Abstract
Climate has complex dynamics due to the plethora of phenomena underlying its evolution. These characteristics pose challenges to conducting solid quantitative analysis and reaching assertive conclusions. In this paper, the global temperature time series (TTS) is viewed as a manifestation of the climate evolution, and its complexity is calculated by means of four different indices, namely the Lempel–Ziv complexity, sample entropy, signal harmonics power ratio, and fractal dimension. In the first phase, the monthly mean TTS is pre-processed by means of empirical mode decomposition, and the TTS trend is calculated. In the second phase, the complexity of the detrended signals is estimated. The four indices capture distinct features of the TTS dynamics in a 4-dim space. Hierarchical clustering is adopted for dimensional reduction and visualization in the 2-dim space. The results show that TTS complexity exhibits space-time variability, suggesting the presence of distinct climate forcing processes in both dimensions. Numerical examples with real-world data demonstrate the effectiveness of the approach.
Highlights
Understanding climatic variability is extremely important nowadays
The temperature time series (TTS) embeds rich information contributed by a multitude of distinct factors at different scales
Information from worldwide land meteorological stations was processed and its complexity was calculated in space and time by means of different indices
Summary
Understanding climatic variability is extremely important nowadays. The Earth’s climate is changing and we are facing an increasing number of extreme weather events, like floods, droughts and anomalous temperatures. Grieser et al [21] studied the monthly mean temperatures of European meteorological stations They applied statistical tools to decompose the TTS into significant components and found that the phase of the annual cycle shifts within the year, backwards and forwards in Western and Eastern Europe, respectively. The authors observed that the occurrence of extreme events increased significantly and corresponded mostly to cold peaks in winter They found large harmonic components with a period of 92.3 months in data from Northern and Western Europe. Numerical examples using real-world data for one century as well as the four complexity indices, illustrate the effectiveness of this new point of view for tackling TTS With these ideas in mind, the paper is organized as follows.
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