Nonlinear time series analysis of palaeoclimate proxy records

dc.authoridMarwan, Norbert/0000-0003-1437-7039
dc.authoridEroglu, Deniz/0000-0001-6725-6949
dc.authoridDonner, Reik V./0000-0001-7023-6375
dc.authoridDonges, Jonathan/0000-0001-5233-7703
dc.authorwosidMarwan, Norbert/D-9576-2011
dc.authorwosidDonges, Jonathan F./HCI-2311-2022
dc.authorwosidEroglu, Deniz/GVS-9233-2022
dc.authorwosidDonner, Reik V./F-4400-2017
dc.contributor.authorEroğlu, Deniz
dc.contributor.authorDonges, Jonathan F.
dc.contributor.authorDonner, Reik, V
dc.contributor.authorEroglu, Deniz
dc.date.accessioned2023-10-19T15:12:15Z
dc.date.available2023-10-19T15:12:15Z
dc.date.issued2021
dc.department-temp[Marwan, Norbert; Donges, Jonathan F.; Donner, Reik, V] Leibniz Assoc, Potsdam Inst Climate Impact Res PIK, Telegrafenberg A31, D-14473 Potsdam, Germany; [Marwan, Norbert] Univ Potsdam, Inst Geosci, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany; [Donges, Jonathan F.] Stockholm Univ, Stockholm Resilience Ctr, Kraftriket 2B, S-11419 Stockholm, Sweden; [Donner, Reik, V] Magdeburg Stendal Univ Appl Sci, Dept Water Environm Construct & Safety, Breitscheidstr 2, D-39114 Magdeburg, Germany; [Eroglu, Deniz] Kadir Has Univ, Fac Engn & Nat Sci, TR-34083 Istanbul, Turkeyen_US
dc.description.abstractIdentifying and characterising dynamical regime shifts, critical transitions or potential tipping points in palaeoclimate time series is relevant for improving 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 predictability, 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, order 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 climate 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, suggesting the latter as potential triggers of nonlinear transitions in palaeoclimate. Overall, the presented study underlines the potentials of nonlinear time series analysis approaches to provide complementary information on dynamical regime shifts in palaeoclimate and their driving processes that cannot be revealed by linear statistics or eyeball inspection of the data alone. (C) 2021 The Authors. Published by Elsevier Ltd.en_US
dc.description.sponsorshipDFG [MA4759/8-1, MA4759/11-1, MA4759/9-1]; TUBITAK [118C236]; BAGEP Award of the Science Academy; Leibniz Association; European Research Council (project ERA) [ERC-2016-ADG-743080]en_US
dc.description.sponsorshipThis study was supported by the DFG projects MA4759/8e1 Impacts of uncertainties in climate data analyses (IUCliD), MA4759/9e1 Recurrence plot analysis of regime changes in dynamical systems, MA4759/11e1 Nonlinear empirical mode analysis of complex systems: Development of general approach and application in climate, by TUBITAK (Grant No. 118C236), by the BAGEP Award of the Science Academy, by the Leibniz Association (project DominoES), and the European Research Council (project ERA, ERC-2016-ADG-743080).en_US
dc.identifier.citation13
dc.identifier.doi10.1016/j.quascirev.2021.107245en_US
dc.identifier.issn0277-3791
dc.identifier.issn1873-457X
dc.identifier.scopus2-s2.0-85118833513en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.quascirev.2021.107245
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5388
dc.identifier.volume274en_US
dc.identifier.wosWOS:000723190100001en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofQuaternary Science Reviewsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRecurrence Quantification AnalysisEn_Us
dc.subjectLong-Term ChangesEn_Us
dc.subjectRegime ShiftsEn_Us
dc.subjectLyapunov ExponentsEn_Us
dc.subjectMonsoon VariabilityEn_Us
dc.subjectEmbedding DimensionEn_Us
dc.subjectComplex NetworksEn_Us
dc.subjectTipping ElementsEn_Us
dc.subjectPractical MethodEn_Us
dc.subjectEarths ClimateEn_Us
dc.subjectRecurrence Quantification Analysis
dc.subjectLong-Term Changes
dc.subjectRegime Shifts
dc.subjectLyapunov Exponents
dc.subjectMonsoon Variability
dc.subjectNonlinear time series analysisen_US
dc.subjectEmbedding Dimension
dc.subjectPalaeoclimate proxyen_US
dc.subjectComplex Networks
dc.subjectPlioceneen_US
dc.subjectTipping Elements
dc.subjectPleistoceneen_US
dc.subjectPractical Method
dc.subjectClimate transitionen_US
dc.subjectEarths Climate
dc.subjectRegime shiften_US
dc.titleNonlinear time series analysis of palaeoclimate proxy recordsen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication5bae555f-a8aa-4b95-bcfe-54cc47812e13
relation.isAuthorOfPublication.latestForDiscovery5bae555f-a8aa-4b95-bcfe-54cc47812e13

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