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Attributing human influence on the July 2017 Chinese heatwave

The influence of sea-surface temperatures

Datos Bibliográficos

ID15549335
AutoresSarah Sparrow (0000-0002-1802-6909, University of Oxford, autor de correspondencia), Qin Su (0000-0001-5003-125X, Yunnan University), Fangxing Tian (0000-0002-6747-1769, National Centre for Atmospheric Science), Sihan Li (0009-0008-2054-1724), S H Li (0000-0001-8923-6746, University of Oxford), Yang Chen (0000-0001-5943-3247, Chinese Academy of Meteorological Sciences), Wei Chen (0000-0002-9419-148X, Chinese Academy of Sciences), Feifei Luo (0000-0001-9320-7147, Chinese Academy of Sciences), Nicolas Freychet (0000-0003-2207-4425, University of Edinburgh), Fraser C Lott (0000-0001-5184-4156, Met Office), Buwen Dong (0000-0003-0809-7911, National Centre for Atmospheric Science), Simon F B Tett (0000-0001-7526-560X, University of Edinburgh), David Wallom (0000-0001-7527-3407, University of Oxford)
Año2018
Volumen13
Número11
Páginas114004-114004
Fecha de publicación2018-09-21
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironmental Research Letters (JOURNAL)
Identificadores de la revistaISSN: 1748-9326 • E-ISSN: 1748-9326
EditorialIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/aae356
OpenAlexW2892249700
IdiomaEN
Citas recibidas7
Referencias citadas23

On21–25 July 2017 a record-breaking heatwave occurred in Central Eastern China, affecting nearly half of the national population and causing severe impacts on public health, agriculture and infrastructure. Here, we compare attribution results from twoUKMet Office Hadley Centre models, HadGEM3-GA6 and weather@home (HadAM3P driving 50 kmHadRM3P). Within HadGEM3-GA6 July 2017-like heatwaves were unequaled in the ensemble representing the world without human influences. Such heatwaves became approximately a 1 in 50 year event and increased by a factor of 4.8 (5%–95% range of 3.1 to 8.0) in weather@home as a result of human activity. Considering the risk ratio (RR) for the full range of return periods shows a discrepancy at all return times between the two model results. Within weather@home a range of different counterfactual sea surface temperature (SST) patterns were used, whereas HadGEM3-GA6 used a single estimate. The global mean difference in SST (between factual and counterfactual simulations) is shown to be related to the generalised extreme value (GEV) location parameter and consequently the RR, especially for return periods of less than 50 years. It is suggested that a suitable range of SST patterns are used for future attribution studies to ensure that this source of uncertainty is represented within the simulations and subsequent attribution results. It is shown that the risk change between factual and counterfactual simulations is not purely a simple shift in the distribution (i.e. change in GEVlocation parameter). For return periods greater than 50 years, the GEVshape parameter is found to strongly influence the RR determined with theGEV scale parameter affecting only the most severe events

Attribution · Climate change · Climatology · Counterfactual thinking · Extreme weather · Geography · Meteorology · Population · Range (aeronautics · Sea surface temperature · Climate Change and Health Impacts · Climate variability and models · Environmental Science · Meteorological Phenomena and Simulations · Psychology · Demography · Geology · Oceanography

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Obras citantes distintas7
Citas por año1
Intervalo de citas2019 - 2025 (7)
Velocidad de citaciónrecent
Altamente citadoNo
Tipos de citaNeutras: 7
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