Uncertainties in climate change projections, from the global to the regional scale

Abstract

A discussion is presented of the different sources of uncertainty in the production of climate change projections at the global to the regional scale. In particular the following uncertainty sources are identified and discussed: greenhouse gas (GHG) emission/concentration scenario, model configuration (or intra-model) and bias, internal unforced variability due to the non-linearities of the climate system, and downscaling uncertainty. Specific examples are presented to intercompare the importance of these sources of uncertainty, which depends on different factors, such as the time horizon of the projection, the variable under consideration and the scale of interest. In general, scenario and model configuration uncertainty dominate for long term climate change, especially at the global scale. The contribution of internal variability increases for near term projections and for higher order climate statistics. Downscaling uncertainty is significant for variables primarily affected by local processes, such as summer convective precipitation. It is argued that because of these sources of uncertainty, the climate prediction problem should be addressed in a probabilistic, rather than deterministic way. The discussion is placed within the context of the identification of two categories of uncertainty source, the Knowledge Uncertainty due to our imperfect knowledge and representation of the problem, and the Intrinsic Uncertainty inherent to the problem. While the former should be reduced with improved science, the latter should be characterized to the largest possible extent to account for all possible outcomes.