There are two main uncertainties in determining future climate: the trajectories of future emissions of greenhouse gases and aerosols, and the response of the global climate system to any given set of future emissions (Meehl et al., 2007). These uncertainties are normally explored through the use of global climate models, which provide information at relatively coarse spatial resolutions. The application of regional climate models that have been nested in the global models presents another source of uncertainty, that associated with the spatial scale of the simulations. In this program we focus on understanding the uncertainty inherent to the global models and the regional models, and do not directly concern ourselves with the uncertainty related to the future emissions scenarios.

While we use 4 different AOGCMs to drive 6 different regional models, we do not simulate all 24 possible combinations, but rather only 12, sampling the full 4x6 matrix using a fractional factorial design. Each RCM uses boundary conditions from half of the global models, and each global model provides boundary conditions to half of the RCMs:

While the 4 AOGCMs used form a relatively small subset of the total set of 23 that produced climate change simulations for the Forth Assessment Report of the IPCC, we are able to place these four simulations of climate change in the context of the full suite of global climate models using probabilistic statistical techniques (e.g., see Tebaldi et al., 2004; 2005). Then the regional model simulations, which essentially branch off from the AOGCMs used to drive the RCMs, can also be placed in the probabilistic context. More details on the development of the probabilistic methods will be forthcoming as they are developed. The 12 scenarios themselves provide a simple and straightforward measure of uncertainty that is still potentially very useful. For example, in a climate impacts study, all 12 scenarios could be used in an impact model (e.g., a water resource or crop model) to determine the potential range of impacts. The eventual availability of probability distributions for the full suite of experiments will also prove useful for policy analysis, impacts analysis and so forth.

References

Meehl, G. et al., 2007: Global Climate Projections. In Solomon et al. (eds.), Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the IPCC. Cambridge University Press: Cambridge, 996 pgs.

Tebaldi, C., R. Smith, D. Nychka, and L. O. Mearns, 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multi-model ensembles. J. Climate 18:1524-1540.

Tebaldi, C., L. O. Mearns, R. Smith, D. Nychka, 2004: Regional probabilities of precipitation change: A Bayesian approach. Geophys. Res. Lett. 31:L24213, doi:10.1029/2004GL021276.