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Enhanced multi-year predictability after El Niño and La Niña events
Several aspects of regional climate including near-surface temperature and precipitation are predictable on interannual to decadal time scales. Despite indications that some climate states may provide higher predictability than others, previous studies analysing decadal predictions typically sample...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567839/ https://www.ncbi.nlm.nih.gov/pubmed/37821438 http://dx.doi.org/10.1038/s41467-023-42113-9 |
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author | Liu, Yiling Donat, Markus. G. England, Matthew. H. Alexander, Lisa. V. Hirsch, Annette L. Delgado-Torres, Carlos |
author_facet | Liu, Yiling Donat, Markus. G. England, Matthew. H. Alexander, Lisa. V. Hirsch, Annette L. Delgado-Torres, Carlos |
author_sort | Liu, Yiling |
collection | PubMed |
description | Several aspects of regional climate including near-surface temperature and precipitation are predictable on interannual to decadal time scales. Despite indications that some climate states may provide higher predictability than others, previous studies analysing decadal predictions typically sample a variety of initial conditions. Here we assess multi-year predictability conditional on the phase of the El Niño–Southern Oscillation (ENSO) at the time of prediction initialisation. We find that predictions starting with El Niño or La Niña conditions exhibit higher skill in predicting near-surface air temperature and precipitation multiple years in advance, compared to predictions initialised from neutral ENSO conditions. This holds true in idealised prediction experiments with the Community Climate System Model Version 4 and to a lesser extent also real-world predictions using the Community Earth System Model and a multi-model ensemble of hindcasts contributed to the Coupled Model Intercomparison Project Phase 6 Decadal Climate Prediction Project. This enhanced predictability following ENSO events is related to phase transitions as part of the ENSO cycle, and related global teleconnections. Our results indicate that certain initial states provide increased predictability, revealing windows of opportunity for more skillful multi-year predictions. |
format | Online Article Text |
id | pubmed-10567839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105678392023-10-13 Enhanced multi-year predictability after El Niño and La Niña events Liu, Yiling Donat, Markus. G. England, Matthew. H. Alexander, Lisa. V. Hirsch, Annette L. Delgado-Torres, Carlos Nat Commun Article Several aspects of regional climate including near-surface temperature and precipitation are predictable on interannual to decadal time scales. Despite indications that some climate states may provide higher predictability than others, previous studies analysing decadal predictions typically sample a variety of initial conditions. Here we assess multi-year predictability conditional on the phase of the El Niño–Southern Oscillation (ENSO) at the time of prediction initialisation. We find that predictions starting with El Niño or La Niña conditions exhibit higher skill in predicting near-surface air temperature and precipitation multiple years in advance, compared to predictions initialised from neutral ENSO conditions. This holds true in idealised prediction experiments with the Community Climate System Model Version 4 and to a lesser extent also real-world predictions using the Community Earth System Model and a multi-model ensemble of hindcasts contributed to the Coupled Model Intercomparison Project Phase 6 Decadal Climate Prediction Project. This enhanced predictability following ENSO events is related to phase transitions as part of the ENSO cycle, and related global teleconnections. Our results indicate that certain initial states provide increased predictability, revealing windows of opportunity for more skillful multi-year predictions. Nature Publishing Group UK 2023-10-11 /pmc/articles/PMC10567839/ /pubmed/37821438 http://dx.doi.org/10.1038/s41467-023-42113-9 Text en © The Author(s) 2023, 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 Liu, Yiling Donat, Markus. G. England, Matthew. H. Alexander, Lisa. V. Hirsch, Annette L. Delgado-Torres, Carlos Enhanced multi-year predictability after El Niño and La Niña events |
title | Enhanced multi-year predictability after El Niño and La Niña events |
title_full | Enhanced multi-year predictability after El Niño and La Niña events |
title_fullStr | Enhanced multi-year predictability after El Niño and La Niña events |
title_full_unstemmed | Enhanced multi-year predictability after El Niño and La Niña events |
title_short | Enhanced multi-year predictability after El Niño and La Niña events |
title_sort | enhanced multi-year predictability after el niño and la niña events |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567839/ https://www.ncbi.nlm.nih.gov/pubmed/37821438 http://dx.doi.org/10.1038/s41467-023-42113-9 |
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