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Attributing human influence on July 2017 Chinese heatwave: the influence of sea-surface temperatures

Sparrow, S. N., Su, Q., Tian, F., Li, S., Chen, Y., Chen, W., Luo, F., Freychet, N., Lott, F. C., Dong, B., Tett, S. and Wallom, D. (2018) Attributing human influence on July 2017 Chinese heatwave: the influence of sea-surface temperatures. Environmental Research Letters, 13 (11). ISSN 1748-9326

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To link to this item DOI: 10.1088/1748-9326/aae356


On 21st-25thJuly 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 two UK Met Office Hadley Centre models, HadGEM3-GA6 and weather@home (HadAM3P driving 50km HadRM3P). 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 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 GEV location parameter). For return periods greater than 50 years the GEV shape parameter is found to strongly influence the RR determined with the GEV scale parameter affecting only the most severe events.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:79537
Publisher:Institute of Physics


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