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Climatic Change

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

Adapting stochastic weather generation algorithms for climate change studies

  • Daniel S. Wilks
Article

Abstract

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.

Keywords

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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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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