Upgrade of the HadGEM3-A based attribution system to high resolution and a new validation framework for probabilistic event attribution

Ciavarella, Andrew; Christidis, Nikos; Andrews, Martin B.; Groenendijk, M.; Rostron, John; Elkington, Mark; Burke, Claire; Lott, Fraser C.; Stott, Peter A.

doi:10.1016/j.wace.2018.03.003

Cited by 84 publications

(86 citation statements)

References 46 publications

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“…Following Chen et al (2018), the Met Office Hadley Centre attribution model, HadGEM3-GA6 (Christidis et al 2012, Ciavarella et al 2018 ran at a resolution of N216 (about 60-km resolution in mid-latitudes) was used in this study. The model simulates the atmosphere only and includes the latest dynamical core (Wood and Stainforth 2010) and JULES land surface model (Best et al 2011).…”

Section: Hadgem3-ga6-n216mentioning

confidence: 99%

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

Sparrow

Tian

et al. 2018

Environ. Res. Lett.

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On 21-25 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 two UK Met Office Hadley Centre models, HadGEM3-GA6 and weather@home (HadAM3P driving 50 km 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 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 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.

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Section: Hadgem3-ga6-n216mentioning

confidence: 99%

“…This scheme was also present in the 2017 runs, in addition to differences in initial conditions caused by continuing from the Historical ensemble. For more details, see Ciavarella et al (2018).…”

Section: Hadgem3-ga6-n216mentioning

confidence: 99%

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

Sparrow

Tian

et al. 2018

Environ. Res. Lett.

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“…More in‐depth assessments may also delve into the representation of potentially important processes associated with the atmospheric circulation, land–atmosphere, or troposphere–stratosphere interactions (Vautard et al , ). Tailor‐made evaluation methodologies for analyses with atmosphere‐only models have also been proposed (Ciavarella et al , ). Although there are no observational estimates of the HRW and HRD over large spatial scales, there is a close correspondence between them and two T max‐based ETCCDI temperature extreme indices, available from the HadEX2 global gridded data set (Donat et al , ; Sillmann et al , ).…”

Section: Cmip5 Index Trendsmentioning

confidence: 99%

“…Over the last decade, the Hadley Centre has developed and utilized a state‐of‐the‐art extreme event attribution system with the view of integrating it into its developing climate services. The system has been built on the atmospheric model HadGEM3‐A (Christidis et al , ) and been subjected to meticulous evaluation assessments that have used a combination of standard as well as novel evaluation techniques (Ciavarella et al , ; Vautard et al , ). During the course of the EUropean CLimate and weather Events: Interpretation and Attribution (EUCLEIA) project (https://eucleia.eu/), the operationalization of the Hadley Centre's attribution system was prototyped, modelled after the structurally similar seasonal forecasting system that has been operational in the UK Met Office for several years.…”

Section: The Hadley Centre Attribution Systemmentioning

confidence: 99%

Anthropogenic climate change and heat effects on health

Christidis

Mitchell

Stott

2019

Intl Journal of Climatology

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Increasing extreme temperatures linked to human influence amplify thermal stress and can lead to decreases in work productivity and increases in heat‐related mortality. However, studies assessing in a formal statistical way the contribution of climate change to such impacts remain sparse. Two new indices are introduced here that measure the effect of anthropogenic climate change on the intensity and frequency of health‐relevant heat extremes. Maximum daily temperature data from observations and climate models are used to compute annual index values in different regions around the world. The models employed in the study are evaluated against observational data and only the 10 best are retained for the analysis. Human‐induced warming that leads to an increase in heat‐related deaths has reached about a degree in all continents and is projected to exceed 3° by 2100. All regions currently experience at least 10 additional days per year when thermal deaths are expected to occur, but the number is several times higher in warmer tropical regions, where it is estimated to exceed 100 days by the end of the century. Significant positive trends may also arise in smaller‐scale areas, as shown for central England. Adaptation to the warmer present‐day climate would take the edge off the intensity of warming conducive to a rise in heat mortality by 2100, reducing it by about a degree, but would have a more moderate effect on the frequency of heat mortality days. Index values are also computed with data from the Hadley Centre's attribution system, and annual assessments of the associated impacts are made, which are envisaged to become part of a developing climate service. A first application to the United Kingdom for two recent years demonstrates the kind of attribution information that can be made available to users.

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“…For HadGEM3-A, we also use analogous Historical2017 and Natural2017 simulations to examine the role of SST and sea-ice variability. To estimate biases of the extreme indices from HadGEM3-A and W@H, respectively, we use the historical 15-and 170-member ensembles described in Ciavarella et al (2018) and Sparrow et al (2018) that include both anthropogenic and natural forcings and span 1/1/1960-30/12/2013 and 1/1/1986-30/12/2017, respectively.…”

Section: Introductionmentioning

confidence: 99%

Learning from the 2018 heatwave in the context of climate change: are high-temperature extremes important for adaptation in Scotland?

Undorf

Allen

Hagg

et al. 2020

Environ. Res. Lett.

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To understand whether high temperatures and temperature extremes are important for climate change adaptation in Scotland, we place the 2018 heatwave in the context of past, present, and future climate, and provide a rapid but comprehensive impact analysis. The observed hottest day (d), 5 d, and 30 d period of 2018 and the 5 d period with the warmest nights had return periods of 5-15 years for 1950-2018. The warmest night and the maximum 30 d average nighttime temperature were more unusual with return periods of >30 years. Anthropogenic climate change since 1850 has made all these high-temperature extremes more likely. Higher risk ratios are found for experiments from the CMIP6generation global climate model HadGEM3-GA6 compared to those from the very-large ensemble system weather@home. Between them, the best estimates of the risk ratios for daytime extremes range between 1.2-2.4, 1.2-2.3, and 1.4-4.0 for the 1, 5, and 30 d averages. For the corresponding nighttime extremes, the values are higher and the ranges wider (1.5->50, 1.5-5.5, and 1.6->50). The short-period nighttime extremes were more likely in 2018 than in 2017, suggesting a contribution from year-to-year climate variability to the risk enhancement of extreme temperatures due to anthropogenic effects. Climate projections suggest further substantial increases in the likelihood of 2018 temperatures between now and 2050, and that towards the end of the century every summer might be as hot as 2018. Major negative impacts occurred, especially on rural sectors, while transport and water infrastructure alleviated most impacts by implementing costly special measures. Overall, Scotland could cope with the impacts of the 2018 heatwave. However, given the likelihood increase of high-temperature extremes, uncertainty about consequences of even higher temperatures and/or repeated heatwaves, and substantial costs of preventing negative impacts, we conclude that despite its cool climate, high-temperature extremes are important to consider for climate change adaptation in Scotland.

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Upgrade of the HadGEM3-A based attribution system to high resolution and a new validation framework for probabilistic event attribution

Cited by 84 publications

References 46 publications

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

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

Anthropogenic climate change and heat effects on health

Learning from the 2018 heatwave in the context of climate change: are high-temperature extremes important for adaptation in Scotland?

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