Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity

Kharin, Viatcheslav; Flato, Greg; Zhang, X.; Gillett, Nathan P.; Zwiers, Francis W.; Anderson, Kevin

doi:10.1002/2018ef000813

Cited by 134 publications

(122 citation statements)

References 25 publications

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“…The best‐estimate RRs for the 20‐year precipitation extremes in CanESM2 (Figure S3) show similar patterns to the results from the 50‐year events, though the magnitudes are smaller. This result is consistent with previous studies that found increases in the probability of extremes increase with the rarity of the event (Fischer & Knutti, ; Kharin et al., ). On the other hand, the lower‐bound RRs for 20‐year precipitation events are larger and more cohesive than those for 50‐year events, particularly for the one grid scale.…”

Section: Resultssupporting

confidence: 93%

“…Keeping in mind the differences in methods and event definitions, the patterns of RR presented here are broadly consistent with those in Kharin et al. () and Fischer and Knutti (). These results are also qualitatively consistent with conclusions of the IPCC SR15 report, which showed statistically detectable changes in heavy precipitation for large subcontinental regions for differences in global warming of only +0.5 °C, but overall no corresponding grid‐scale signals because of too small signal‐to‐noise ratios (Hoegh‐Guldberg et al., ).…”

Section: Discussionsupporting

confidence: 88%

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Importance of Framing for Extreme Event Attribution: The Role of Spatial and Temporal Scales

et al. 2019

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Event attribution, which determines how anthropogenic climate change has affected the likelihood of certain types of extreme events, is of broad interest to industries, governments, and the public. Attribution results can be highly dependent on the definition of the event and the characteristics assessed, which are part of framing the attribution question. Despite a widely acknowledged sensitivity to framing, little work has been done to document the impacts on attribution and the resulting implications. Here, we use a perfect‐model approach and large ensembles of coupled climate‐model simulations to demonstrate how event attribution depends on the spatial and temporal scales used to define the event. In general, stronger attribution is found for events defined over longer time scales and larger spatial scales due to enhanced signal‐to‐noise ratios. With strong warming trends, most regions see large changes in the likelihood of temperature extremes at all scales, even at low levels of global mean temperature increase. For precipitation extremes, spatial scale plays a strong role. It may be possible to attribute changes in likelihood for extreme precipitation events defined over larger scales, but greater levels of global warming are often required before it is possible to attribute changes in the likelihood of smaller‐scale precipitation events. Care must be taken to understand the scales used in event attribution, in order to properly understand the results.

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Section: Resultssupporting

confidence: 93%

Section: Discussionsupporting

confidence: 88%

Importance of Framing for Extreme Event Attribution: The Role of Spatial and Temporal Scales

et al. 2019

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“…Results globally confirmed the theoretical arguments pointing at the intensification of extreme precipitation over the study domain, with relatively stronger trends for short‐duration AM and more extreme events. This conclusion is consistent with previous analyses over North America (e.g., Kharin et al., ; Mailhot et al., ) and was highlighted by results on AM empirical quantile, as well as from the analysis of their spatiotemporal scaling.…”

Section: Discussionsupporting

confidence: 93%

Projected Changes in the Probability Distributions, Seasonality, and Spatiotemporal Scaling of Daily and Subdaily Extreme Precipitation Simulated by a 50‐Member Ensemble Over Northeastern North America

et al. 2019

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Global warming is expected to produce modifications in the intensity, as well as in the seasonality and spatiotemporal structure of extreme precipitation. In the present study, the temporal evolution of simulated daily and subdaily precipitation extremes was analyzed to assess how they respond to climate warming over different time horizons. Pooling series from the recent 50‐member Canadian Regional Climate Model v5 Large Ensemble, the probability distributions, date and time of occurrences, and spatiotemporal structure of simulated Annual Maxima (AM) precipitation were analyzed at various spatial scales and for durations between 1 hr and 3 days. In agreement with previous studies, the results underline the large increases in AM precipitation quantiles, especially for the shortest durations and for the more extreme events (i.e., longest return periods), and modifications in their spatiotemporal scaling properties and annual and diurnal cycles. For instance, subdaily AM extremes are expected to occur later in the evening, while, no matter the duration, the extremes are expected to occur over a wider period of the year in future climate. Finally, the analysis of projected AM probability distributions showed that heavy‐tail Generalized Extreme Value (GEV) distributions will most likely be observed in the future climate, with some model grid boxes experiencing a significant increase of GEV shape parameters. These results may have major consequences in terms of the occurrence and possible impacts of the most extreme precipitation events.

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“…For example, a generalized extreme value distribution is routinely used to fit extreme values such as annual maximum daily temperatures (Kharin et al, 2013(Kharin et al, , 2018. A probability distribution is often used to fit the data if a particular form of the distribution can be assumed.…”

Section: Risk Ratiomentioning

confidence: 99%

Substantial Increase in Heat Wave Risks in China in a Future Warmer World

Sun

Zhang

2018

Earth's Future

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Increases in the frequency and intensity of heat waves have serious impacts on human health, agriculture, energy and infrastructure. Here we use three simple metrics including the number of heat wave days, the length of heat wave season, and the annual hottest day temperature to characterize future changes in heat wave severity in China, based on large ensemble simulations conducted with the Canadian Earth System Model Version 2 (CanESM2) in the context of emergency preparedness. A heat wave day is defined as a day with daily maximum temperature reaching heat alert level (35°C). We find that global warming is associated with more severe heat waves including more heat wave days, longer heat wave season and higher hottest day temperature, and expansion of regions impacted by heat waves. While the increase in the magnitude of extremes in heat wave metrics with global warming level is close to linear, the increase in the frequency of extremes is much faster. For example, the historically hottest summer in 2013 in Eastern China, which occurs about one in 5 years in the 2013 climate, is projected to become more frequent than one in 2 years under 1.5°C global warming and almost every year would be worse than 2013 under 2°C warming. Additionally, the increase in the frequency of the extreme events is larger for rarer extremes. The frequencies for once-in-5-year, once-in-10-year, and once-in-50-year events increase by 2.5, 3.5, and 5.5 times under 1.5°C global warming, respectively.Plain Language Summary Heat waves have serious impacts on human health, agriculture, energy, and infrastructure. Though a few studies have investigated the future changes in heat waves in China, the heat waves defined in those studies are seldom of direct relevance to emergency preparedness. Here we study changes in heat wave days that are defined as daily maximum temperature above 35°C, a threshold for issuing a heat alert according to China's national Standards. We examine three simple metrics including the annual number of heat wave days, the length of heat wave season, and the annual hottest day temperature based on large ensemble simulations of CanESM2. We find that global warming is associated with more severe heat waves including more heat wave days, longer heat wave season, higher hottest day temperature, and expansion of regions impacted by heat waves. The increase in the heat wave metrics with global warming level is close to linear, while the increase in the frequency of extremes in these metrics is much faster. For example, the once-in-5-year event in the current climate, with a magnitude of the historically hottest summer in 2013 in Eastern China, is projected to become more frequent, to become a once-in-2-year event under 1.5°C global warming and every year event under 2°C global warming.

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Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity

Cited by 134 publications

References 25 publications

Importance of Framing for Extreme Event Attribution: The Role of Spatial and Temporal Scales

Importance of Framing for Extreme Event Attribution: The Role of Spatial and Temporal Scales

Projected Changes in the Probability Distributions, Seasonality, and Spatiotemporal Scaling of Daily and Subdaily Extreme Precipitation Simulated by a 50‐Member Ensemble Over Northeastern North America

Substantial Increase in Heat Wave Risks in China in a Future Warmer World

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