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.2022 Jun 11;24(6):817.
doi: 10.3390/e24060817.

Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO2 Concentration Time Series during 2010-2018 over China

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Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO2 Concentration Time Series during 2010-2018 over China

Yiran Ma et al. Entropy (Basel)..

Abstract

Exploring the spatial distribution of the multi-fractal scaling behaviours in atmospheric CO2 concentration time series is useful for understanding the dynamic mechanisms of carbon emission and absorption. In this work, we utilise a well-established multi-fractal detrended fluctuation analysis to examine the multi-fractal scaling behaviour of a column-averaged dry-air mole fraction of carbon dioxide (XCO2) concentration time series over China, and portray the spatial distribution of the multi-fractal scaling behaviour. As XCO2 data values from the Greenhouse Gases Observing Satellite (GOSAT) are insufficient, a spatio-temporal thin plate spline interpolation method is applied. The results show that XCO2 concentration records over almost all of China exhibit a multi-fractal nature. Two types of multi-fractal sources are detected. One is long-range correlations, and the other is both long-range correlations and a broad probability density function; these are mainly distributed in southern and northern China, respectively. The atmospheric temperature and carbon emission/absorption are two possible external factors influencing the multi-fractality of the atmospheric XCO2 concentration.Highlight: (1) An XCO2 concentration interpolation is conducted using a spatio-temporal thin plate spline method. (2) The spatial distribution of the multi-fractality of XCO2 concentration over China is shown. (3) Multi-fractal sources and two external factors affecting multi-fractality are analysed.

Keywords: atmospheric XCO2 concentration; multi-fractal detrended fluctuation analysis; multi-fractal scaling behaviour; spatial distribution; spatio-temporal thin plate spline interpolation approach.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Spatial pattern of major vegetation types and four typical grid points with different climate, land cover and vegetation.
Figure 2
Figure 2
Comparison between the observed in-situ values and interpolated ones in WLG. (a) for STTPS (spatio-temporal TPS) and (b) for STPS (spatial TPS).
Figure 3
Figure 3
Generalised Hurst exponents of the original series, shuffle series, and surrogate series againstq for four typical grid points A, B, C, and D, respectively. Panels (A1–A3) for grid point A, panels (B1–B3) for grid point B, and so on.
Figure 4
Figure 4
Singular spectra of four original series for grid points a, b, c, and d. Panel (a) for A, panel (b) for B, and so on.
Figure 5
Figure 5
Spatial distribution of multi-fractal scaling behaviours for the original series. Panel (a) for scaling exponents, panel (b) for multi-fractal strength, panel (c) for maxium Hölder exponent and panel (d) for minimum Hölder exponent.
Figure 6
Figure 6
Spatial distribution of the multi-fractal sources. P-P is a broad probability density function, L-P is both long-range correlations and a broad probability density function, and L-L is long-range correlations.
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