Cargando…
Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022
Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582174/ https://www.ncbi.nlm.nih.gov/pubmed/37848427 http://dx.doi.org/10.1038/s41467-023-42377-1 |
_version_ | 1785122272093143040 |
---|---|
author | Dunstone, Nick Smith, Doug M. Hardiman, Steven C. Davies, Paul Ineson, Sarah Jain, Shipra Kent, Chris Martin, Gill Scaife, Adam A. |
author_facet | Dunstone, Nick Smith, Doug M. Hardiman, Steven C. Davies, Paul Ineson, Sarah Jain, Shipra Kent, Chris Martin, Gill Scaife, Adam A. |
author_sort | Dunstone, Nick |
collection | PubMed |
description | Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings. |
format | Online Article Text |
id | pubmed-10582174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105821742023-10-19 Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 Dunstone, Nick Smith, Doug M. Hardiman, Steven C. Davies, Paul Ineson, Sarah Jain, Shipra Kent, Chris Martin, Gill Scaife, Adam A. Nat Commun Article Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings. Nature Publishing Group UK 2023-10-17 /pmc/articles/PMC10582174/ /pubmed/37848427 http://dx.doi.org/10.1038/s41467-023-42377-1 Text en © Crown 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 Dunstone, Nick Smith, Doug M. Hardiman, Steven C. Davies, Paul Ineson, Sarah Jain, Shipra Kent, Chris Martin, Gill Scaife, Adam A. Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 |
title | Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 |
title_full | Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 |
title_fullStr | Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 |
title_full_unstemmed | Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 |
title_short | Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 |
title_sort | windows of opportunity for predicting seasonal climate extremes highlighted by the pakistan floods of 2022 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582174/ https://www.ncbi.nlm.nih.gov/pubmed/37848427 http://dx.doi.org/10.1038/s41467-023-42377-1 |
work_keys_str_mv | AT dunstonenick windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT smithdougm windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT hardimanstevenc windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT daviespaul windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT inesonsarah windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT jainshipra windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT kentchris windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT martingill windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 AT scaifeadama windowsofopportunityforpredictingseasonalclimateextremeshighlightedbythepakistanfloodsof2022 |