The examination of fluctuations in the correlations betweenδ13C andδ18O is of significant importance for the reconstruction of the Earth's climate history.A key challenge in paleoclimatology is finding a suitabl...The examination of fluctuations in the correlations betweenδ13C andδ18O 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 betweenδ13C andδ18O.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 betweenδ13C andδ18O poses a chal-lenge in developing reliable and interpretable approaches.The transition network,which involves embedding theδ13C andδ18O 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 betweenδ13C andδ18O 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.展开更多
基金supported by 2022 Subject Development Research Fund Project of China University of Geosciences,Beijing(Grant No.2022XK211)2023 Graduate Innovation Fund Project of China University of Geosciences,Beijing(Grant No.YB2023YC014)supported by the National Natural Science Foundation of China(Grant No.42174149 and No.42272134).
文摘The examination of fluctuations in the correlations betweenδ13C andδ18O 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 betweenδ13C andδ18O.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 betweenδ13C andδ18O poses a chal-lenge in developing reliable and interpretable approaches.The transition network,which involves embedding theδ13C andδ18O 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 betweenδ13C andδ18O 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.
