A Probability and Decision-Model Analysis of a Multimodel Ensemble of Climate Change Simulations

Abstract

Because of the inherent uncertainties in the computational representation of climate and because of unforced chaotic climate variability, it is argued that climate change projections should be expressed in probabilistic form. In this paper, 17 Coupled Model Intercomparison Project second-phase experiments sharing the same gradual increase in atmospheric CO2 are treated as a probabilistic multimodel ensemble projection of future climate. Tools commonly used for evaluation of probabilistic weather and seasonal forecasts are applied to this climate change ensemble. The probabilities of some temperature- and precipitation-related events defined for 20-yr seasonal means of climate are first studied. A cross-verification exercise is then used to obtain an upper estimate of the quality of these probability forecasts in terms of Brier skill scores, reliability diagrams, and potential economic value. Skill and value estimates are consistently higher for temperature-related events (e.g., will the 20-yr period around the doubling of CO2 be at least 1°C warmer than the present?) than for precipitation-related events (e.g., will the mean precipitation decrease by 10% or more?). For large enough CO2 forcing, however, probabilistic projections of precipitation-related events also exhibit substantial potential economic value for a range of cost–loss ratios. The treatment of climate change information in a probabilistic rather than deterministic manner (e.g., using the ensemble consensus forecast) can greatly enhance its potential value.