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Mutual information based weighted variance approach for uncertainty quantification of climate projections

Future climate projections are a vital source of information that aid in deriving effective mitigation and adaptation measures. Due to the inherent uncertainty in these climate projections, quantification of uncertainty is essential for increasing its credibility in policymaking. While quantifying t...

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Detalles Bibliográficos
Autores principales: Majhi, Archana, Dhanya, C.T., Chakma, Sumedha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958507/
https://www.ncbi.nlm.nih.gov/pubmed/36851983
http://dx.doi.org/10.1016/j.mex.2023.102063
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author Majhi, Archana
Dhanya, C.T.
Chakma, Sumedha
author_facet Majhi, Archana
Dhanya, C.T.
Chakma, Sumedha
author_sort Majhi, Archana
collection PubMed
description Future climate projections are a vital source of information that aid in deriving effective mitigation and adaptation measures. Due to the inherent uncertainty in these climate projections, quantification of uncertainty is essential for increasing its credibility in policymaking. While quantifying the uncertainty, often the possible dependency between the General Circulation Models (GCMs) due to their shared common model code, literature, ideas of representation processes, parameterization schemes, evaluation datasets etc., are ignored. As this will lead to wrong conclusions, the inter-model dependency and the respective independence weights need to be considered, for a realistic quantification of uncertainty. Here, we present the detailed step-wise methodology of a “mutual information based independence weight” framework, that accounts for the linear and nonlinear dependence between GCMs and the equitability property. • A brief illustration of the utility of this method is provided by applying it to the multi-model ensemble of 20 GCMs. • The weighted variance approach seemingly reduces the uncertainty about one GCM given the knowledge of another.
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spelling pubmed-99585072023-02-26 Mutual information based weighted variance approach for uncertainty quantification of climate projections Majhi, Archana Dhanya, C.T. Chakma, Sumedha MethodsX Method Article Future climate projections are a vital source of information that aid in deriving effective mitigation and adaptation measures. Due to the inherent uncertainty in these climate projections, quantification of uncertainty is essential for increasing its credibility in policymaking. While quantifying the uncertainty, often the possible dependency between the General Circulation Models (GCMs) due to their shared common model code, literature, ideas of representation processes, parameterization schemes, evaluation datasets etc., are ignored. As this will lead to wrong conclusions, the inter-model dependency and the respective independence weights need to be considered, for a realistic quantification of uncertainty. Here, we present the detailed step-wise methodology of a “mutual information based independence weight” framework, that accounts for the linear and nonlinear dependence between GCMs and the equitability property. • A brief illustration of the utility of this method is provided by applying it to the multi-model ensemble of 20 GCMs. • The weighted variance approach seemingly reduces the uncertainty about one GCM given the knowledge of another. Elsevier 2023-02-05 /pmc/articles/PMC9958507/ /pubmed/36851983 http://dx.doi.org/10.1016/j.mex.2023.102063 Text en © 2023 Published by Elsevier B.V. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Method Article
Majhi, Archana
Dhanya, C.T.
Chakma, Sumedha
Mutual information based weighted variance approach for uncertainty quantification of climate projections
title Mutual information based weighted variance approach for uncertainty quantification of climate projections
title_full Mutual information based weighted variance approach for uncertainty quantification of climate projections
title_fullStr Mutual information based weighted variance approach for uncertainty quantification of climate projections
title_full_unstemmed Mutual information based weighted variance approach for uncertainty quantification of climate projections
title_short Mutual information based weighted variance approach for uncertainty quantification of climate projections
title_sort mutual information based weighted variance approach for uncertainty quantification of climate projections
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958507/
https://www.ncbi.nlm.nih.gov/pubmed/36851983
http://dx.doi.org/10.1016/j.mex.2023.102063
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