Variability conceals emerging trend in 100yr projections of UK local hourly rainfall extremes
dc.contributor.author
Kendon, Elizabeth J.
dc.contributor.author
Fischer, Erich
dc.contributor.author
Short, Chris J.
dc.date.accessioned
2023-04-12T08:04:42Z
dc.date.available
2023-03-18T07:54:12Z
dc.date.available
2023-03-21T10:45:19Z
dc.date.available
2023-03-21T13:47:25Z
dc.date.available
2023-04-12T08:04:42Z
dc.date.issued
2023
dc.identifier.issn
2041-1723
dc.identifier.other
10.1038/s41467-023-36499-9
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/603779
dc.identifier.doi
10.3929/ethz-b-000603779
dc.description.abstract
Extreme precipitation is projected to intensify with warming, but how this will manifest locally through time is uncertain. Here, we exploit an ensemble of convection-permitting transient simulations to examine the emerging signal in local hourly rainfall extremes over 100-years. We show rainfall events in the UK exceeding 20 mm/h that can cause flash floods are 4-times as frequent by 2070s under high emissions; in contrast, a coarser resolution regional model shows only a 2.6x increase. With every degree of regional warming, the intensity of extreme downpours increases by 5-15%. Regional records of local hourly rainfall occur 40% more often than in the absence of warming. However, these changes are not realised as a smooth trend. Instead, as a result of internal variability, extreme years with record-breaking events may be followed by multiple decades with no new local rainfall records. The tendency for extreme years to cluster poses key challenges for communities trying to adapt.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Variability conceals emerging trend in 100yr projections of UK local hourly rainfall extremes
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2023-03-07
ethz.journal.title
Nature Communications
ethz.journal.volume
14
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Nat Commun
ethz.pages.start
1133
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Extreme Events: Artificial Intelligence for Detection and Attribution
en_US
ethz.grant
Understanding and quantifying the occurrence of very rare climate extremes in a changing climate
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03777 - Knutti, Reto / Knutti, Reto
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03777 - Knutti, Reto / Knutti, Reto
ethz.grant.agreementno
101003469
ethz.grant.agreementno
178778
ethz.grant.fundername
EC
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
H2020
ethz.grant.program
Projekte MINT
ethz.date.deposited
2023-03-18T07:54:12Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-04-12T08:04:43Z
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2023-04-12T08:04:43Z
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Journal Article [121987]