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The trend and spatial spread of multisectoral climate extremes in CMIP6 models
Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979–2014), near future (2025–2060) and far future (2065–2100) periods under two emission scenarios, in eleven C...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9722700/ https://www.ncbi.nlm.nih.gov/pubmed/36470927 http://dx.doi.org/10.1038/s41598-022-25265-4 |
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author | Adeyeri, Oluwafemi E. Zhou, Wen Wang, Xuan Zhang, Ruhua Laux, Patrick Ishola, Kazeem A. Usman, Muhammad |
author_facet | Adeyeri, Oluwafemi E. Zhou, Wen Wang, Xuan Zhang, Ruhua Laux, Patrick Ishola, Kazeem A. Usman, Muhammad |
author_sort | Adeyeri, Oluwafemi E. |
collection | PubMed |
description | Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979–2014), near future (2025–2060) and far future (2065–2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions. |
format | Online Article Text |
id | pubmed-9722700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97227002022-12-07 The trend and spatial spread of multisectoral climate extremes in CMIP6 models Adeyeri, Oluwafemi E. Zhou, Wen Wang, Xuan Zhang, Ruhua Laux, Patrick Ishola, Kazeem A. Usman, Muhammad Sci Rep Article Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979–2014), near future (2025–2060) and far future (2065–2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions. Nature Publishing Group UK 2022-12-05 /pmc/articles/PMC9722700/ /pubmed/36470927 http://dx.doi.org/10.1038/s41598-022-25265-4 Text en © The Author(s) 2022, corrected publication 2023 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 | Article Adeyeri, Oluwafemi E. Zhou, Wen Wang, Xuan Zhang, Ruhua Laux, Patrick Ishola, Kazeem A. Usman, Muhammad The trend and spatial spread of multisectoral climate extremes in CMIP6 models |
title | The trend and spatial spread of multisectoral climate extremes in CMIP6 models |
title_full | The trend and spatial spread of multisectoral climate extremes in CMIP6 models |
title_fullStr | The trend and spatial spread of multisectoral climate extremes in CMIP6 models |
title_full_unstemmed | The trend and spatial spread of multisectoral climate extremes in CMIP6 models |
title_short | The trend and spatial spread of multisectoral climate extremes in CMIP6 models |
title_sort | trend and spatial spread of multisectoral climate extremes in cmip6 models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9722700/ https://www.ncbi.nlm.nih.gov/pubmed/36470927 http://dx.doi.org/10.1038/s41598-022-25265-4 |
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