Datensatz

Zusammenfassung

Identifying and characterising dynamical regime shifts, critical transitions
or potential tipping points in palaeoclimate time series is relevant for im-
proving the understanding of often highly nonlinear Earth system dynamics.
Beyond linear changes in time series properties such as mean, variance, or
trend, these nonlinear regime shifts can manifest as changes in signal pre-
dictability, regularity, complexity, or higher-order stochastic properties such
as multi-stability. In recent years, several classes of methods have been put
forward to study these critical transitions in time series data that are based
on concepts from nonlinear dynamics, complex systems science, information
theory, and stochastic analysis. These include approaches such as phase
space-based recurrence plots and recurrence networks, visibility graphs, or-
der pattern-based entropies, and stochastic modelling. Here, we review and
compare in detail several prominent methods from these fields by applying
them to the same set of marine palaeoclimate proxy records of African cli-
mate variations during the past 5 million years. Applying these methods,
we observe notable nonlinear transitions in palaeoclimate dynamics in these
marine proxy records and discuss them in the context of important climate
events and regimes such as phases of intensified Walker circulation, marine
isotope stage M2, the onset of northern hemisphere glaciation and the mid-Pleistocene transition. We find that the studied approaches complement each
other by allowing us to point out distinct aspects of dynamical regime shifts
in palaeoclimate time series. We also detect significant correlations of these
nonlinear regime shift indicators with variations of Earth’s orbit, suggest-
ing the latter as potential triggers of nonlinear transitions in palaeoclimate.
Overall, the presented study underlines the potentials of nonlinear time se-
ries analysis approaches to provide complementary information on dynamical
regime shifts in palaeoclimate and their driving processes that cannot be re-
vealed by linear statistics or eyeball inspection of the data alone.