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

Llopis-Albert, C; Merigó, JM; Xu, Y; Liao, H

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

Llopis-Albert, C Merigó, JM

Xu, Y Liao, H

Permalink

Publication Type:: Journal Article
Citation:: Environmental Engineering Science, 2017, 34 (12), pp. 880 - 886
Issue Date:: 2017-12-01

Closed Access

	Filename	Description	Size
	ees.2016.0546.pdf	Published Version	308.54 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Llopis-Albert, C	en_US
dc.contributor.author	Merigó, JM https://orcid.org/0000-0002-4672-6961	en_US
dc.contributor.author	Xu, Y	en_US
dc.contributor.author	Liao, H	en_US
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	Environmental Engineering Science, 2017, 34 (12), pp. 880 - 886	en_US
dc.identifier.issn	1092-8758	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126400
dc.description.abstract	© Copyright 2017, Mary Ann Liebert, Inc. 2017. 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.	en_US
dc.relation.ispartof	Environmental Engineering Science	en_US
dc.relation.isbasedon	10.1089/ees.2016.0546	en_US
dc.subject.classification	Environmental Engineering	en_US
dc.title	Improving Regional Climate Projections by Prioritized Aggregation via Ordered Weighted Averaging Operators	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	34	en_US
utslib.for	0502 Environmental Science and Management	en_US
utslib.for	0907 Environmental Engineering	en_US
utslib.for	0904 Chemical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

© Copyright 2017, Mary Ann Liebert, Inc. 2017. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/126400