Beyond probability: new methods for representing uncertainty in projections of future climate

Please note that Tyndall working papers are "work in progress". Whilst they are commented on by Tyndall researchers, they have not been subject to a full peer review. The accuracy of this work and the conclusions reached are the responsibility of the author(s) alone and not the Tyndall Centre. Summary Whilst the majority of the climate research community is now set upon the objective of generating probabilistic predictions of climate change, disconcerting reservations persist. Attempts to construct probability distributions over socioeconomic scenarios are doggedly resisted. Variation between published probability distributions of climate sensitivity attests to incomplete knowledge of the prior distributions of critical parameters in climate models. In this paper we address these concerns by adopting an imprecise probability approach. The uncertainties considered in our analysis are from two sources: emissions scenarios and climate model uncertainties. For the former, we argue that emissions scenarios based on different views of social, economic and technical developments in the future that are expressed in terms of fuzzy linguistic narratives and therefore any precise emissions trajectory can be thought of as having a degree of membership between 0 and 1 in a given scenario. We demonstrate how these scenarios can be propagated through a simple climate model, MAGICC. Imprecise probability distributions are constructed to represent climate model uncertainties in terms of the published probability distributions of climate sensitivity. This is justified on the basis that probabilistic estimates of climate sensitivity are highly contested and there is little prospect of a unique probability distribution being collectively agreed upon in the near future. We then demonstrate how imprecise probability distributions of climate sensitivity can be propagated through MAGICC. Emissions scenario uncertainties and imprecise probabilistic representation of model uncertainties are combined to generate lower and upper cumulative probability distributions for Global Mean Temperature (GMT).