Author(s): Hall JW, Fu G, Lawry J
Abstract: 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 socio-economic 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. We think of socio-economic scenarios as fuzzy linguistic constructs. Any precise emissions trajectory (which is required for climate modelling) can be thought of as having a degree of membership in a fuzzy scenario. Rather than attempting to distribute an additive probability measure across scenarios a weaker assumption is adopted in monotonic (but non-additive) measures. We argue that this approach can capture some of the semantics of socio-economic scenarios that defy conventional probabilistic representation. It is demonstrated how fuzzy scenarios can be propagated through a low-dimensional climate model, MAGICC. Fuzzy scenario uncertainties and imprecise probabilistic representation of climate model uncertainties are combined using random set theory to generate lower and upper cumulative probability distributions for Global Mean Temperature. In a decision-making context this information will lead to identification of sets of options that are robust to uncertainty, avoiding the naïve optimizing behaviour that can be implied by conventional probabilistic decision theory.