Literature DB >> 36257997

Intercomparison of regional loss estimates from global synthetic tropical cyclone models.

Simona Meiler1,2, Thomas Vogt3, Nadia Bloemendaal4,5, Alessio Ciullo6,7, Chia-Ying Lee5, Suzana J Camargo5, Kerry Emanuel8, David N Bresch6,7.   

Abstract

Tropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity. While these models have been evaluated on their ability to simulate TC activity, no study to date has focused on model performance and applicability in TC risk assessments. This study performs the intercomparison of four different global-scale synthetic TC datasets in the impact space, comparing impact return period curves, probability of rare events, and hazard intensity distribution over land. We find that the model choice influences the costliest events, particularly in basins with limited TC activity. Modelled direct economic damages in the North Indian Ocean, for instance, range from 40 to 246 billion USD for the 100-yr event over the four hazard sets. We furthermore provide guidelines for the suitability of the different synthetic models for various research purposes.
© 2022. The Author(s).

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Year:  2022        PMID: 36257997      PMCID: PMC9579140          DOI: 10.1038/s41467-022-33918-1

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   17.694


  5 in total

1.  Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century.

Authors:  Kerry A Emanuel
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-08       Impact factor: 11.205

2.  Global increase in major tropical cyclone exceedance probability over the past four decades.

Authors:  James P Kossin; Kenneth R Knapp; Timothy L Olander; Christopher S Velden
Journal:  Proc Natl Acad Sci U S A       Date:  2020-05-18       Impact factor: 11.205

3.  Generation of a global synthetic tropical cyclone hazard dataset using STORM.

Authors:  Nadia Bloemendaal; Ivan D Haigh; Hans de Moel; Sanne Muis; Reindert J Haarsma; Jeroen C J H Aerts
Journal:  Sci Data       Date:  2020-02-06       Impact factor: 6.444

4.  Estimation of global tropical cyclone wind speed probabilities using the STORM dataset.

Authors:  Nadia Bloemendaal; Hans de Moel; Sanne Muis; Ivan D Haigh; Jeroen C J H Aerts
Journal:  Sci Data       Date:  2020-11-10       Impact factor: 6.444

  5 in total

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