Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword
- Preface
- The significance of weather and climate extremes to society: an introduction
- I Defining and modeling the nature of weather and climate extremes
- II Impacts of weather and climate extremes
- 8 Extreme climatic events and their impacts: examples from the Swiss Alps
- 9 The impact of weather and climate extremes on coral growth
- 10 Forecasting US insured hurricane losses
- 11 Integrating hurricane loss models with climate models
- 12 An exploration of trends in normalized weather-related catastrophe losses
- 13 An overview of the impact of climate change on the insurance industry
- 14 Toward a comprehensive loss inventory of weather and climate hazards
- 15 The catastrophe modeling response to Hurricane Katrina
- 16 The Risk Prediction Initiative: a successful science–business partnership for analyzing natural hazard risk
- Index
- Plate section
- References
11 - Integrating hurricane loss models with climate models
Published online by Cambridge University Press: 14 September 2009
Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword
- Preface
- The significance of weather and climate extremes to society: an introduction
- I Defining and modeling the nature of weather and climate extremes
- II Impacts of weather and climate extremes
- 8 Extreme climatic events and their impacts: examples from the Swiss Alps
- 9 The impact of weather and climate extremes on coral growth
- 10 Forecasting US insured hurricane losses
- 11 Integrating hurricane loss models with climate models
- 12 An exploration of trends in normalized weather-related catastrophe losses
- 13 An overview of the impact of climate change on the insurance industry
- 14 Toward a comprehensive loss inventory of weather and climate hazards
- 15 The catastrophe modeling response to Hurricane Katrina
- 16 The Risk Prediction Initiative: a successful science–business partnership for analyzing natural hazard risk
- Index
- Plate section
- References
Summary
Condensed summary
Hurricane loss modeling has become an important if not vital aspect of many elements of hurricane planning, especially in the financial sector. The insurance industry has provided financial motivation for the development of complex hurricane damage and loss models. These models rely on a number of databases and model subcomponents that interact in complex ways, the most critical of which is hurricane climatology. The required hurricane climatology is developed through various analyses of the historical record. Unfortunately, there are numerous issues with the historical record that make detailed analysis of that record problematic, such as the length of the record, the quality of the observations, and the potential that the record is complicated by natural or anthropogenic climate signals. As climate modeling continues to advance, there is increasing potential for the use of these models to drive loss models, overcoming many of the limitations of the existing historical record. Here we describe the results of a study conducted for the Florida Commission on Hurricane Loss Projection Methodology (FCHLPM), a part of which was an assessment of historical hurricane climatology, and the potential for the use of general circulation climate models in driving loss models for both existing and future climates. The Community Climate System Model (CCSM) was used to drive a mesoscale model, which in turn was used to create inputs to an ensemble of loss models.
- Type
- Chapter
- Information
- Climate Extremes and Society , pp. 209 - 224Publisher: Cambridge University PressPrint publication year: 2008
References
, , and (1977). Parameterization characteristics of a wind-wave tropical cyclone model for the western North Pacific Ocean. Journal of Physical Oceanography, 7, 739–46.2.0.CO;2>CrossRefGoogle Scholar
, , and (2005). A statistical assessment of tropical cyclone activity in atmospheric general circulation models. Tellus, 57A, 589–604.CrossRefGoogle Scholar
(1986). A formal approach to catastrophe risk assessment and management. Proceedings of the Casualty Actuarial Society, Vol. LXXIII, No. 140, November.Google Scholar
Clark, K. M. (1997). Current and potential impact of hurricane variability on the insurance industry. In Hurricanes, Climate and Socioeconomic Impacts, ed. and . New York: Springer, pp. 273–83.Google Scholar
(2005a). Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686–8.CrossRefGoogle Scholar
(1984). Natural hazard risk assessment for an insurance program. The Geneva Papers on Risk and Insurance, 9(30).Google Scholar
Grell, G. A., Dudhia, J., and Stauffer, D. R. (1993). A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical Note, NCAR/TN-398 + STR.
, , , , and (1987). Hurricane Climatology for the Atlantic and Gulf Coasts of the United States. NOAA Technical Report NWS 38, Washington, D.C.: Federal Emergency Management Agency.Google Scholar
, , and (2005a). Sensitivity analysis for computer model projections of hurricane losses. Risk Analysis, 25, 1277–97.CrossRefGoogle Scholar
, , and (2005b). Uncertainty analysis for computer model projections of hurricane losses. Risk Analysis, 25, 1299–312.CrossRefGoogle Scholar
, , and (2006). Statistical aspects of forecasting and planning for hurricanes. The American Statistician, 60, 105–21.CrossRefGoogle Scholar
, , and (1984). A Tropical Cyclone Data Tape for the North Atlantic Basin, 1886–1983: Contents, Limitations, and Uses. NOAA Technical Memorandum NWS NHC 22, Coral Gables, Florida.Google Scholar
, and (1999). Hurricane return period estimation. Proceedings of the Tenth Symposium on Global Change Studies, Dallas, Texas, pp. 478–9.Google Scholar
(2005). Hurricanes and global warming. Brief communications arising in Nature, 438, E11–E13, with a reply from K. Emanuel.CrossRefGoogle Scholar
, , , et al. (2006). Tropical cyclone climatology in a global-warming climate as simulated in a 20 km-mesh global atmospheric model: frequency and wind intensity analyses. Journal of the Meteorological Society of Japan, 84(2), 259–76.CrossRefGoogle Scholar
(2005). Are there trends in hurricane destruction? Brief communications arising in Nature, 438, E12, with a reply from K. Emanuel.CrossRefGoogle Scholar
, , , et al. (2005). State of Florida Hurricane Loss Projection Model: atmospheric science component. Journal of Wind Engineering and Industrial Aerodynamics, 93, 651–74.CrossRefGoogle Scholar
, and (2000). The Automated Tropical Cyclone Forecasting System (Version 3.2). Bulletin of the American Meteorological Society, 81, 1231–40.2.3.CO;2>CrossRefGoogle Scholar
, , and (1979). Meteorological Criteria for Standard Project Hurricane and Probable Maximum Hurricane Wind Fields, Gulf and East Coasts of the United States. NOAA Technical Report NWS 23, Silver Spring, Maryland: National Weather Service.Google Scholar
, and (2001). High-resolution simulation of Hurricane Floyd (1999) using MM5 with a vortex following mesh refinement. Preprints, 14th Conference on Numerical Weather Prediction, July 30–August 2, 2001, Ft. Lauderdale, Florida, American Meteorological Society, J54–J56.Google Scholar
, , and (2000). Simulation of hurricane risk in the U.S. using Empirical Track Model. Journal of Structural Engineering, 126, 1222–37.CrossRefGoogle Scholar
, and (2004). Hurricane loss estimation models: opportunities for improving the state of the art. Bulletin of the American Meteorological Society, 85, 1713–26.CrossRefGoogle Scholar
Watson, C., Jr., and Johnson, M. (2006). Assessment of Computer Generated Loss Costs in Florida. Report to the Florida Commission on Hurricane Loss Projection Methodology.
, , and (2004). Insurance rate filings and hurricane loss estimation models. Journal of Insurance Research, 22, 39–64.Google Scholar
, and (1992). A GCM simulation of the relationship between tropical storm formation and ENSO. Monthly Weather Review, 120, 958–77.2.0.CO;2>CrossRefGoogle Scholar
- 4
- Cited by