Climatic Change

, Volume 22, Issue 1, pp 67–84 | Cite as

Adapting stochastic weather generation algorithms for climate change studies

  • Daniel S. Wilks


While large-scale climate models (GCMs) are in principle the most appropriate tools for predicting climate changes, at present little confidence can be placed in the details of their projections. Use of tools such as crop simulation models for investigation of potential impacts of climatic change requires daily data pertaining to small spatial scales, not the monthly-averaged and large-scale information typically available from the GCMs. A method is presented to adapt stochastic weather generation models, describing daily weather variations in the present-day climate at particular locations, to generate synthetic daily time series consistent with assumed future climates. These assumed climates are specified in terms of the commonly available monthly means and variances of temperature and precipitation, including time-dependent (so-called ‘transient’) climate changes. Unlike the usual practice of applying assumed changes in mean values to historically observed data, simulation of meteorological time series also exhibiting changes in variability is possible. Considerable freedom in climate change ‘scenario’ construction is allowed. The results are suitable for investigating agricultural and other impacts of a variety of hypothetical climate changes specified in terms of monthly-averaged statistics.


Climate Change Daily Weather Climate Change Study Daily Time Series Predict Climate Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Angell, J. K.: 1986, ‘Annual and Seasonal Global Temperature Changes in the Troposphere and Low Stratosphere, 1960–1985’, Mon. Wea. Rev. 114, 1922–1930.Google Scholar
  2. Cohen, S. J.: 1990, ‘Bringing the Global Warming Issue Closer to Home: The Challenge of Regional Impact Studies’, Bull. Amer. Meteorol. Soc. 71, 520–526.Google Scholar
  3. Gates, W. L.: 1985, ‘The Use of General Circulation Models in the Analysis of the Ecosystem Impacts of Climate Change’, Clim. Change 7, 267–284.Google Scholar
  4. Grotch, S. L. and MacCracken, M. C.: 1991, ‘The Use of General Circulation Models to Predict Regional Climatic Change’ J. Clim. 4, 286–303.Google Scholar
  5. Hansen, J., Fung, I., Lacis, A., Rind, D., Lebedeff, S., Ruedy, R., and Russell, G.: 1988, ‘Global Climate Changes as Forecast by Goddard Institute for Space Studies Three-Dimensional Model’, J. Geophys. Res. D93, 9341–9364.Google Scholar
  6. Hutchinson, M. F.: 1986, ‘Methods of Generation of Weather Sequences’, in Bunting, A. H. (ed.), Agricultural Environments, C.A.B. International, Wallingford, 149–157.Google Scholar
  7. Karl, T. R., Kukla, G., and Gavin, J.: 1984, ‘Decreasing Diurnal Temperature Range in the United States and Canada from 1941 through 1980’, J. Clim. Applied Meteorol. 23, 1489–1504.Google Scholar
  8. Karl, T. R., Wang, W.-C., Schlesinger, M. E., Knight, R. W., and Portman, D.: 1990, ‘A Method of Relating General Circulation Model Simulated Climate to the Observed Local Climate. Part I: Seasonal Statistics’, J. Clim. 3, 1053–1079.Google Scholar
  9. Katz, R. W.: 1982, ‘Statistical Evaluation of Climate Experiments with General Circulation Models: A Parametric Time Series Modeling Approach’, J. Atmos. Sci. 39, 1446–1455.Google Scholar
  10. Katz, R. W.: 1983, ‘Statistical Procedures for Making Inferences about Precipitation Changes Simulated by an Atmospheric General Circulation Model’, J. Atmos. Sci. 40, 2193–2201.Google Scholar
  11. Katz, R. W: 1985, ‘Probabilistic Models’, in Murphy, A. H., and Katz, R. W. (eds.), Probability, Statistics, and Decision Making in the Atmospheric Sciences, Westview, 261–288.Google Scholar
  12. Katz, R. W. and Brown, B. G.: 1992, ‘Extreme Events in a Changing Climate: Variability is More Important than Averages’, Clim. Change, in press.Google Scholar
  13. Kendall, M. and Ord, J. K.: 1990, Time Series, third edition, Edward Arnold, 296 pp.Google Scholar
  14. Kim, J.-W., Chang, J.-T., Baker, N. L., Wilks, D. S., and Gates, W. L., 1984, ‘The Statistical Problem of Climate Inversion: Determination of the Relationship between Local and Large-Scale Climate’, Mon. Wea. Rev. 112, 2069–2077.Google Scholar
  15. LLNL (Lawrence Livermore National Laboratory) et al., 1990, Energy and Climate Change, Report of the Multi-Laboratory Climate Change Committee, Lewis, 161 pp.Google Scholar
  16. Manabe, S., Stouffer, R. J., Spelman, M. J., and Bryan, K.: 1991, ‘Transient Response of a Coupled Ocean-Atmosphere Model to Gradual Changes of Atmospheric CO2. Part I: Annual Mean Response’, J. Clim. 4, 785–818.Google Scholar
  17. Matalas, N. C.: 1967, ‘Mathematical Assessment of Synthetic Hydrology’, Water Resourc. Res. 3, 937–945.Google Scholar
  18. Mearns, L. O., Katz, R. W., and Schneider, S. H.: 1984, ‘Extreme High-Temperature Events: Changes in Their Probabilities with Changes in Mean Temperature’, J. Clim. Appl. Meteorol. 23, 1601–1613.Google Scholar
  19. Mearns, L. O., Schneider, S. H., Thompson, S. L., and McDaniel, L. R.: 1990, ‘Analysis of Climate Variability in General Circulation Models: Comparison with Observations and Changes in Variability in 2 × CO2 Experiments’, J. Geophys. Res. D95, 20469–20490.Google Scholar
  20. Neild, R. E., Richman, H. N., and Seeley, M. W.: 1979, ‘Impacts of Different Types of Temperature Change on the Growing Season for Maize’, Agricult. Meteorol. 20, 367–374.Google Scholar
  21. Parry, M. L. and Carter, T. R.: 1985, ‘The Effect of Climatic Variations on Agricultural Risk’, Clim. Change 7, 95–110.Google Scholar
  22. Reed, D. N.: 1986, ‘Simulation of Time Series of Temperature and Precipitation over Eastern England by an Atmospheric General Circulation Model’, J. Climatol. 6, 233–257.Google Scholar
  23. Richardson, C. W.: 1981, ‘Stochastic Simulation of Daily Precipitation, Temperature, and Solar Radiation’, Water Resourc. Res. 17, 182–190.Google Scholar
  24. Richardson, C. W. and Wright, D. A.: 1984, ‘WGEN: A Model for Generating Daily Weather Variables’, U.S. Dept. of Agriculture, ARS-8, 83 pp.Google Scholar
  25. Riebsame, W.: 1989, Assessing the Social Implications of Climate Fluctuations: A Guide to Climate Impact Studies, World Climate Impacts Programme, UNEP, Nairobi, 83 pp.Google Scholar
  26. Rind, D., Goldberg, R., and Ruedy, R.: 1989, ‘Change in Climate Variability in the 21st Century’, Clim. Change 14, 5–37.Google Scholar
  27. Rotmans, J., de Boois, H., and Swart, R. J.: 1990, An Integrated Model for the Assessment of the Greenhouse Effect: The Dutch Approach’, Clim. Change 16, 331–356.Google Scholar
  28. Santer, B. D., Wigley, T. M. L., Schlesinger, M. E., and Mitchell, J. F. B.: 1990, ‘Developing Scenarios from Equilibrium GCM Results’, Report No. 47, Max-Planck-Institut für Meteorologie, Hamburg, 29 pp.Google Scholar
  29. Schlesinger, M. E., and Mitchell, J. F. B.: 1987, ‘Climate Model Simulations of the Equilibrium Climatic Response to Increased Carbon Dioxide’, Rev. Geophys. 25, 760–798.Google Scholar
  30. Waggoner, P. E.: 1989, ‘Anticipating the Frequency Distributions of Precipitation if Climate Change Alters its Mean’, Agricult. Forest Meteorol. 47, 321–337.Google Scholar
  31. Washington, W. M. and Meehl, G. A.: 1989, ‘Climate Sensitivity due to Increased CO2: Experiments with a Coupled Atmosphere and Ocean General Circulation Model’, Clim. Dynam. 4, 1–38.Google Scholar
  32. Wilks, D. S.: 1986, ‘Specification of Local Surface Weather Elements from Large-Scale General Circulation Model Information, with Application to Agricultural Impact Assessment’, SCIL Report 86-1, Department of Atmospheric Sciences, Oregon State University, Corvallis 97331, 233 pp.Google Scholar
  33. Wilks, D. S.: 1989, ‘Statistical Specification of Local Surface Weather Elements from Large-Scale Information’, Theor. Appl. Clim. 40, 119–134.Google Scholar
  34. Wilson, C. A. and Mitchell, J. F. B.: 1987, ‘Simulated Climate and CO2-Induced Climate Change over Western Europe’, Clim. Change 10, 11–42.Google Scholar

Copyright information

© Kluwer Academic Publishers 1992

Authors and Affiliations

  • Daniel S. Wilks
    • 1
  1. 1.Department of Soil, Crop and Atmospheric SciencesCornell UniversityIthacaU.S.A.

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