EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Climate change, historical data and catastrophe modelling

Richard Dixon1,2, Sam Franklin3, Len Shaffrey2, and Debbie Clifford3
Richard Dixon et al.
  • 1CatInsight, London, United Kingdom (
  • 2Department of Meteorology, University of Reading, Reading, United Kingdom
  • 3Institute for Environmental Analytics, Reading, United Kingdom

This presentation will discuss climate change in the context of catastrophe modelling and tail risk. Given that the catastrophe modelling industry typical only has short historical records that provide limited information as to whether hazard is non-stationary, what are the methods and datasets that may aid the catastrophe modelling community to better understand how and whether risk is changing temporally? 

The issues will be framed by using examples of output from a multi-year multi-ensemble 60km global climate simulation, where extra-tropical windstorm daily maximum gust data has been converted into yearly aggregate European insurance loss with the help of PERILS European industry exposure data. The data is used to show how reliance on single historical datasets can produce misleading trends in catastrophe losses - but also potentially point to underlying trends in risk that single historical datasets may not be able to detect.

How to cite: Dixon, R., Franklin, S., Shaffrey, L., and Clifford, D.: Climate change, historical data and catastrophe modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20647,, 2020


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