A pair of projects is presented with the goal of improving understanding of future climate change in California. The first project assesses the ability of regional climate models and statistical downscaling to simulate the present climate in the State. This will provide understanding of the strengths and weaknesses of competing models and approaches. The second project provides probabilistic projections of future climate for use by decision-makers, impacts researchers, and others. The uncertainties in these projections will reflect uncertainty in knowledge of future climate perturbations (e.g., greenhouse gas emissions) as well as uncertainty in the response of the climate system. For the first (present climate) project, three options are presented which differ in the level of funding required ($500,000 to $2 million) and in the quantity and quality of information expected to result. The future-climate project assumes the existence of present climate simulations, which are needed as baselines. Thus, this project assumes that one of the more comprehensive present-climate project options will have been completed.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.