The tornado that struck Joplin, Missouri Sunday reveals that the insurance and catastrophe modeling industry will face greater challenges as severe storms continue to target densely populated areas in the U.S.
The Joplin tornado follows the significant increase in the insured loss estimates tied to the April U.S. tornado outbreak.
Last week Risk Management Solutions said that its estimates for insured losses following the April 25-28, 2011 U.S. tornado outbreak now range from $3.5 to $6 billion, nearly doubling some original loss estimates for losses in states such as Alabama, Georgia, and Tennessee.
Earlier AIR Worldwide announced that that its estimates for the same event range from $3.7 billion and $5.5 billion.
A debate continues to rage in the modeling and insurance industries regarding what are prompting the long-term increase in severe thunderstorm losses in the U.S., says Matthew Nielsen, product manager at RMS.
According to Nielsen, the frequency of weak to moderate tornadoes — those with an Enhanced Fujita Scale [EF] rating between zero and three — has been increasing over past half century, but the frequency of the strongest and most violent tornadoes — with an EF rating between four and five — has remained “fairly steady.”
“It is poorly understood if this is related only to the increase in the number of tornadoes that have been observed as population increases and more are reported, or if there is some sort of climate signal that relates to other variables, like Pacific sea surface temperatures,” Neilsen says. “What is known is that short term climate variability, like El Nino and La Nina, tend to change the location of tornado outbreaks,” he explains, adding that La Nina tends to shift activity from the Southern Plains towards the Southeast and the Ohio River Valley.”
Neilsen adds that although the science around the increase in tornado modeling has been evolving over the last few years, the key to understanding tornado frequency lies in the understanding of the favorable conditions for formation.
“The RMS model, instead of relying solely on the historical record (which shows increases over time due to prior underreporting) uses numerical modeling output to understand areas where tornadoes may have been underreported historically.”
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