Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022
Abstract 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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-42377-1 |
| _version_ | 1827709902247690240 |
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| author | Nick Dunstone Doug M. Smith Steven C. Hardiman Paul Davies Sarah Ineson Shipra Jain Chris Kent Gill Martin Adam A. Scaife |
| author_facet | Nick Dunstone Doug M. Smith Steven C. Hardiman Paul Davies Sarah Ineson Shipra Jain Chris Kent Gill Martin Adam A. Scaife |
| author_sort | Nick Dunstone |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-10T17:28:31Z |
| format | Article |
| id | doaj.art-7f12a9c8b2c34a5ab8d9826a5c3df8fa |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:28:31Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-7f12a9c8b2c34a5ab8d9826a5c3df8fa2023-11-20T10:05:14ZengNature PortfolioNature Communications2041-17232023-10-0114111110.1038/s41467-023-42377-1Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022Nick Dunstone0Doug M. Smith1Steven C. Hardiman2Paul Davies3Sarah Ineson4Shipra Jain5Chris Kent6Gill Martin7Adam A. Scaife8Met Office Hadley CentreMet Office Hadley CentreMet Office Hadley CentreMet Office Hadley CentreMet Office Hadley CentreCentre for Climate Research Singapore (CCRS)Met Office Hadley CentreMet Office Hadley CentreMet Office Hadley CentreAbstract 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.https://doi.org/10.1038/s41467-023-42377-1 |
| spellingShingle | Nick Dunstone Doug M. Smith Steven C. Hardiman Paul Davies Sarah Ineson Shipra Jain Chris Kent Gill Martin Adam A. Scaife Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022 Nature Communications |
| 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 |
| url | https://doi.org/10.1038/s41467-023-42377-1 |
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