Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. The predictions must account for climate-change mitigation policies which themselves may depend on climate predictions. This interdependency, or “circularity”, means that physical climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range climate forecasts also suffer from information asymmetry because users cannot rely on track records to judge the skill of providers.
The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with joint-outcome spaces that allow simultaneous forecasts of GHG concentrations and global temperature. The viability of prediction markets with the necessary highly granular, joint-outcome spaces was tested with markets for monthly UK rainfall and temperature. The experiments demonstrate that these markets can aggregate the judgments of experts with relevant expertise, and they indicate that similarly structured markets, with longer prediction horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and the disclosure of these risks.