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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123000663 |
| _version_ | 1827917779470123008 |
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| author | Archana Majhi C.T. Dhanya Sumedha Chakma |
| author_facet | Archana Majhi C.T. Dhanya Sumedha Chakma |
| author_sort | Archana Majhi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T03:33:19Z |
| format | Article |
| id | doaj.art-565cc0ecd4be4f57a98e110e20d5fdfc |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-13T03:33:19Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-565cc0ecd4be4f57a98e110e20d5fdfc2023-06-24T05:17:11ZengElsevierMethodsX2215-01612023-01-0110102063Mutual information based weighted variance approach for uncertainty quantification of climate projectionsArchana Majhi0C.T. Dhanya1Sumedha Chakma2Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, IndiaCorresponding author.; Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, IndiaDepartment of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, IndiaFuture 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.http://www.sciencedirect.com/science/article/pii/S2215016123000663Mutual-information-based weighted variance approach for climatic projections uncertainty quantification |
| spellingShingle | Archana Majhi C.T. Dhanya Sumedha Chakma Mutual information based weighted variance approach for uncertainty quantification of climate projections MethodsX Mutual-information-based weighted variance approach for climatic projections uncertainty quantification |
| 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 | Mutual-information-based weighted variance approach for climatic projections uncertainty quantification |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123000663 |
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