7A.6 Interpretation of decadal tropical cyclone forecasts for decision-support: application to insurance and disaster risk reduction along the US Gulf Coast and the Caribbean

Tuesday, 11 May 2010: 2:30 PM
Arizona Ballroom 6 (JW MArriott Starr Pass Resort)
Nicola Ann Ranger, London School of Economics and Political Science, London, United Kingdom; and L. Smith, F. Niehoerster, R. Muir-Wood, and H. Kunreuther

We present preliminary results of a collaborative project, between the industry and academia, that develops a framework for interpreting the range of available tropical cyclone activity projections under climate change with the goal of informing the development of robust climate change adaptation strategies by individuals, the insurance industry and public policymakers.

Predictions of changes in tropical cyclone characteristics with climate change (both natural and anthropogenic) are notoriously uncertain. Firstly, the relatively short length and data quality issues in observational records make the detection and attribution of trends in tropical cyclone characteristics problematic. Secondly, the small-scale physics involved in tropical cyclone formation and evolution make adequate simulation in global climate models impossible at present; with different climate models giving divergent results. However, there is information in past records and future projections, if interpreted with full account of the uncertainties.

Here, we present a framework for interpreting current tropical cyclone projections for decision-support, which accommodates uncertainty and facilitates robust decision making using the information available today. This framework is used to construct an envelope of plausible future risk scenarios (a future ‘risk space') using a coupled climate-catastrophe modelling approach. We will present preliminary results demonstrating the application of this framework to assess how climate change could affect losses and insurance systems in Florida and the Caribbean.

A prime objective of this research is to identify possible adaptive responses by individuals, the insurance industry and public policymakers that cost-effectively manage future risks and are robust under the deep uncertainties in current long-term forecasts.

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