Climatic Change

, Volume 43, Issue 2, pp 455–476 | Cite as

The Potential Impact of Global Warming on Hail Losses to Winter Cereal Crops in New South Wales

  • H. J. McMaster


This study was undertaken to determine the impact of potential global warming on the magnitude of hail losses to winter cereal crops within two areas situated on the western slopes of New South Wales, Australia. A model relating historical crop hail losses to climatic variables was developed for each area. These models included seasonal measures of vertical instability, low-level moisture and the height of the freezing level. In both areas, windshear was not found to be an important factor influencing seasonal crop hail losses. The two crop hail loss models were then used in conjunction with upper-air climatic data from three single mixed-layer global climate models (GCMs). Each GCM was run for 1 × CO2 conditions and for 2 × CO2 conditions. The enhanced greenhouse effect on climatic variables was taken to be the difference between their values for these two runs. Changes to climatic variables were then translated directly into changes in the percentage value of the winter cereal crop lost due to hail. In both areas, the three GCMs agreed concerning the direction of change in each of the variables used in the crop hail loss model. GCM simulations of the greenhouse effect resulted in a decline in winter cereal crop hail losses, with the exception of one GCM simulation at one location where losses increased slightly. None of the changes due to the enhanced greenhouse effect, however, were significant owing to a large observed seasonal variability of crop hail losses. Also, the simulated seasonal variability of crop hail losses did not change significantly due to the enhanced greenhouse effect. These results depended on two important assumptions. Firstly, it was assumed that the dominant relationships between climatic variables and crop hail losses in the past would remain the same in a future climate. Secondly, it was assumed that the single mixed-layer GCMs used in the study were correctly predicting climate change under enhanced greenhouse conditions.


Global Warming Climatic Variable Seasonal Variability Potential Global Warming Global Climate Model 
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  1. Allan, R.: 1994, ‘Historical Climatology of Pressure Anomalies, ENSO and Australian Rainfall’, Agric. Syst. Inform. Technol. 6(2), 15–18.Google Scholar
  2. Changnon, S. A.: 1977, ‘The Scales of Hail’, J. Appl. Meteorol. 16, 626–648.Google Scholar
  3. Colquhoun, J. R.: 1987, ‘A Decision Tree Method of Forecasting Thunderstorms, Severe Thunderstorms and Tornadoes’, Wea. Forecasting 2, 337–345.Google Scholar
  4. Dessens, J.: 1995, ‘Severe Convective Weather in the Context of a Nighttime Global Warming’, Geophys. Res. Lett. 22, 1241–1244.Google Scholar
  5. Gigliotti, P., Ward, R., Nowak, L., and Beard, G.: 1992, Analysis of Total-Totals Index for Melbourne, Meteorological Note 199, Bureau of Meteorology, Australia, p. 8.Google Scholar
  6. Griffiths, D. J., Colquhoun, J. R., Batt, K. L., and Casinader, T. R.: 1993, ‘Severe Thunderstorms in New South Wales: Climatology and Means of Assessing the Impact of Climate Change’, Clim. Change 25, 369–388.Google Scholar
  7. Hart, T. L., Bourke, W., McAvaney, B. J., Forgan, B. W., and McGregor, J. L.: 1990, ‘Atmospheric General Circulation Simulations with the BMRC Global Spectral Model: The Impact of Revised Physical Parameterization’, J. Climate 3, 436–459.Google Scholar
  8. Henderson-Sellers, A., Dickinson, R. E., Durbridge, T. B., Kennedy, P. J., McGuffie, K., and Pitman, A. J.: 1993, ‘Tropical Deforestation: Modelling Local to Regional-Scale Climate Change’, J. Geophys. Res. 98, 7289–7315.Google Scholar
  9. LaDochy, S.: 1985, ‘Climatic Characteristics of Hailstorms in Agricultural Manitoba, Canada’, Geographical Perspectives 55 (Spring), 15–25.Google Scholar
  10. McMaster, H. J.: 1997, Climate and Hail Losses to Winter Cereal Crops in New South Wales, Ph.D. Thesis, Macquarie University, Sydney, Australia, p. 206.Google Scholar
  11. Meehl, G. A. and Washington, W. M.: 1996, ‘El Niño-like Climate Change in a Model with Increased Atmospheric CO2 Concentrations’, Nature 382, 56–60.Google Scholar
  12. Miller, R. C.: 1972, Central Technical Report 200 (Rev), Notes on Analysis and Severe Storm Forecasting Procedures of the Air Force Global Weather Central, Air Weather Service, Scott Air Force Base.Google Scholar
  13. Nicholls, N., Lavery, B., Frederiksen, C., Drosdowsky, W., and Torok, S.: 1996, ‘Recent Apparent Changes in Relationships between the El Niño — Southern Oscillation and Australian Rainfall and Temperature’, Geophys. Res. Lett. 23, 3357–3360.Google Scholar
  14. Nicholls, N.: 1997, ‘Increased Australian Wheat Yields due to Recent Climate Trends’, Nature 387, 484–485.Google Scholar
  15. Pittock, A. B. and Salinger, M. J.: 1991, ‘Southern Hemisphere Climate Scenarios’, Clim. Change 13, 205–222.Google Scholar
  16. Ryan, C. J.: 1992, Dynamical Classification of Australian Thunderstorms, Meteorological Study No. 40, Bureau of Meteorology, Australian Government Publishing Service, Canberra, 1992, p. 61.Google Scholar
  17. Tett, S. F. B., Mitchell, J. F. B., Parker, D. E., and Allen, M. R.: 1996, ‘Human Influence on the Atmospheric Vertical Temperature Structure: Detection and Observations’, Science 274, 1170–1173.Google Scholar
  18. Watterson, I. G., Dix, M. R., Gordon, H. B., and McGregor, J. L.: 1995, ‘The CSIRO Nine-Level Atmospheric General Circulation Model and its Equilibrium Present and Doubled CO2 Climates’, Aust. Met. Mag. 44, 111–125.Google Scholar
  19. Weisman, M. L. and Klemp, J. B.: 1982, ‘The Dependence of Numerically Simulated Convective Storms on Vertical Wind Shear and Buoyancy’, Mon. Wea. Rev. 110, 504–520.Google Scholar
  20. Weisman, M. L. and Klemp, J. B.: 1984, ‘The Structure and Classification of Numerically Simulated Convective Storms in Directionally Varying Wind Shears’, Mon. Wea. Rev. 112, 2479–2498.Google Scholar
  21. Whetton, P. H., England, M. H., O'Farrell, S. P., Watterson, I. G., and Pittock, A. B.: 1996, ‘Global Comparison of the Regional Rainfall Results of Enhanced Greenhouse Coupled and Mixed Layer Ocean Experiments: Implications for Climate Change Scenario Development’, Clim. Change 33, 497–519.Google Scholar
  22. Willemse, S.: 1995, A Statistical Analysis and Climatological Interpretation of Hailstorms in Switzerland, Doctor of Natural Sciences Thesis Dissertation No. 11137, Swiss Federal Institute of Technology, Zurich, p. 176.Google Scholar
  23. Williamson, D. L., Kiehl, J. T., Ramanathan, V., Dickinson, R. E., and Hack, J. J.: 1987, Description of NCAR Community Climate Model (CCM1), NCAR Technical Note, NCAR/TC-285+STR, Boulder, CO, p. 112.Google Scholar

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© Kluwer Academic Publishers 1999

Authors and Affiliations

  • H. J. McMaster
    • 1
  1. 1.Macquarie UniversityNew South WalesAustralia

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