land use and land cover change (De Fries et al., 2002; Chapter 2, Section 7.2 and Box 11.4) and aerosol forcing adds to regional uncertainty. When analysing studies involving models to add local detail, the full cascade of uncertainty through the chain of models has to be considered. The degree to which these uncertainties influence the regional projections of different climate variables is not uniform. An indication of this is, for example, that models agree more readily on the sign and magnitude of temperature changes than of precipitation changes.

The regional impact of these uncertainties in climate projections has been illustrated by several authors. For example, incorporating a model of the carbon cycle into a coupled AOGCM gave a dramatically enhanced response to climate change over the Amazon Basin (Cox et al., 2000; Jones et al., 2003) and Borneo (Kumagi et al., 2004). Further, the scale of the resolved processes in a climate model can significantly affect its simulation of climate over large regional scales (Pope and Stratton, 2002; Lorenz and Jacob, 2005). Frei et al. (2003) show that models with the same representation of resolved processes but different representations of sub-grid scale processes can represent the climate differently. The regional impact of changes in the representation of the land surface feedback is demonstrated by, for example, Oleson et al. (2004) and Feddema et al. (2005) (see also Box 11.4).

Evaluation of uncertainties at regional and local scales is complicated by the smaller ratio of the signal to the internal variability, especially for precipitation, which makes the detection of a response more difficult. In addition, the climate may itself be poorly known on regional scales in many data-sparse regions. Thus, evaluation of model performance as a component of an analysis of uncertainty can itself be problematic.