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A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study

Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSC...

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Autores principales: Ng, Kelvin S., Leckebusch, Gregor C., Hodges, Kevin I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Science Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107216/
https://www.ncbi.nlm.nih.gov/pubmed/35601396
http://dx.doi.org/10.1007/s00376-022-1348-3
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author Ng, Kelvin S.
Leckebusch, Gregor C.
Hodges, Kevin I.
author_facet Ng, Kelvin S.
Leckebusch, Gregor C.
Hodges, Kevin I.
author_sort Ng, Kelvin S.
collection PubMed
description Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models. In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s00376-022-1348-3.
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spelling pubmed-91072162022-05-16 A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study Ng, Kelvin S. Leckebusch, Gregor C. Hodges, Kevin I. Adv Atmos Sci Original Paper Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models. In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s00376-022-1348-3. Science Press 2022-05-14 2022 /pmc/articles/PMC9107216/ /pubmed/35601396 http://dx.doi.org/10.1007/s00376-022-1348-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Paper
Ng, Kelvin S.
Leckebusch, Gregor C.
Hodges, Kevin I.
A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
title A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
title_full A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
title_fullStr A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
title_full_unstemmed A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
title_short A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
title_sort causality-guided statistical approach for modeling extreme mei-yu rainfall based on known large-scale modes—a pilot study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107216/
https://www.ncbi.nlm.nih.gov/pubmed/35601396
http://dx.doi.org/10.1007/s00376-022-1348-3
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