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Verification of extreme event attribution: Using out-of-sample observations to assess changes in probabilities of unprecedented events.


ABSTRACT: Independent verification of anthropogenic influence on specific extreme climate events remains elusive. This study presents a framework for such verification. This framework reveals that previously published results based on a 1961-2005 attribution period frequently underestimate the influence of global warming on the probability of unprecedented extremes during the 2006-2017 period. This underestimation is particularly pronounced for hot and wet events, with greater uncertainty for dry events. The underestimation is reflected in discrepancies between probabilities predicted during the attribution period and frequencies observed during the out-of-sample verification period. These discrepancies are most explained by increases in climate forcing between the attribution and verification periods, suggesting that 21st-century global warming has substantially increased the probability of unprecedented hot and wet events. Hence, the use of temporally lagged periods for attribution-and, more broadly, for extreme event probability quantification-can cause underestimation of historical impacts, and current and future risks.

SUBMITTER: Diffenbaugh NS 

PROVIDER: S-EPMC7080452 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Verification of extreme event attribution: Using out-of-sample observations to assess changes in probabilities of unprecedented events.

Diffenbaugh Noah S NS  

Science advances 20200318 12


Independent verification of anthropogenic influence on specific extreme climate events remains elusive. This study presents a framework for such verification. This framework reveals that previously published results based on a 1961-2005 attribution period frequently underestimate the influence of global warming on the probability of unprecedented extremes during the 2006-2017 period. This underestimation is particularly pronounced for hot and wet events, with greater uncertainty for dry events.  ...[more]

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