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      Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

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          Abstract

          We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variability of the underlying process, and to make weighted probabilistic projections. We validate the method using a suite of one-at-a-time cross-validation experiments involving Atlantic meridional overturning circulation (AMOC), its temperature-based index, as well as Korean summer mean maximum temperature. In these experiments the method tends to exhibit superior skill over a trend-only Bayesian model averaging weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean maximum temperature change over Korea by the end of the 21 st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields.

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          Most cited references49

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          An Essay towards Solving a Problem in the Doctrine of Chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S.

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            Robust Locally Weighted Regression and Smoothing Scatterplots

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              Stochastic climate models Part I. Theory

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                10 April 2019
                2019
                : 14
                : 4
                : e0214535
                Affiliations
                [1 ] Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
                [2 ] Center for Climate Physics, Institute for Basic Science, Busan, South Korea
                [3 ] Pusan National University, Busan, South Korea
                [4 ] School of Mathematics and Statistics, UNSW Australia, Sydney, New South Wales, Australia
                [5 ] Climate Change Research Centre and ARC Centre for Excellence in Climate Extremes, UNSW Australia, Sydney, New South Wales, Australia
                Universidade de Vigo, SPAIN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-8233-9467
                http://orcid.org/0000-0002-0003-429X
                Article
                PONE-D-18-32816
                10.1371/journal.pone.0214535
                6457553
                30969982
                d9d09e8f-7660-4bcb-9eff-e4bc04bcf096
                © 2019 Olson et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 November 2018
                : 14 March 2019
                Page count
                Figures: 8, Tables: 2, Pages: 24
                Funding
                Funded by: National research foundation of Korea
                Award ID: NRF-2018R1A5A1024958
                Award Recipient :
                Funded by: National Research Foundation of Korea
                Award ID: NRF-2017K1A3A7A03087790
                Award Recipient :
                RO and SIA were supported by Basic Science Research Program through National Research Foundation of Korea (NRF-2017K1A3A7A03087790, NRF-2018R1A5A1024958) ( http://www.nrf.re.kr/eng/main). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Mathematics
                Statistics
                Statistical Models
                Earth Sciences
                Seasons
                Summer
                Computer and Information Sciences
                Systems Science
                Dynamical Systems
                Physical Sciences
                Mathematics
                Systems Science
                Dynamical Systems
                Research and Analysis Methods
                Simulation and Modeling
                Climate Modeling
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Modeling
                Computer and Information Sciences
                Systems Science
                Nonlinear Dynamics
                Physical Sciences
                Mathematics
                Systems Science
                Nonlinear Dynamics
                Computer and Information Sciences
                Systems Science
                Nonlinear Systems
                Physical Sciences
                Mathematics
                Systems Science
                Nonlinear Systems
                Earth Sciences
                Seasons
                Winter
                Physical Sciences
                Materials Science
                Material Properties
                Surface Properties
                Surface Temperature
                Custom metadata
                The relevant data and code implementing the methodology and used to produce the results of the paper (including programs to make the figures) are available as a supplementary file S2_File.zip. Observational data used in this paper are freely available from third party websites, with details provided in the references. The Climate Model Intercomparison Project phase 5 model output is available from https://esgf-node.llnl.gov/projects/esgf-llnl/. Data access requires filling in a registration form at https://esgf-node.llnl.gov/user/add/. The results of the cross-validation and projection experiments, including the weights, the Markov chain Monte Carlo chains, the R programming language workspaces, and the control input files are available from the https://www.climatelab.yonsei.ac.kr/forum.

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