Real-time extreme weather event attribution with forecast seasonal SSTs
Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual ext...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Published: |
IOP Publishing
2016
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| _version_ | 1797070023753203712 |
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| author | Haustein, K Otto, F Uhe, P Schaller, N Allen, M Hermanson, L Christidis, N McLean, P Cullen, H |
| author_facet | Haustein, K Otto, F Uhe, P Schaller, N Allen, M Hermanson, L Christidis, N McLean, P Cullen, H |
| author_sort | Haustein, K |
| collection | OXFORD |
| description | Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by seasonal forecast sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or seasonal forecast SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, forecast SSTs can provide sufficient guidance to determine the dynamic contribution to the event. |
| first_indexed | 2024-03-06T22:33:04Z |
| format | Journal article |
| id | oxford-uuid:58ea4d26-196e-46b6-b987-9e5d253a69d5 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:33:04Z |
| publishDate | 2016 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | oxford-uuid:58ea4d26-196e-46b6-b987-9e5d253a69d52022-03-26T17:06:41ZReal-time extreme weather event attribution with forecast seasonal SSTsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:58ea4d26-196e-46b6-b987-9e5d253a69d5Symplectic Elements at OxfordIOP Publishing2016Haustein, KOtto, FUhe, PSchaller, NAllen, MHermanson, LChristidis, NMcLean, PCullen, HWithin the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by seasonal forecast sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or seasonal forecast SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, forecast SSTs can provide sufficient guidance to determine the dynamic contribution to the event. |
| spellingShingle | Haustein, K Otto, F Uhe, P Schaller, N Allen, M Hermanson, L Christidis, N McLean, P Cullen, H Real-time extreme weather event attribution with forecast seasonal SSTs |
| title | Real-time extreme weather event attribution with forecast seasonal SSTs |
| title_full | Real-time extreme weather event attribution with forecast seasonal SSTs |
| title_fullStr | Real-time extreme weather event attribution with forecast seasonal SSTs |
| title_full_unstemmed | Real-time extreme weather event attribution with forecast seasonal SSTs |
| title_short | Real-time extreme weather event attribution with forecast seasonal SSTs |
| title_sort | real time extreme weather event attribution with forecast seasonal ssts |
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