Estimating concurrent climate extremes: A conditional approach

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dc.contributor.authorHuang, Whitney K.
dc.contributor.authorMonahan, Adam H.
dc.contributor.authorZwiers, Francis W.
dc.date.accessioned2021-12-21T13:34:29Z
dc.date.available2021-12-21T13:34:29Z
dc.date.copyright2021en_US
dc.date.issued2021
dc.descriptionThis work was conducted as part of the Canadian Statistical Sciences Institute (CANSSI, http://www.canssi.ca/) Postdoctoral Fellowships program. The authors acknowledge the Canadian Center for Climate Modeling and Analysis of Environment and Climate Change Canada for executing and making available the CanRCM4 large ensemble simulations. The authors would also like to thank Dr. Alex Cannon and three anonymous reviewers for their valuable input.en_US
dc.description.abstractSimultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipWH acknowledges the support of the NSF Grant # 1638521 to the Statistical and Applied Mathematical Sciences Institute (SAMSI). AM acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [funding reference number RGPIN-2019-04986].en_US
dc.identifier.citationHuang, Whitney K, Monahan, Adam H., Zwiers, Francis W. (2021). “Estimating concurrent climate extremes: A conditional approach.” Weather and Climate Extremes, 33, 100332. DOI: https://doi.org/10.1016/j.wace.2021.100332en_US
dc.identifier.urihttps://doi.org/10.1016/j.wace.2021.100332
dc.identifier.urihttp://hdl.handle.net/1828/13622
dc.language.isoenen_US
dc.publisherWeather and Climate Extremesen_US
dc.subjectConcurrent wind and precipitation extremes
dc.subjectQuantile regression
dc.subjectConditional extreme value model
dc.subjectLarge climate ensembles
dc.subjectPacific Climate Impacts Consortium (PCIC)
dc.subject.departmentSchool of Earth and Ocean Sciences
dc.subject.departmentDepartment of Physics and Astronomy
dc.titleEstimating concurrent climate extremes: A conditional approachen_US
dc.typeArticleen_US

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