Abstract

Interest in climate change has increased over the last 30 years due largely to global predictions associated with the greenhouse effect, which appear to lead to a substantial increase in planetary temperature. Implications of such results have led many scientists to examine climatic records from different regions of the world in order to understand temperature behavior. However, many researchers have noted that changes in temperature variability are also important in determining the future temperature distributions. In this context, we have analyzed a long-term record of monthly extreme minimum temperature registered at Guanajuato, Mexico. Data set was treated as a fractal profile to estimate the fractal dimension through variography (Dv) and power-spectral (Ds) approaches under two situations: (1) complete series, from January 1895 to December 1997 with 312 missing observations, and (2) partial series, from January, 1921 to April, 1963 with no missing values. In both cases, we obtained similar values for the two types of fractal dimensions meaning there is not a significant effect of missing values. The estimated fractal dimensions for the partial series (508 observations) are near 1.5 (Dv = 1.445 ± 0.06, Ds = 1.486 ± 0.155), which means monthly extreme minimum temperature is almost equally characterized by both short- and long-range variations. Evaluating through scaling arguments did not evidence multifractality in the scale range of two to 254 months. Then interpolation can make use of the fact that monthly extreme minimum temperature has a power-law spectrum. Interpolated data generated by this way may develop greater confidence in their capability to forecast near future climate.

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