Preprints
https://doi.org/10.5194/nhess-2022-12
https://doi.org/10.5194/nhess-2022-12
 
25 Jan 2022
25 Jan 2022
Status: this preprint is currently under review for the journal NHESS.

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

Julia F. Lockwood1, Galina S. Guentchev1, Alexander Alabaster2, Simon J. Brown1, Erika J. Palin1, Malcolm J. Roberts1, and Hazel E. Thornton1 Julia F. Lockwood et al.
  • 1Met Office Hadley Centre, Exeter, EX1 3PB, UK
  • 2AON, London, EC3V 4AN, UK

Abstract. PRIMAVERA was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets, for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data.

To create the storm footprints for the event set, the storms in the PRIMAVERA models are identified through tracking. A method is developed to separate the winds from storms occurring in the domain at the same time. The wind footprints are bias corrected and converted to 3-s gusts onto a uniform grid using quantile mapping. The distribution of the number of model storms per season as a function of estimated loss is consistent with re-analysis, as are the total losses per season, and the additional event set data greatly reduces uncertainty on return period magnitudes. The event set also reproduces the temporally clustered nature of European windstorms.

Since the event set is generated from global climate models, it can help to quantify the non-linear relationship between large-scale climate indices such as the North Atlantic Oscillation (NAO) and windstorm damage. Although we find only a moderate positive correlation between extended winter NAO and storm damage in northern European countries (consistent with re-analysis), there is a large change in risk of extreme seasons between negative and positive NAO states. The intensities of the most severe storms in the event set are, however, sensitive to the gust conversion/bias correction method used, so care should be taken when interpreting the expected damages for very long return periods.

Julia F. Lockwood et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-12', Matthias Klawa, 20 Feb 2022
    • AC1: 'Reply on RC1', Julia Lockwood, 27 Apr 2022
  • RC2: 'Comment on nhess-2022-12', Anonymous Referee #2, 21 Feb 2022
    • AC3: 'Reply on RC2', Julia Lockwood, 27 Apr 2022
  • RC3: 'Comment on nhess-2022-12', Anonymous Referee #3, 22 Feb 2022
    • AC2: 'Reply on RC3', Julia Lockwood, 27 Apr 2022

Julia F. Lockwood et al.

Julia F. Lockwood et al.

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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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