The question of whether climate change is driving an increase in the severity of weather events is a key concern for insurers. How can modelling systems keep up?
The past three years have seen insurers incur major losses from weather-related events.
2013 saw natural catastrophes in Europe emerging as the top loss events for the year across the globe. Flooding caused billions of euros of losses in Germany, Austria and the Czech Republic, while windstorms wreaked havoc in the UK, Belgium, the Netherlands and Denmark.
However, previous years were also chart-toppers in terms of natural catastrophe losses. 2011 was considered globally as a record year, with insured losses amounting to more than $127bn (£76bn) while 2012's Superstorm Sandy in the US saw a 30% increase in surge losses due to high sea levels.
Climate change impact
The question of whether climate change is driving an increase in the severity of weather events is a key concern for insurers and catastrophe modelling companies – particularly the potential implication the role of climate change could have on the accuracy of catastrophe models.
At the World Bank's Understanding Risk conference, held in London in June, the development of climate change models was discussed by delegates. These models, known as general circulation models, simulate the Earth's oceans, atmosphere and climate, and provide the basis for assessments of current and future weather.
While great bounds have been made in recent years as to the capability of these models, uncertainties remain around how to predict future climate.
Geoff Saville, Willis Research Network atmospheric hub leader, says any climate projection into the future requires trade-offs, partly due to the large amount of data needed to process and model climate over a longer period. "To illustrate the point, we have numerical weather prediction models that only predict a few days to a week in advance," he explains.
"This shorter time scale allows much higher image resolution – and therefore more detail – to be able to [look at], for example, an individual thunderstorm. This is not yet available for longer range climate modelling but is a key area of research for bodies such as the Willis Research Network."
Claire Souch, RMS business solutions senior vice president, says, while the company's models can do a projection of any time period, projections become more uncertain the further into the future.
"We build our models to reflect the current risk. We take into account what has already happened and then we build in future scenarios," Souch adds.
"We reflect where there is knowledge in our models today so we are able to account for whether there are changes happening already. One thing we are seeing is the impact of sea level rise on coastal flood risk," she explains.
Oasis Loss Modelling Framework project director Dickie Whitaker believes the opportunity to incorporate data from GCM models into existing catastrophe models will improve uncertainty around climate change modelling.
"At the moment we are advancing considerably in our [ability] to use catastrophe models to make decisions about weather, but we are very limited in what we can use these models," he says.
"When somebody can say with some certainty that the frequency of hurricanes will increase or reduce, then we can incorporate that into the cat models and use it as part of the decision-making process. In my view that information is not available yet."
Open source models
There have been discussions around whether open source models – which allow experts to share information on a common platform – are more effective at modelling climate change than traditional proprietary models.
However, Saville says it is not a case of one model being better than another model – rather, each model offers an alternative perspective.
"Higher resolution climate models can help us further understand the complexity of the climate system and better grasp the potential changes in climate extremes which are often dependent and regional," he says.
"Some GCMs are open source, but many require huge super-computers to run them at a resolution that can help us understand climate extremes far into the future and evaluate climate change."
Willis capital, science and policy practice chief operating officer Olivia Gray adds: "Open source models are required to enable organisations to have the ability to assess their own disaster risk exposures and to provide them with information to inform their decision-making and assist in improving their own resilience."
"The benefit of open source modelling is that a wider range of organisations will be informed about their disaster risk exposure and will be in a position to proactively manage and mitigate it," she says.
Also discussed at the conference were natural catastrophes – in particular, whether there is a need for businesses to disclose their exposure to these disasters in an effort to increase resilience and mitigate impact.
Whitaker says, to a large extent, many businesses are already aware of their catastrophe exposure. "Any business that has a material exposure to natural catastrophes, weather and climate change wants to understand what it is doing. The critical thing is how easily they can get that information, how much does it cost, and what is the uncertainty that comes with the information they get," he says.
Gray is calling for industry standards to address businesses' requirements for natural disaster mitigation. "Many private sector companies do try to mitigate the impact of natural disasters, but there needs to be a method of incentivising private sector organisations to do this so it becomes another business standard rather than an approach taken by only the most forward-thinking firms," she says.
Commentators were divided on whether better modelling might have reduced the impact of previous disasters on society.
For Saville, better modelling would only have informed decision making. "The full chain of information, from raw model output, to understanding the science, to communication to policymakers needs to be addressed to make a real difference to impacts," he says.
"By using catastrophe models, as we do in the reinsurance industry, we can better assess the exposure, vulnerability, and hazard to allow the design of financial structures that can help communities and businesses recover from natural disasters."
Whitaker identifies how the insurance industry could become involved in this area. "There is some interesting modelling becoming available that means building designs can be incorporated into models. [This] will allow people to question whether a better designed building will get a rate reduction in the [event] of a natural disaster," he explains.
"[It will be possible] to model that difference, which will mean insurance companies can give a reduction in price which will encourage people to do remedial work on existing buildings to lessen the consequences of natural catastrophes in the future."
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