Understanding the sources and consequences of uncertainty in climate and sectoral models results is crucial for robust adaptation and mitigation decisions that influence integrated regional modeling. Within PRIMA, PNNL scientists are developing and demonstrating rigorous and tractable methods for uncertainty characterization (UC), including uncertainty quantification and propagation.
A key insight from this work is the benefit derived from bounding the UC process by the context of the stakeholder question. Defensibly characterizing uncertainty across all aspects of the PRIMA framework simultaneously is infeasible due to the sheer magnitude of the parameters and couplings involved. However, it is realistic to address the uncertainties relevant to a particular stakeholder decision by focusing on the relevant PRIMA component models and parameters affecting that decision. As a result, PRIMA’s demonstration activities rely heavily on stakeholder and decision support research.
Scott MJ, DS Daly, Y Zhou, JS Rice, PL Patel, HC McJeon, GP Kyle, SH Kim, J Eom, and LE Clarke. 2014. “Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technological, and socioeconomic futures in a regional integrated-assessment model.” Energy Economics 43:22-33. DOI: 10.1016/j.eneco.2014.01.012.
Moss R. 2011. “Reducing Doubt about Uncertainty: Guidance for IPCC’s Third Assessment.” Climate Change 108(4):641-658. DOI: 10.1007/s10584-011-0182-x.