Probabilistic thinking can often be unintuitive. This is the case even for simple problems, let alone the more complex ones arising in climate modelling, where disparate information sources need to be combined. The physical models, the natural variability of systems, the measurement errors and their dependence upon the observational period length should be modelled together in order to understand the intricacies of the underlying processes. We use Bayesian networks (BNs) to connect all the above-mentioned pieces in a climate trend uncertainty quantification framework. Inference in such models allows us to observe some seemingly nonsensical outcomes. We argue that they must be pondered rather than discarded until we understand how they arise. We would like to stress that the main focus of this paper is the use of BNs in complex probabilistic settings rather than the application itself.@en

The goal of this study is to show how to quantify the benefits of accelerated learning about key parameters of the climatic system and use this knowledge to improve decision-making on climate policy. The US social cost of carbon (SCC) methodology is used in innovative ways to value new Earth observing systems (EOSs). The study departs from the strict US SCC methodology, and from previous work, in that net benefits are used instead of only damages to calculate the value of information of the enhanced systems. In other respects the US SCC methodology is followed closely. We compute the surfeit expected net benefits of learning the actionable information earlier, with the enhanced system, versus learning later with existing systems. The enhanced systems are designed to give reliable information about climate sensitivity on accelerated timescales relative to existing systems; therefore, the decision context stipulates that a global reduced emissions path would be deployed upon receiving suitable information on the rate of temperature rise with a suitable level of confidence. By placing the enhanced observing system in a decision context, the SCC enables valuing this system as a real option. Policy relevance Uncertainty in key parameters of the climatic system is often cited as a barrier for near-term reductions of carbon emissions. It is a truism among risk managers that uncertainty costs money, and its reduction has economic value. Advancing policy making under uncertainty requires valuing the reduction in uncertainty. Using CLARREO, a new proposed EOS,as an example, this article applies value of information/real option theory to value the reduction of uncertainty in the decadal rate of temperature rise. The US interagency social cost of carbon directive provides the decision context for the valuations. It is shown that the real option value of the uncertainty reduction, relative to existing observing systems, is a very large multiple of the new system's cost.@en

The goal of this study is to show how to quantify the benefits of accelerated learning about key parameters of the climatic system and use this knowledge to improve decision-making on climate policy. The US social cost of carbon (SCC) methodology is used in innovative ways to value new Earth observing systems (EOSs). The study departs from the strict US SCC methodology, and from previous work, in that net benefits are used instead of only damages to calculate the value of information of the enhanced systems. In other respects the US SCC methodology is followed closely. We compute the surfeit expected net benefits of learning the actionable information earlier, with the enhanced system, versus learning later with existing systems. The enhanced systems are designed to give reliable information about climate sensitivity on accelerated timescales relative to existing systems; therefore, the decision context stipulates that a global reduced emissions path would be deployed upon receiving suitable information on the rate of temperature rise with a suitable level of confidence. By placing the enhanced observing system in a decision context, the SCC enables valuing this system as a real option. Policy relevance Uncertainty in key parameters of the climatic system is often cited as a barrier for near-term reductions of carbon emissions. It is a truism among risk managers that uncertainty costs money, and its reduction has economic value. Advancing policy making under uncertainty requires valuing the reduction in uncertainty. Using CLARREO, a new proposed EOS,as an example, this article applies value of information/real option theory to value the reduction of uncertainty in the decadal rate of temperature rise. The US interagency social cost of carbon directive provides the decision context for the valuations. It is shown that the real option value of the uncertainty reduction, relative to existing observing systems, is a very large multiple of the new system's cost.@en