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Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
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Complexity Analysis of Global Temperature Time Series.

António M Lopes1, J A Tenreiro Machado2

  • 1UISPA-LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study quantifies global temperature time series complexity using four indices. Results reveal space-time climate variability, indicating diverse climate forcing processes.

Keywords:
Lempel–Ziv complexitycomplexitysample entropytemperature time series

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Area of Science:

  • Climate Science
  • Complex Systems Analysis
  • Time Series Analysis

Background:

  • Climate dynamics are intricate, challenging quantitative analysis.
  • Global temperature time series (TTS) complexity offers insights into climate evolution.
  • Existing methods may not fully capture the multifaceted nature of climate dynamics.

Purpose of the Study:

  • To quantify the complexity of the global temperature time series (TTS).
  • To investigate the space-time variability of climate complexity.
  • To identify potential distinct climate forcing processes.

Main Methods:

  • Pre-processing of monthly mean TTS using empirical mode decomposition to calculate the trend.
  • Estimation of detrended signal complexity using Lempel-Ziv complexity, sample entropy, signal harmonics power ratio, and fractal dimension.
  • Dimensional reduction and visualization using hierarchical clustering in a 4-dimensional space to a 2-dimensional space.

Main Results:

  • The four complexity indices capture distinct features of TTS dynamics.
  • TTS complexity demonstrates significant space-time variability.
  • Hierarchical clustering effectively reduces dimensionality while preserving key information.

Conclusions:

  • The calculated space-time variability in TTS complexity suggests the influence of distinct climate forcing processes.
  • The employed methodology provides an effective approach for analyzing climate dynamics.
  • This quantitative analysis enhances our understanding of complex climate system behavior.