There are several levels of uncertainty in the generation of regional climate change information. The first level, which is not dealt with in this chapter, is associated with alternative scenarios of future emissions, their conversion to atmospheric concentrations and the radiative effects of these (see Chapter 13). The second level is related to the simulation of the transient climate response by AOGCMs for a given emission scenario (see also Chapters 8 and 9). This uncertainty has a global aspect, related to the model global sensitivity to forcing, as well as a regional aspect, more tied to the model simulation of general circulation features. This uncertainty is important both, when AOGCM information is used for impact work without the intermediate step of a regionalisation tool, and when AOGCM fields are used to drive a regionalisation technique. The final level of uncertainty occurs when the AOGCM data are processed through a regionalisation method.
Sources of uncertainty in producing regional climate information are of different nature. On the modelling and statistical downscaling side, uncertainties are associated with imperfect knowledge and/or representation of physical processes, limitations due to the numerical approximation of the model's equations, simplifications and assumptions in the models and/or approaches, internal model variability, and inter-model or inter-method differences in the simulation of climate response to given forcings. It is also important to recognise that the observed regional climate is sometimes characterised by a high level of uncertainty due to measurement errors and sparseness of stations, especially in remote regions and in regions of complex topography. Finally, the internal variability of the global and regional climate system adds a further level of uncertainty in the evaluation of a climate change simulation.
Criteria to evaluate the level of confidence in a regional climate change simulation can be based on how well the models reproduce present day climate or past climates and how well the climate change simulations converge across models and methods (see Chapters 8 and 9). These criteria will be drawn upon in evaluating available simulations. We add that the emerging activity of seasonal to interannual climate forecasting, particularly at the regional scale, may give valuable insights into the capability of models to simulate climatic changes and may provide objective methodologies for evaluating the long-term prediction performance of climate models at the regional scale.
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