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Multifractal analysis to study break points in temperature data sets.

A P García-Marín1, J Estévez1, J A Alcalá-Miras1

  • 1Engineering Projects Area, Department of Rural Engineering, University of Cordoba, Cordoba 14071, Spain.

Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

This study uses multifractal analysis to validate break points in temperature data. It helps select the best statistical test for detecting climate change-related inhomogeneities.

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Area of Science:

  • Climatology and Meteorology
  • Complex Systems Analysis
  • Statistical Physics

Background:

  • Global air surface temperatures show increasing trends and regional variations.
  • Detecting inhomogeneities in meteorological data is crucial for climate change studies.
  • Different statistical tests may yield conflicting results for break point detection.

Purpose of the Study:

  • To employ multifractal properties to determine the correct break point year in temperature data series.
  • To compare the efficacy of the Pettitt and Standard Normal Homogeneity tests.
  • To establish criteria for selecting the most suitable inhomogeneity detection method.

Main Methods:

  • Analysis of multifractal properties of monthly temperature data series.
  • Application of the box counting method to derive fractal dimension function Dq and multifractal spectrum.
  • Comparison of multifractal characteristics before and after splitting data series at potential break points.

Main Results:

  • Multifractal analysis confirmed break points when different fractal dimensions and spectra were observed.
  • When multifractal parameters remained consistent, the proposed break point year was rejected.
  • The study provides a method to validate break point detection in climate data.

Conclusions:

  • Multifractal analysis offers a robust approach to verify break point detection in temperature series.
  • This method aids in selecting appropriate statistical tests for climate data analysis.
  • Accurate detection of inhomogeneities is vital for reliable climate change impact assessments.