European wind storms and reinsurance loss: New estimates of the risk

1. European wind storms and reinsurance loss: New estimates of the risk Paul Della-Marta, Mark Liniger, Christof Appenzeller, David Bresch, Pamela Köllner-Heck, Veruska Muccione ACRE Meeting, Zurich, 24.06.2008

2. Outline • Overview of wind storm risk assessment • European wind storm climate from 1880 (EMULATE) • The PreWiStoR project • Improved estimates of European wind storm climate • Improved estimates of wind storm loss • Conclusions • How can ACRE improve wind storm risk assessment?

3. Wind Storms in Europe: What is the risk?

4. How can we estimate the Risk? : Data In-Situ Model

5. Data example: Daria, 26.01.1990 • Maximum wind speed at each gridpoint over the duration of the storm m/s SwissRe: • MSLP • In-situ wind • Dynamics ERA-40 • Geostr. wind @850hPa • Assimilated obs. *not wind • Dynamics

6. How can we estimate the Risk? • Extreme Value Theory • Method of Estimation • e.g. Maximum likelihood, L-Moments • Quantification of Uncertainty • Model parameters, return periods • Model diagnostics • Goodness-of-fit • Maximal use of information • Peak Over Threshold instead of Block Maxima See Coles (2001)

7. The wind climate of Europe 1880-2003 • Geostrophic Wind Speed derived from daily MSLP EMULATE (Ansell et al 2006) (over land only, ONDJFMA)

8. Storm Selection Method Scalar wind statistic 95% threshold Winter 1999/2000

9. The storm climate of Europe 1880-2001 • Derived from daily MSLP EMULATE data 1957-2002 1880-2002

10. Change in return period and confidence using EMULATE data • Land areas only 1957-2002 1880-2002

11. PreWiStoR: Prediction of winter Wind Storm Risk • Problem: Observed records of wind storms are not long enough • Solution: ~150 storms based on observations. • Use probabilistic modelling to generate synthetic storms based on perturbed statistics • Calculate losses • New approach to use ENSEMBLE prediction systems (seasonal to decadal, s2d) • Replace statistical perturbation with physics • Utilise around ~500 seasons of s2d data • Obtain a better estimate of wind storm risk and losses See van den Brink et al. IJC (2005)

12. The concept of the PreWiStoR project • Let Lorenz attractor represent the possible trajectories of extreme wind related weather in the current climate • The envelope (climate) of trajectories of the system observed in ERA-40 (45years) • The climate of trajectories sampled in s2d data (300+ years) Source: Wikipedia

13. PreWiStoR: Data • Seasonal to decadal (s2d) climate prediction models • Using the seasonal forecasting model of the ECMWF • A coupled ocean-atmosphere Global Circulation Model • 6-7 month forecast, T159, 26 years hindcast 11 (41) member ensemble • First month removed to ensure independence • Separate ocean analysis system to initiate the seasonal forecasts • ENSEMBLE prediction system: Model is run many times  Initial conditions are perturbed  Probabilistic Forecasts

14. Comparison of wind storm climatologies from s2d • Wind storm climatologies are different in magnitude and shape and frequency • All s2d models seem to have a less negative shape than ERA-40

15. Percentile calibration and sampling 45 year periods • SYS3 has a more extreme wind climate than ERA-40 • SYS2 has a similar extreme wind climate to ERA-40 • After application of calibration • Which one is closer to the true climate? • Can we tell which one is correct?

16. Sampling experiments with s2d climatologies • Randomly order the 315 years of s2d winter seasons • Plot the estimated shape parameter as a function of the number of seasons used in the calc. • Repeat many times to simulate chaotic inter-annual variability Shape (& Scale) parameter has not converged at most (~70%) 45 year length climatologies  ERA-40 underestimates European storm climate See Vannitsem Tellus (2007)

17. Sampling experiments with s2d climatologies • Randomly order the 315 years of s2d winter seasons • Plot the estimated shape parameter as a function of the number of seasons used in the calc. • Repeat many times to simulate chaotic inter-annual variability Shape (& Scale) parameter has not converged at most (~70%) 45 year length climatologies  ERA-40 underestimates European storm climate See Vannitsem Tellus (2007)

18. Swiss Re Wind Storm Loss Model(catXos) • Vulnerability curve shows a cubic relation which is capped • Portfolio value distribution is inhomogeous

19. Range of loss uncertainty due to sampling 45 year periods • Swiss Re estimates of expected loss-frequency fit within the range of sampling uncertainty • SYS3 shows the added value of using a longer dataset

20. Conclusions • Using EMULATE data we can improve our estimates of wind storm risk longer climatology provides greater confidence • EMULATE data represents a 24hr mean MSLP  not ideal for wind storms  Geostrophic approximations • Ensemble dynamical forecasts also improve storm risk estimates Evidence that ERA-40 underestimates the risk • Dynamical models have biases and other deficientcies • Use of s2d data has replaced statistical perturbation of storms (SwissRE) with dynamical perturbations (s2d) • Sampling experiments lead to greater insight to risk of storms and loss uncertainty

21. How can ACRE improve wind storm risk assessment? • Longer reanalyses  sample more of the dynamics • Longer ensemble based reanalyses  sample more of the dynamics + account for observation errors • Longer reanalyses can be used to provide a greater range of initial conditions • S2d data only have a hindcast of ~ 30 years  limited sample of initial conditions • But to be most useful we need: • High temporal resolution output variables and/or integrated quantities e.g. Max. Wind Gust • No just event-based but a full climatology else frequencies are hard to define

22. A bivariate extreme value peak over threshold model for wind storm intensity and loss • Using the methodology in Coles (2001) and the evd R -package • Fitted to ERA40 wind storm Q95 and the transformed %TIV • Could be used to define the vulnerability with real loss data