Abstract
Weather generators (WGs) typically have unique advantages to the climate change impacts assessment. However, each of them has the weaknesses, strengths, and scope of their own. This paper aims to provide a comprehensive comparison of performance of the SDSM and IWG. It is important to know for what application range each WG can be used. Also, a method that strengthens the performance of one WG in a particular case can be used to modify another WG where it is weak. The performance of the models was investigated and compared directly and indirectly. The results showed that each of the two models reproduces many of the climate variables characteristics appropriately. Although WGs typically do not have appropriate performance in the reproduction of low frequency variability and extreme values, IWG well reproduces extreme rainfall values and low frequency variability of temperature. But this model relatively underestimates low frequency variability of rainfall. By contrast, the SDSM underestimates extreme values. However, it well reproduces low frequency variability of rainfall, because SDSM models the local variables depending on the large-scale atmospheric variables in which low frequency variability naturally exist. As a result, in order to correct the low frequency variability of rainfall in the IWG model, it is suggested that the rainfall statistics to be modeled depend on large-scale variables, and based on which, the parameters of the IWG be changed over the years. By correcting the low frequency variability of rainfall of IWG model, an appropriate WG would be obtained to climate change impacts assessment.
Published Version
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