Some thoughts on insuring natural catastophe and weather events, by Stephen Bennett from Demex Group;
Unprecedented flooding in Yellowstone National Park recently left substantial damage to roads and park infrastructure. It has been estimated that it could cost upward of $1 billion and several years to rebuild the environmentally sensitive landscape. Dangerously high temperatures have also recently been observed during two record-breaking heat waves across the southern and central U.S. Researchers suggest that productivity losses due to heat cost the U.S. an estimated $100 billion each year.
A 2021 report by the World Meteorological Organization estimates that highly weather-sensitive sectors such as agriculture, energy, transport, construction, and disaster risk management could benefit by over $160 billion per year from potential improvements in weather forecasting capabilities. The report goes on to indicate that these improvements are already within reach given the current state of scientific knowledge and technology.
Weather and climate forecasting consider the complex interactions between oceanic and atmospheric conditions. Numerical weather prediction (aka weather modeling) is the foundation for every weather forecast and recent advancements in computing have greatly benefited the science of weather modeling. Thanks to updated technology, weather forecasts have improved dramatically in just the past few decades.
Weather models divide the earth into a set of three-dimensional boxes, often called a “grid.” The size of the boxes defines the resolution of the grid. Large boxes form a low-resolution grid and cannot model what’s happening over small areas. Low resolution models provide a valuable picture of large-scale weather trends over long timelines. For example, this approach is helpful for forecasting big storms that travel across the entirety of North America on a week-long horizon. Low resolution models are also used to extend forecast timelines to weeks, months, years, and even decades into the future.
Smaller boxes form a higher resolution grid which can model hyper-local weather phenomena that might only be one or two miles wide. These models require significant computing resources and, as such, are limited to forecasting only a few hours or days into the future.
Climate scientists often use high and low resolution modeling coupled together. Recent advancements with computing capacity, big data, machine learning, and artificial intelligence are all improving climate forecasts. Recent research concludes that extreme weather events are increasing in frequency and severity in some regions. Climate change is shifting predictability for weather patterns such as summer heat, winter cold, seasonal rain, and seasonal snow conditions. As variability and volatility in weather trends grow, the distribution of potential weather expands.
Weather events like heavy precipitation and winter storms are gaining intensity in some areas. Research by the EPA indicates that nine out of the ten top years for extreme single-day precipitation events in the US have taken place since 1996. According to the New York Times, the Northeastern United States experienced three to four times as many winter storms between 2008 and 2018 than in the previous five decades.
This intensification is consistent with the physics of a changing atmospheric composition: as the atmosphere warms, it holds more moisture. Consequently, there is more water vapor in the air, which can drive increased amounts of rain and snow during major storms.
Scientists at the Lawrence Berkeley National Laboratory in collaboration with the World Weather Attribution organization found that up to 38% of the extreme rainfall experienced by southeastern Texas during Hurricane Harvey in August 2017 was attributable to climate change. Harvey, a record-breaking storm, devastated Texas. The National Oceanic and Atmospheric Association (NOAA) recorded that Harvey caused at least 68 deaths and $123 billion in economic damage. The Texas Department of Public Safety reported damage to over 290,000 homes and the destruction of more than 300,000 vehicles.
Increased irregularity of weather events presents risk and uncertainty, making financial security for businesses increasingly difficult to insure. Understanding these risks requires analysis of the individual, local effects on business operations in real-time.
Regulators are Responding
In response to the increased risks of extreme weather, the movement toward climate-related risk management Is underway.
In March 2022, the Securities and Exchange Commission (SEC) announced a draft proposal that would require public companies to disclose information regarding climate-related risks. Specifically, the proposed rule requires companies to report on the impacts of extreme weather on their costs and revenues for each individual extreme weather event.
In April, the White House Office of Management and Budget (OMB) took the historic step of formally accounting for the risks of climate change in the Federal Budget, warning of potential financial losses for the US government from climate change and recommending congressional budget allocation to address this threat and better inform climate action.
Insurance is Leading
Parametric insurance leverages the same technological advancements that are making weather and climate forecasts more accurate. Analysis, pricing, underwriting and portfolio management have become quick and fully transparent. Coverage automatically triggers upon specific extreme events. When the event is triggered: claim, settlement, and payment occur automatically and much quicker than traditional insurance.
In the case of property management, parametric insurance can save the policyholder 10-40% on snow removal costs. Custom-fit winter weather management solutions incorporate hyper-local snowfall patterns to safeguard against cold weather fluctuations. When snow reaches a certain extreme level – low or high – claims are automatically generated, compensating policyholders for increased costs or lost revenue.
Vave, an algorithmic underwriting MGA platform, is the first company to offer parametric insurance for extreme temperature to commercial properties across the United States. The policies are designed to provide cash relief directly to small-to-medium size businesses in the case of extreme cold. These cold weather claims help offset losses during extreme cold snaps. Innovative insurance options designed for a shifting climate will protect the business bottom line following the next major storm or extreme temperature event.
Stephen Bennett is the Chief Climate Officer of The Demex Group and Chairman of the American Meteorological Society’s Committee on Financial Weathers and Climate Risk.