Estimating Tropical Cyclone Damages Under Climate Change in the Southern Hemisphere Using Reported Damages

Abstract

This paper estimates the damages from tropical cyclones (TCs) in the Southern Hemisphere under future climate change scenarios based on the historical TC records in Australia. From the best-track TC data, we examine the changes in frequency, intensity, and economic damage of the TCs that made landfall since 1970. From the detailed individual TC reports, damage estimates are constructed based on reported damages. We find that the TC frequency has significantly declined over time. The intensity, however, does not show a significant trend. Average damage per TC has declined significantly from 43 million AUD in the 1970s to 11 million in the 1990s. This paper finds that 1 % decrease in minimum central pressure leads to 32.7 % increase in economic damage, which is more than three times larger than that found in the US hurricane study with regards to maximum wind speeds. For future damage projections, characteristics of the 14,000 TCs generated under seven different AOGCM climate models are applied. All seven climate models predict a decrease in TC frequency in the Southern Hemisphere but intensity predictions vary. By the end of the twenty second century, changes in climate are expected to increase the TC damage under the MRI (\(+\)94 %), the MIROC (\(+\)73 %), and the CSIRO (\(+\)66 %) model due to increased intensity. However, TC damage is expected to fall under the GFDL (\(-\)92 %) and the CNRM (\(-\)85 %) model due to decreased intensity and frequency. Adaptation will be a key determinant of the future vulnerability to TCs in the Southern Hemisphere.

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Notes

  1. 1.

    According to the BOM, the number of intense TCs (Category 4 and 5) shows no appreciable trend over the period (BOM 2011a).

    Table 1 Frequency, intensity, and damage of all landfalling cyclones since 1970
  2. 2.

    The report on TC Tracy states that it caused several hundreds of millions of dollars in 1974 dollar (BOM 2011a). Excluding the number of lives lost (65) and the number of people seriously injured (145), a conservative estimate of 200 million in 1974 dollars (0.32 % of the GDP at the time) is used for the analysis. This is approximately the value of the total number of houses destroyed, i.e., 70 % of the total houses in Darwin according to the report.

  3. 3.

    Total population of a town instead of population density is used as an explanatory variable since it is a better indicator of vulnerability by way of the number of houses destroyed or damaged.

  4. 4.

    The State dummies capture such factors as different income levels across the States.

  5. 5.

    The damage estimates are normalized to 2007 Australian dollars using the GDP deflator. Therefore, the analysis assumes the same rate of income growth in the future.

References

  1. Australian Bureau of Statistics (ABS) (2010) Population projections, Australia, 2006–2101. Australian Government. Available at http://www.abs.gov.au

  2. Australian Bureau of Statistics (ABS) (2012) Australian Census. Canberra, Australia. Available at http://www.abs.gov.au

  3. Bureau of Meteorology (BOM) (2011a) Tropical cyclones. Australian Government. Available at http://www.bom.gov.au

  4. Bureau of Meteorology (BOM) (2011b) ENSO wrap-up. Australian Government. Available at http://www.bom.gov.au/climate/enso/index.shtml

  5. Cook GD, Nicholls MJ (2007) Estimation of tropical cyclone wind hazard for Darwin: comparison with two other locations and the Australian wind loading code. J Appl Meteorol Clim 48:2331–2340

    Article  Google Scholar 

  6. Durbin J, Watson GS (1951) Testing for serial correlation in least squares regression. Biometrika 37:409–428

    Google Scholar 

  7. Elsner JB, Kossin JP, Jagger TH (2008) The increasing intensity of the strongest tropical cyclones. Nature 455:92–95

    Article  Google Scholar 

  8. Emanuel K (1987) The dependence of hurricane intensity on climate. Nature 326:483–485

    Article  Google Scholar 

  9. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688

    Article  Google Scholar 

  10. Emanuel K (2008) The hurricane-climate connection. Bull Am Meteorol Soc 89:ES10–ES20

    Google Scholar 

  11. Emanuel K (2011) Global warming effects on US hurricane damage. Weather Clim Soc 3:261–268

    Article  Google Scholar 

  12. Emanuel K (2013) Increased global tropical cyclone activity from global warming: results of downscaling CMIP5 climate models. Presented at the international summit on hurricanes and climate change, Kos, Greece

  13. Emanuel K, Sundararajan R, Williams J (2008) Hurricanes and global warming: results from downscaling IPCC AR4 simulations. Bull Am Meteorol Soc 89:347–367

    Article  Google Scholar 

  14. Garnaut R (2010) Garnaut climate change review. http://www.garnautreview.org.au/domino/Web_Notes/Garnaut/garnautweb.html

  15. Hallegate S (2007) The use of synthetic hurricane tracks in risk analysis and climate change damage assessment. J Appl Meteorol Clim 46:1956–1966

    Article  Google Scholar 

  16. Hansen J, Sato M (2006) Global temperature change. Proc Natl Sci Acad USA 103:14288–14293

    Article  Google Scholar 

  17. Hansen J, Sato M, Reudy R (2012) Perception of climate change. Proc Natl Sci Acad USA. doi:10.1073/pnas.1205276109

  18. Holland GJ (1997) The maximum potential intensity of tropical cyclones. J Atmos Sci 54:2519–2541

    Google Scholar 

  19. Intergovernmental Panel on Climate Change (IPCC) (2001) Climate shange 2001: the scientific basis. Cambridge University Press, Cambridge

  20. Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge

  21. Intergovernmental Panel on Climate Change (IPCC) (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge, UK, A special report of working groups I and II of the Intergovernmental Panel on Climate Change, pp 582

  22. Keeling CD, Piper SC, Bacastow RB, Wahlen M, Whorf TP, Heimann M, Meijer HA (2005) Atmospheric \(\text{ CO }_{2}\) and \(^{13}\text{ CO }_{2}\) exchange with the terrestrial biosphere and oceans from 1978 to 2000: observations and carbon cycle implications. In: Ehleringer JR, Cerling TE, Dearing MD (eds) A history of atmospheric \(\text{ CO }_{2}\) and its effects on plants, animals, and ecosystems. Springer, New York, pp 83–113

  23. Kossin JP, Knapp KR, Vimont DJ, Murnane RJ, Harper BA (2007) A globally consistent reanalysis of hurricane variability and trends. Geophys Res Lett 34:L04815

    Article  Google Scholar 

  24. Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, Srivastava AK, Sugi M (2010) Tropical cyclones and climate change. Nat Geosci 3:157–163

    Article  Google Scholar 

  25. Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46:33–50

    Article  Google Scholar 

  26. Landsea CW, Harper BA, Hoarau K, Knaff JA (2006) Can we detect trends in extreme tropical cyclones? Science 313:452–454

    Article  Google Scholar 

  27. Landsea C, Vecchi GA, Bengtsson L, Knutson TR (2009) Impact of duration thresholds on Atlantic tropical cyclone counts. J Clim 23:2508–2519

    Article  Google Scholar 

  28. Meat and Livestock Australia (MLA) (2012) Prices and markets. North Sydney, Australia. Available at http://www.mla.com.au/Prices-and-markets

  29. Mendelsohn R, Emanuel K, Chonabayashi S, Bakkenshen L (2012) The impact of climate change on global tropical cyclone damage. Nat Clim Chang 2:205–209

    Article  Google Scholar 

  30. Murnane RJ, Elsner JB (2012) Maximum wind speeds and US hurricane losses. Geophys Res Lett 39:L16707. doi:10.1029/2012GL052740

    Article  Google Scholar 

  31. Nakicenovic N, Davidson O, Davis G et al (2000) Emissions scenarios. A special report of working group III of the Intergovernmental Panel on Climate Change. IPCC, Geneva

    Google Scholar 

  32. National Oceanic Atmospheric Administration (NOAA) (2009) Tropical cyclone reports. National Hurricane Center, NOAA

  33. Nordhaus W (2010) The economics of hurricanes and implications of global warming. Clim Chang Econ 1:1–24

    Article  Google Scholar 

  34. Pielke RA, Gratz J, Landsea CW, Collins D, Saunders MA, Musulin R (2008) Normalized hurricane damages in the United States: 1900–2005. Nat Hazards Rev 9:29–42

    Article  Google Scholar 

  35. Rahmstorf S, Coumou D (2011) Increase of extreme events in a warming world. Proc Natl Sci Acad USA 108:17905–17909

    Article  Google Scholar 

  36. Real Estate Institute of Australia (REIA) (2012) REIA Datacube. ACT, Australia

  37. Seo SN (2012) Decision making under climate risks: an analysis of sub-Saharan farmers’ adaptation behaviors. Weather Clim Soc 4:285–299

    Article  Google Scholar 

  38. Sydney Morning Herald (SMH) (2011) Yasi spares life, crushes communities. SMH (Feb 3, 2011). http://news.smh.com.au/breaking-news-national/yasi-spares-life-crushes-communities-20110203-1ae14.html

  39. Somers J (1992) Building wealth through investment property. Somerset Financial Services, Australia

    Google Scholar 

  40. Tebaldi C, Hayhoe K, Arblaster JM, Meehl GE (2007) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Clim Chang 82:233–234

    Article  Google Scholar 

  41. United States General Accounting Office (GAO) (2003) Flood insurance: challenges facing the national flood insurance program. GAO, Washington, DC #/Find\_Data/Products\_and\_Data\_Available/GTOPO30

  42. United States Geological Survey (USGS) (2004) Global 30 arc second elevation data. 20 USGS National Mapping Division, EROS Data Centre, USGS. Available at http://eros.usgs.gov

  43. Vecchi GA, Knutson TR (2008) On estimates of historical North Atlantic tropical cyclone activity. J Clim 21:3580–3600

    Article  Google Scholar 

  44. Webster PJ, Holland GJ, Curry JA, Chang HR (2005) Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309:1844–1846

    Article  Google Scholar 

  45. White H (1980) A heteroscedasticity-consistent covariance matrix estimator and a direct test for Heteroscedasticity. Econometrica 48:817–838

    Article  Google Scholar 

  46. World Bank (2012) World Bank Data: Australia. World Bank. Washington, DC, USA. Available at http://data.worldbank.org/country/australia

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Acknowledgments

I would like to express my gratitude to Robert Mendelsohn and Laura Bakkensen for the comments on various drafts and to Kerry Emanuel for the advice on future TC projections. An earlier version was presented at the 4th International Summit on Hurricanes and Climate Change held in Kos, Greece on June 15th, 2013. I am thankful to the participants’ comments and James Elsner in particular. This project was funded by the International Research Office at the University of Sydney.

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Correspondence to S. Niggol Seo.

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Seo, S.N. Estimating Tropical Cyclone Damages Under Climate Change in the Southern Hemisphere Using Reported Damages. Environ Resource Econ 58, 473–490 (2014). https://doi.org/10.1007/s10640-013-9744-x

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Keywords

  • Adaptations
  • Climate change
  • Extreme events
  • Southern Hemisphere
  • Tropical cyclones