Articles | Volume 22, issue 11
Nat. Hazards Earth Syst. Sci., 22, 3585–3606, 2022
https://doi.org/10.5194/nhess-22-3585-2022
Nat. Hazards Earth Syst. Sci., 22, 3585–3606, 2022
https://doi.org/10.5194/nhess-22-3585-2022
Research article
02 Nov 2022
Research article | 02 Nov 2022

Using high-resolution global climate models from the PRIMAVERA project to create a European winter windstorm event set

Julia F. Lockwood et al.

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Cited articles

Athanasiadis, P. J., Yeager, S., Kwon, Y.-O., Bellucci, A., Smith, D., and Tibaldi, S.: Decadal predictability of North Atlantic blocking and the NAO, npj Clim. Atmos. Sci., 3, 20, https://doi.org/10.1038/s41612-020-0120-6, 2020. 
Baker, A. J., Schiemann, R., Hodges, K. I., Demory, M., Mizielinski, M. S., Roberts, M. J., Shaffrey, L. C., Strachan, J., and Vidale, P. L.: Enhanced Climate Change Response of Wintertime North Atlantic Circulation, Cyclonic Activity, and Precipitation in a 25-km-Resolution Global Atmospheric Model, J. Climate, 32, 7763–7781, https://doi.org/10.1175/JCLI-D-19-0054.1, 2019. 
Befort, D. J., Wild, S., Knight, J. R., Lockwood, J. F., Thornton, H. E., Hermanson, L., Bett, P. E., Weisheimer, A., and Leckebusch, G. C.: Seasonal forecast skill for extratropical cyclones and windstorms, Q. J. Roy. Meteorol. Soc., 145, 92–104, https://doi.org/10.1002/qj.3406, 2019. 
Bojovic, D., Mishra, N., Palin, E., Guentchev, G., Lockwood, J., Brayshaw, D., Gonzalez, P., Bessembinder, J., and van der Linden, E.: PRIMAVERA Deliverable D11.6: Report on end-user requirements, https://www.primavera-h2020.eu/assets/media/uploads/d11.6_v1.0_end_user_reqts.pdf (last access: November 2022), 2017. 
Center for International Earth Science Information Network – CIESIN – Columbia University: Gridded Population of the World, Version 4 (GPWv4): Population Density, NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, https://doi.org/10.7927/H4NP22DQ (last access: July 2017), 2016. 
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Short summary
We describe how we developed a set of 1300 years' worth of European winter windstorm footprints, using a multi-model ensemble of high-resolution global climate models, for use by the insurance industry to analyse windstorm risk. The large amount of data greatly reduces uncertainty on risk estimates compared to using shorter observational data sets and also allows the relationship between windstorm risk and predictable large-scale climate indices to be quantified.
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