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Insurers ready for bad cyclone season

Insurers are preparing for another tough cyclone season in northern Australia.

The Bureau of Meteorology says current weather conditions point to an above average number of cyclones this season in all four northern regions.

Climate Prediction Manager Andrew Watkins says climate models are suggesting another La Nina event, though weaker than last summer’s, is likely to influence the weather.

“No two La Nina events are the same,” he said. “While this La Nina may be weaker than the last, this doesn’t mean we can expect fewer tropical cyclones than the previous season. In other words, we can’t afford to be complacent.”

Insurance Council of Australia (ICA) GM Risk and Disaster Planning Karl Sullivan says insurers have held seminars around the country after receiving a briefing from the bureau.

“All of the insurers we’ve been working with are planning for a big season in accordance with the bureau’s forecast for an active cyclone and flooding season,” he told insuranceNEWS.com.au.

He says insurers have activated their natural catastrophe plans and have already started reviewing their arrangements with service providers such as call centres and building contractors.

“Everybody’s had ample warning that this season could be quite bad.”

The bureau’s forecast for wetter conditions in the north this season was brought sharply home in Cairns last week when more than 500mm of rain fell on the city in one 24-hour period.

Joint research from the University of Queensland and the University of Exeter in the UK indicates cyclone activity occurs in short intense clusters which are followed by longer quiet periods.

The study’s lead researchers, Peter Mumby and David Stephenson, say the research has wide implications and could help insurance and reinsurance companies price risk.

Dr Stephenson says reinsurance companies “are a bit like ecosystems, and so need time to recover after major losses. So clustering of cyclones allows the industry to build profits before the next cluster of storm losses.