Improving Regional Climate Projections by Prioritized Aggregation via Ordered Weighted Averaging Operators

摘要

Decision makers express a strong need for reliable information on future climate changes to develop with the best mitigation and adaptation strategies to address impacts. These decisions are based on future climate projections that are simulated by using different Representative Concentration Pathways (RCPs), General Circulation Models (GCMs), and downscaling techniques to obtain high-resolution Regional Climate Models. RCPs defined by the Intergovernmental Panel on Climate Change entail a certain combination of the underlying driving forces behind climate and land use/land cover changes, which leads to different anthropogenic Greenhouse Gases concentration trajectories. Projections of global and regional climate change should also take into account relevant sources of uncertainty and stakeholders' risk attitudes when defining climate polices. The goal of this article is to improve regional climate projections by their prioritized aggregation through the ordered weighted averaging (OWA) operator. The aggregated projection is achieved by considering the similarity of the projections obtained by combining different GCMs, RCPs, and downscaling techniques. Relative weights of different projections to be aggregated by the OWA operator are obtained by regular increasing monotone fuzzy quantifiers, which enables modeling the stakeholders' risk attitudes. The methodology provides a robust decision-making tool to evaluate performance of future climate projections and to design sustainable policies under uncertainty and risk tolerance, which has been successfully applied to a real-case study.