Measurement of climate complexity using sample entropy

A climate system is a complex nonlinear system. Estimation of the complexity is of great interest in climatic forecast and prediction. In this paper, we propose the use of sample entropy (SampEn), an entropy-based algorithm, to measure the complexity of daily temperature series. Estimations of SampEn were calculated for 50 meteorological stations in the mountains of Southwest China, particularly in Yunnan Province. On the basis of these data, stations were grouped in climatically homogenous regions (climate provinces), and the spatial pattern of SampEn for each climate province was investigated. The SampEn value of spatial distribution of climate provinces reflects the varying degree of influence of the monsoonal air masses. High SampEn values occur in interactive regions of different air masses, owing to large regional differences in weather processes, while the southwest region is under the influence of the Southwest Monsoon leading to a homogenous climatic environment, low SampEn values and small spatial variations of SampEn. The results suggest that SampEn is an alternative nonlinear approach for analyzing and predicting complexity of climatic time series. Copyright © 2006 Royal Meteorological Society