Resumen
Climate variabilities over the period of 80 years (1930?2010) are analyzed by the combined use of divergence measures and rank correlation. First, on the basis of a statistical linguistics procedure, the m-th order differences of the monthly mean precipitations and temperatures on the globe are symbolized according to a binary coding rule. Subsequently, the annual 12-bit binary sequence for a station is divided into twelve 6-bit sequences by scanning it over a year. Computed results indicate that there is an optimal order of differences with which one can reveal the variabilities most distinctly. Specifically, it is found that for the analysis of precipitations, the second differences (m = 2) are most useful, whereas, for the temperatures, the third differences (m = 3) are preferable. A detailed comparison between the information-theoretic and the ranking methods suggests that along with the stability and coherence, owing to its ability to make an appeal to the eyes, the latter is superior to the former.