European WindstormInsurance Industry “Key Questions”

Short Descriptions

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03EUROPEAN WINDSTORM - INSURANCE INDUSTRY “KEY QUESTIONS” - SHORT DESCRIPTIONS

There has been a geographical shift of windstorm activity in recent years whereby France has

been hit by four windstorms and the UK has remained relatively quiet. Furthermore, models are

starting to include a “short term” view of risk to allow for the fact that windstorm activity is

perceived to have decreased in the past 20-25 years.

• Are these changes natural variability of part of an ongoing climate-related trend?

• Can we develop a better understanding of the natural variability “mechanisms” (e.g. NAO)

and their relation to storminess?

There is concern within the catastrophe modelling industry y that the storms that inhabit the tail

of the curve (i.e. event with loss return periods of >200 years) have a size and direction unlike the

most severe historical storms.

• Can we get some more data-points from the “tail” of climate model runs to sit alongside

existing catastrophe model output as a “second set of eyes”?

01. Natural variability of Europe Windstorms vs. cycles vs. trends/climate

02. Storms in the tail: better understanding limits to EUWS footprints: intensity, size, shape

There is currently very little understanding (or indeed implementation in catastrophe models) of

the correlation between wind and flood in Europe.

• Do winters that possess multiple damaging windstorms also contain loss-making

flood events?

• How well correlated are wind and flood hazards not just in the tail but at shorter

return periods?

• Do stormier winter mean that the chance of an event with both wind/flood losses is greater

owing to antecedent conditions?

03. Correlation between wind and flood risk

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Similar to the “Winter storms in Europe: messages from forgotten catastrophe” study:

• Can we look at recent storms of the past 150-200 years, produce footprint and potentially

the uncertainty around them from running re-analyses.

• Are there are other sources of historical data/reanalysis that exist which could be used to

understand historical precedents?

Differences in cross-country correlation are a big driver of portfolio-level loss differences for

reinsurers who have European-wide books of business. It would be useful to understand

better the hazard correlation between countries (e.g. peak gusts, Storm Severity Index). Potential

also (e.g. through OASIS) to translate any hazard correlation research into loss correlation using

vulnerability curves.

• Can we translate climate research output into understanding of the multiple aspects of

hazard that influence the amount of correlation between different countries?

04. Academic data to expand our understanding of historical EUWS

05. Spatial correlation of hazard (and loss) across different countries

Although research has been done to look into clustering, it does vary greatly between

catastrophe models.

• Can we use climate models and synthetic event sets produced from these models to improve

our understanding of event clustering?

• Can we here also to understand better the relationship between clustering and event

severity?

06. Clustering: better understanding on frequency/severity

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Other views of windstorm risk are always welcome in the insurance industry as an extra pair of

eyes to compare against existing vendor windstorm models.

• One suggestion is whether there is potential to piggy-back off existing climate model data

to provide an alternative view – per item 9) here? (One initiative is under way with the

Copernicus WISC project providing 4km storm footprints).

Part of the concern in item 2) above comes from the hypothesis that storm shape/size is related

to the base model resolution when using a climate model.

• What is the coarsest resolution of GCM that can still be useful and provide adequate data

for dynamical downscaling?

• What resolution of GCM can be useful for risk assessment without having to do dynamical

downscaling (i.e. perform statistical downscaling on the dataset)?

• Also, can we get some understanding around calibration techniques for climate models to

understand the biases and how/when to correct them?

• What are the best statistical downscaling techniques and what is useful from GCM and what

is not reliable?

07. Creation of open source EUWS cat models through academia

08. Understanding the impact of choice of climate model resolution on the resultant view of risk

This is essentially a catch-all for a number of these topics, but it has been kept in nonetheless.

• How much GCM data is out there that has enough “years” of data at an appropriately high

enough resolution to make it useful to repurpose it for risk estimation studies?

09. Improving understanding of EUWS through repurposing pre-existing high-res GCM data

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This one is potentially one to stay within the industry to solve. We rarely get detailed loss

information to understand the impact of low severity wind events (e.g. convective summertime

wind events as well as small wintertime events).

• Is there any data out there to help us understand better the impact of these low severity

(both winter and summertime events?)

• Is there a way of standardising a definition of what makes a high frequency, low severity

event?

Hurricane remnants that re-intensify over Europe as extratropical storms (the example of

Hurricane Lili was used when list created, obviously we have Ophelia now) are something we

arguably know little about in terms of potential.

• Could there be tail events that include this type of event?

• Is there a role of warm air associated with hurricane season affecting early-season

windstorms to give them increased potential?

• Does the suggested future atmospheric/oceanic warming in future extend the crossover

region between hurricanes and re-intensifying systems further NE into the Atlantic?

10. High-frequency low severity wind events (winter and summer)

11. Crossover of hurricane & EUWS seasons. Influence of hurricane remnants on EUWS, now & future

Primarily improvements in forecasting here would help the industry with business preparedness

and claims handling.

• Rapid development of European storms makes it difficult to increase the time element of

warnings beforehand: can this be improved?

• Can we improved very local forecasting: which in particular would help for flood forecasting.

12. Forecasting of storm/flood events at the localised level

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Credits

Dr Richard Dixon Director, CatInsight

Dr Claire Souch Director, AWHA Consulting Limited

CATII

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