In the early hours of Monday, January 15, 1968, cyclone “Low Q” charged across northern U.K. and smashed the densely populated Central Belt of Scotland with urban winds which have only since been matched when storm Lothar hit southern Paris in late 1999. Glasgow suffered the most intense damage leading to the storm’s more common misnomer of the “Glasgow Hurricane”. This event has quite a low profile today, even in the U.K., and we use its fiftieth anniversary to highlight this exceptional European Windstorm.
In December I wrote about Lothar and Daria, a cluster of windstorms that emphasized the significance of ‘location’ when assessing windstorm risk. This month we have the 25th anniversary of the most damaging cluster of European windstorms on record—Daria, Herta, Wiebke, and Vivan.
This cluster of storms highlighted the need for better understanding the potential impact of clustering for insurance industry.
At the time of the events the industry was poorly prepared to deal with the cluster of four extreme windstorms that struck in rapid succession over a very short timeframe. However, since then we have not seen such a clustering again of such significance, so how important is this phenomena really over the long term?
There has been plenty of discourse over what makes a cluster of storms significant, the definition of clustering and how clustering should be modeled in recent years.
Today the industry accepts the need to consider the impact of clustering on the risk, and assess its importance when making decisions on underwriting and capital management. However, identifying and modeling a simple process to describe cyclone clustering is still proving to be a challenge for the modeling community due to the complexity and variety of mechanisms that govern fronts and cyclones.
What is a cluster of storms?
Broadly, a cluster can be defined as a group of cyclones that occur close in time.
But the insurance industry is mostly concerned with severity of the storms. Thus, how do we define a severe cluster? Are we talking about severe storms, such as those in 1990 and 1999, which had very extended and strong wind footprints. Or is it storms like those in the winter 2013/2014 season, that were not extremely windy but instead very wet and generated flooding in the U.K.? There are actually multiple descriptions of storm clustering, in terms of storm severity or spatial hazard variability.
Without a clearly identified precedence of these features, defining a unique modeled view for clustering has been complicated and brings uncertainty in the modelled results. This issue also exists in other aspects of wind catastrophe modeling, but in the case of clustering, the limited amount of calibration data available makes the problem particularly challenging.
Moreover, the frequency of storms is impacted by climate variability and as a result there are different valid assumptions that could be applied for modeling, depending on the activity time frame replicated in the model. For example, the 1980s and 1990s were more active than the most recent decade. A model that is calibrated against an active period will produce higher losses than one calibrated against a period of lower activity.
Due to the underlying uncertainty in the model impact, the industry should be cautious of only assessing either a clustered or non-clustered view of risk until future research has demonstrated that one view of clustering is superior to others.
How does RMS help?
RMS offers clustering as an optional view that reflects well-defined and transparent assumptions. By having different views of risk model available to them, users can better deepen their understanding of how clustering will impact a particular book of business, and explore the impact of the uncertainty around this topic, helping them make more informed decisions.
This transparent approach to modeling is very important in the context of Solvency II and helping (re)insurers better understand their tail risk.
Right now there are still many unknowns surrounding clustering but ongoing investigation, both in academia and industry, will help modelers to better understand the clustering mechanisms and dynamics, and the impacts on model components to further reduce the prevalent uncertainty that surrounds windstorm hazard in Europe.
While wind speed can indicate a storm’s damageability, two storms with similar peak wind speeds can cause vastly different levels of damage if they pass over locations with different concentrations of exposure.
This month marks the 15th anniversary of Lothar and Martin. Two powerful storms that tracked violently across Europe on December 26-28, 1999.
The combined European loss of both storms is in excess of $11 billion (2013 values). Since the storms occurred within days of each other it’s difficult to calculate the exact split of damage, however a 70:30 ratio is commonly accepted, ranking Lothar as the second largest Europe windstorm loss on record after Daria (1990).
France was hit hardest by the storms—particularly Paris, which was right in the bullseye of Lothar’s most extreme physical characteristics. The recorded wind speeds in the low-lying regions of Paris were above 160 km/h and as high as 200 km/h at the top of the Eiffel Tower.
An exceptional storm
While Lothar’s wind speeds are comparable to other historical Europe windstorms, it’s considered an exceptional event for the insurance industry because of its track and the timing of its maximum intensification over Paris. Today, Lothar is a key benchmark used by the industry to understand the potential magnitude of Europe windstorm losses.
Lothar – a one-off for France?
Many industry experts believe Lothar to be higher than a 100-year return period loss event for France; however this should be interpreted as a long-term average and France could potentially experience a similarly extreme storm this winter.
Using current industry exposures, RMS calculated the potential French losses that would result from a Lothar-like storm striking different locations in France. By relocating Lothar’s peak gusts along points up to 500 km in each direction from their original location, our modelers concluded that Lothar was the fourth worst-case storm that could have happened out of a total of 437 scenarios.
The worst-case scenario for France is a Lothar-like storm relocated approximately 100 km west of the original event but which would still significantly impact Paris. The losses from this scenario are not much higher than Lothar’s. At only 15 percent higher the small increase in loss reinforces Lothar as an exceptional benchmark for the insurance industry.
We found that the majority of scenarios in the study produced notably lower losses. This is because the displacement of the storm, by even small distances, meant that the most extreme wind speeds impacted much lower concentrations of insured exposures. The study reinforces our understanding of the sensitivity of windstorm loss to a storm’s path. It also highlights the importance of using a stochastic model containing tens of thousands of events to be able to comprehensively evaluate potential windstorm losses.
London at risk
No European city is immune from damaging windstorms. RMS also re-located Lothar over London—only a 350 km shift to the north—to see what the impacts would be. We calculated the insured loss for Europe could be as much as 25 percent higher than Lothar’s losses and potentially bigger than the $8.6 billion loss caused by Daria.
The uncertainty inherent to the climatic phenomena that drive windstorms makes it impossible to forecast exactly when and where the next strong storm will hit France or Europe. However, catastrophe models can at least help to evaluate the potential financial impact of extreme storms like Lothar.