19 Mar Addressing partial identification in climate modeling and policy analysis
Proceedings of the National Academy of Sciences, Vol. 118, No. 15 (2021). https://www.pnas.org/content/118/15/e2022886118.
Co-authors: Charles F. Manski, Alan H. Sanstad, and Stephen J. DeCanio
Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including inter-model “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multi-model ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose instead framing climate model uncertainty as a problem of partial identification, or “deep” uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research.
Incomplete scientific understanding of the climate and challenges in numerical computation have resulted in numerous climate simulation models being developed and used to generate ensembles, or sets, of climate projections as functions of single greenhouse gas (GHG) emissions scenarios. We propose a “deep uncertainty” approach to framing and analyzing these ensemble model outputs that allows their use in climate-related decision-making without reliance on problematic model weighting schemes. We provide a theoretical framework and an illustrative numerical model to show how single, nearly-optimal decisions can be made regarding GHG abatement without requiring single, average climate model projections.