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Multifractal features and dynamical thresholds of temperature extremes in Bangladesh

  • Anxin Liu
    ,
  • Zhihua Zhang
    ,
  • James Crabbe
    ,
  • Lipon Chandra Das
  • Shandong University
    ,
  • Beijing Normal University
    ,
  • University of Oxford
    ,
  • University of Chittagong
Research Output:
Contribution to journal
Article
 Peer-review

Open access

Sustainable Development Goals

  • SDG 13 - Climate Action
    SDG 13 Climate Action

Abstract

Multifractal detrended fluctuation analysis (DFA) can extract multi-scaling behavior andmeasure long-range correlations in climatic time series. In this study, with the help of multifractalDFA, we investigated the scaling behavior of daily minimum/maximum temperatures during theyears 1989–2019 from 34 meteorological stations in Bangladesh. We revealed spatial patterns, topographicimpacts and global warming impacts of long-range correlations embedded in small andlarge fluctuations in temperature time series. Meanwhile, we developed a multifractal DFA-basedalgorithm to dynamically determine thresholds to discriminate extreme and non-extreme eventsin climate systems and applied it to analyze the frequency and trends of temperature extremes in Bangladesh. Compared with widely-used percentile thresholds, the extreme climate events capturedin our algorithm are more reliable since they are determined dynamically by the climate system itself.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Article number

540

Journal (Volume, Issue Number)

Fractal and Fractional (Volume 7, Issue 7)

Publication milestones

  • Accepted/In press - 09/07/2023
  • Published - 13/07/2023

Publication status

Published - 13/07/2023

Publication IDs

  • handle.net: 10547/625952
  • Scopus: 85165937829

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