Climate policymaking and risk assessment sit at the confluence of deeply uncertain physical, social, and economic systems. Substantial uncertainty is introduced, propagated, and transformed at every stage of the climate-impact-decision chain: from projections and emulators, through sector-specific impact models, including hydrological, agricultural, and flood-loss models, to integrated assessment, energy-system, economic, financial, and insurance risk models. Yet these outputs increasingly inform decisions with long-term consequences. Understanding how uncertainty is introduced, propagated, transformed, and communicated across this chain is therefore critical for assessing the robustness of the information on which decisions are based.

This session invites contributions that advance the understanding and quantification of uncertainty across climate impacts, risk assessment, and decision-making. We welcome a range of approaches, including Monte Carlo methods, emulators, robust decision-making, sensitivity analysis, and Bayesian methods, as well as work on structural model uncertainty and inter-model comparison. We are particularly interested in studies examining how uncertainty within individual models is propagated and transformed across successive stages of an analysis, and how it can be communicated to policymakers, financial institutions, and the public in ways that support sound decision-making.

We particularly encourage submissions that bridge disciplinary boundaries among physical climate science, impact modeling, economics, decision theory, and science communication, as well as case studies examining how uncertainty is handled in real-world climate policymaking and in financial or insurance risk assessment. The session focuses primarily on analyses at regional to global scales. Sub-national case studies are also welcome when they provide insights into uncertainty relevant to broader-scale risk assessment or decision-making.