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.
| Original language | English |
|---|---|
| Article number | 540 |
| Journal | Fractal and Fractional |
| Volume | 7 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 13 Jul 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Climate projections
- Computer Sciences and Mathematical Tools
- Population Health
- climate change
- scaling behavior
- thresholds of climate extremes
- multifractal detrended fluctuation analysis
- small and large fluctuations
ASJC Scopus subject areas
- Analysis
- Statistical and Nonlinear Physics
- Statistics and Probability
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