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Tracking δ¹³C and δ¹⁸O fluctuations uncovers stable modes and key patterns of paleoclimate

Shifeng Sun, Haiying Wang, Yongjian Huang

Geoscience Frontiers ›› 2024, Vol. 15 ›› Issue (4) : 101805.

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Geoscience Frontiers ›› 2024, Vol. 15 ›› Issue (4) : 101805. DOI: 10.1016/j.gsf.2024.101805

Tracking δ¹³C and δ¹⁸O fluctuations uncovers stable modes and key patterns of paleoclimate

• Shifeng Sun^a , 
• Haiying Wang^{a, *} , 
• Yongjian Huang^{b, c}

Author information +

^aSchool of Science, China University of Geosciences(Beijing), Beijing 100083, China

^bState Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences (Beijing), Beijing 100083, China

^cSchool of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China

* E-mail address: whycht@126.com

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Published
01 Jul 2024
Issue Date
23 Mar 2025

Abstract

The examination of fluctuations in the correlations between ${\delta }^{13}$C and ${\delta }^{18}$O is of significant importance for the reconstruction of the Earth’s climate history. A key challenge in paleoclimatology is finding a suitable method to represent the correlated fluctuation system between ${\delta }^{13}$C and ${\delta }^{18}$O. The method must be able to handle data sets with missing or inaccurate values, while still retaining the full range of dynamic information about the system. The non-linear and complex correlations between ${\delta }^{13}$C and ${\delta }^{18}$O poses a challenge in developing reliable and interpretable approaches. The transition network, which involves embedding the ${\delta }^{13}$C and ${\delta }^{18}$O sequence into the network using phase space reconstruction, is a coarse-grained based approach. This approach is well-suited to nonlinear, complex dynamic systems, and is particularly adept at emerging knowledge from low-quality datasets. We have effectively represented the fluctuations in the correlation between ${\delta }^{13}$C and ${\delta }^{18}$O since $66$ million years ago (Ma) using a system of complex network. This system, which has topological dynamical structures, is able to uncover the stable modes and key patterns in Cenozoic climate dynamics. Our findings could help to improve climate models and predictions of future climate change.

Keywords

Cenozoic climate dynamics / Correlation analysis / Complex network / Coarse-grained methods / Phase space reconstruction

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Shifeng Sun, Haiying Wang, Yongjian Huang. Tracking δ¹³C and δ¹⁸O fluctuations uncovers stable modes and key patterns of paleoclimate. Geoscience Frontiers, 2024, 15(4): 101805 https://doi.org/10.1016/j.gsf.2024.101805

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