Temperature and Precipitation Change PDFs and CDFs
We have developed a Bayesian statistical model which compares multiple AOGCM's to historical data and each other, to determine a statistical distribution of a region's future climate (in this case, seasonal absolute changes in temperature and relative changes in precipitation). The data from this Bayesian model is too coarse to be of much direct use in impact assessment science, but it can and will be used to derive higher resolution, watershed-scale climate data using a k-nearest neighbor (k-nn) downscaling technique.
We make available detailed Probability Distribution Functions (PDFs) and Cumulative Distribution Functions (CDFs) representations of temperature and precipitation changes for all regions, under three SRES scenarios, based on model output from all the AOGCMs contributing data to the IPCC-AR4 archive.
The ranges of these probabilistic summaries of multi-model projections should be seen as conservative estimates of the ranges of future changes for at least two reasons: (1) the IPCC-AR4 ensemble of AOGCMs spans a very limited (and central) range of climate sensitivities; (2) we assume that the natural variability in the future is the same as the one estimated from the current climate. Also, please note that each of the PDF gives projections conditionally on a particular SRES scenario. We do not address the relative likelihood of the different emission scenarios in this work.
Plots of CDF and PDF for various world regions are available below. For more data, analyses of various subregions in the United States, and custom analyses, please go to http://rcpm.ucar.edu.