GC41H-05
Towards Quantifying Robust Uncertainty Information for Climate Change Decision-making
Thursday, 17 December 2015: 09:00
3001 (Moscone West)
Chris E Forest, Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, United States; The Pennsylvania State University,, Meteorology & Geosciences, University Park, PA, United States, Alex G Libardoni, Pennsylvania State University Main Campus, Meteorology, University Park, PA, United States, Chii-Yun Tsai, Pennsylvania State University Main Campus, University Park, PA, United States, Andrei P Sokolov, Massachusetts Institute of Technology, Joint Program on the Science and Policy of Global Change, Cambridge, MA, United States, Erwan Monier, Massachusetts Institute of Technology, Center for Global Change Science, Cambridge, MA, United States, Ryan L Sriver, University of Illinois at Urbana Champaign, Urbana, IL, United States and Klaus Keller, Carnegie Mellon University, Engineering and Public Policy, Pittsburgh, PA, United States
Abstract:
The expected future impacts of climate change can be a manageable problem provided the risks to society can be properly assessed. Given our current understanding of both the climate system and the related decision problems, we strive to develop tools that can assess these risks and provide robust strategies given possible futures. In this talk, we will present two examples from recent work ranging from global to regional scales to highlight these issues. Typically, we begin by assessing the probability of events without information on impacts specifically, however, recent developments allow us to address the risk management problem directly. In the first example, we discuss recent advances in quantifying probability distributions for equilibrium climate sensitivity (ECS). A comprehensive examination of factors all contributing to the total uncertainty in ECS can include updates to estimates of observed climate changes (oceanic, atmospheric, and surface records), improved understanding of radiative forcing and internal variability, revised statistical calibration methods, and overall longer records. In a second example, we contrast the assessment of probabilistic information for global scale climate change with that for regional changes. The relative importance of model structural uncertainty, uncertainty in future forcing, and the role of internal variability will be compared within the context of the decision making problem. In both cases, robust estimates of uncertainty are desired and needed… but surprises happen. Incorporating these basic issues into robust decision making frameworks is a long-term research goal with near-term implications.
