A globally consistent local-scale assessment of future tropical cyclone risk

Nadia Bloemendaal, Hans de Moel, Andrew B. Martinez, Sanne Muis, Ivan D. Haigh, Karin van der Wiel, Reindert J. Haarsma, Philip J. Ward, Malcolm J. Roberts, Job C.M. Dullaart, Jeroen C.J.H. Aerts

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

There is considerable uncertainty surrounding future changes in tropical cyclone (TC) frequency and intensity, particularly at local scales. This uncertainty complicates risk assessments and implementation of risk mitigation strategies. We present a novel approach to overcome this problem, using the statistical model STORM to generate 10,000 years of synthetic TCs under past (1980-2017) and future climate (SSP585; 2015-2050) conditions from an ensemble of four high-resolution climate models. We then derive high-resolution (10-km) wind speed return period maps up to 1000 years to assess local-scale changes in wind speed probabilities. Our results indicate that the probability of intense TCs, on average, more than doubles in all regions except for the Bay of Bengal and the Gulf of Mexico. Our unique and innovative methodology enables globally consistent comparison of TC risk in both time and space and can be easily adapted to accommodate alternative climate scenarios and time periods.

Original languageEnglish
Article numbereabm8438
Pages (from-to)1-13
Number of pages13
JournalScience advances
Volume8
Issue number17
DOIs
Publication statusPublished - 27 Apr 2022

Funding

We thank A. Couasnon and R. van Westen for the comments and suggestions. We also acknowledge SURF Sara (www.surf.nl) for the support in using the Lisa and Cartesius Computer Cluster, and the JASMIN Data Analysis Facility for support in retrieving the HighResMIP datasets. We also thank 4TU.ResearchData for their support in the storage and maintenance of the STORM datasets. N.B. and J.C.J.H.A. are funded by a Vici grant from the Netherlands Organisation for Scientific Research (NWO) (grant number 453-13-006) and the ERC Advanced Grant COASTMOVE no. 884442. I.D.H. was funded by NERC Grant CompFlood (grant number NE/S003150/1). A.B.M. received funding from Climate Econometrics and the Robertson Foundation (grant number 9907422). S.M. received funding from the research programme MOSAIC with project number ASDI.2018.036, which is financed by the NWO. M.J.R. was funded by the Joint U.K. BEIS/Defra Met Office Hadley Centre Climate Programme (grant no. GA01101), and we thank H2020 PRIMAVERA (grant no. 641727) for supplying the model datasets. P.J.W. received funding from the NWO Vidi research grant 016.161.324. This research is part of the IS-ENES3 project that has received funding from the European Union’s Horizon 2020 research and innovation funding programme under grant agreement no. 824084. The views presented in this paper are solely those of the authors and do not necessarily represent those of the Treasury Department or the U.S. government.

FundersFunder number
Climate Econometrics
Defra Met Office Hadley Centre Climate ProgrammeGA01101
Horizon 2020 Framework Programme641727, 824084, 016.161.324
Department for Business, Energy and Industrial Strategy, UK Government
Robertson Foundation9907422
Natural Environment Research CouncilNE/S003150/1
European Research Council884442
Nederlandse Organisatie voor Wetenschappelijk Onderzoek453-13-006

    Keywords

    • Tropical Cyclones
    • Climate Change
    • Risk
    • synthetic modeling

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