A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand
A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events i...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Environmental Research: Climate |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2752-5295/acf4b4 |
| _version_ | 1797327239012941824 |
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| author | Jordis S Tradowsky Greg E Bodeker Christopher J Noble Dáithí A Stone Graham D Rye Leroy J Bird William I Herewini Sapna Rana Johannes Rausch Iman Soltanzadeh |
| author_facet | Jordis S Tradowsky Greg E Bodeker Christopher J Noble Dáithí A Stone Graham D Rye Leroy J Bird William I Herewini Sapna Rana Johannes Rausch Iman Soltanzadeh |
| author_sort | Jordis S Tradowsky |
| collection | DOAJ |
| description | A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events. |
| first_indexed | 2024-03-08T06:35:27Z |
| format | Article |
| id | doaj.art-819ffc4c3e794feaa7bde4327045860a |
| institution | Directory Open Access Journal |
| issn | 2752-5295 |
| language | English |
| last_indexed | 2024-03-08T06:35:27Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research: Climate |
| spelling | doaj.art-819ffc4c3e794feaa7bde4327045860a2024-02-03T10:16:54ZengIOP PublishingEnvironmental Research: Climate2752-52952023-01-012404500810.1088/2752-5295/acf4b4A forecast-model-based extreme weather event attribution system developed for Aotearoa New ZealandJordis S Tradowsky0https://orcid.org/0000-0001-9059-4292Greg E Bodeker1https://orcid.org/0000-0003-1094-5852Christopher J Noble2https://orcid.org/0000-0002-3203-1704Dáithí A Stone3https://orcid.org/0000-0002-2518-100XGraham D Rye4https://orcid.org/0009-0007-4282-2183Leroy J Bird5https://orcid.org/0000-0002-7034-7064William I Herewini6Sapna Rana7https://orcid.org/0000-0002-3561-9423Johannes Rausch8Iman Soltanzadeh9https://orcid.org/0000-0002-7465-4117Bodeker Scientific , Alexandra, Aotearoa New Zealand; Norwegian Meteorological Institute , Oslo, NorwayBodeker Scientific , Alexandra, Aotearoa New ZealandMeteorological Service of New Zealand Limited , Wellington, Aotearoa New ZealandNational Institute of Water and Atmospheric Research , Wellington, Aotearoa New ZealandMeteorological Service of New Zealand Limited , Wellington, Aotearoa New ZealandBodeker Scientific , Alexandra, Aotearoa New ZealandBodeker Scientific , Alexandra, Aotearoa New Zealand; Meridian Energy , Christchurch, Aotearoa New ZealandMeteorological Service of New Zealand Limited , Wellington, Aotearoa New Zealand; He Pou a Rangi Climate Change Commission , Wellington, Aotearoa New ZealandMeteorological Service of New Zealand Limited , Wellington, Aotearoa New Zealand; Meteomatics AG , St. Gallen, SwitzerlandMeteorological Service of New Zealand Limited , Wellington, Aotearoa New Zealand; E.ON Digital Technology , Bonn, GermanyA largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events.https://doi.org/10.1088/2752-5295/acf4b4extreme weather eventsextreme event attributionprecipitation simulationattribution system |
| spellingShingle | Jordis S Tradowsky Greg E Bodeker Christopher J Noble Dáithí A Stone Graham D Rye Leroy J Bird William I Herewini Sapna Rana Johannes Rausch Iman Soltanzadeh A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand Environmental Research: Climate extreme weather events extreme event attribution precipitation simulation attribution system |
| title | A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand |
| title_full | A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand |
| title_fullStr | A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand |
| title_full_unstemmed | A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand |
| title_short | A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand |
| title_sort | forecast model based extreme weather event attribution system developed for aotearoa new zealand |
| topic | extreme weather events extreme event attribution precipitation simulation attribution system |
| url | https://doi.org/10.1088/2752-5295/acf4b4 |
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