001     1052666
005     20260127203443.0
024 7 _ |a 10.1007/s00382-025-07880-9
|2 doi
024 7 _ |a 0930-7575
|2 ISSN
024 7 _ |a 1432-0894
|2 ISSN
024 7 _ |a 10.34734/FZJ-2026-01038
|2 datacite_doi
037 _ _ |a FZJ-2026-01038
082 _ _ |a 550
100 1 _ |a Riechers, Keno
|0 P:(DE-HGF)0
|b 0
245 _ _ |a Discontinuous stochastic forcing in Greenland ice core data
260 _ _ |a Heidelberg
|c 2025
|b Springer
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1769499346_24660
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Paleoclimate proxy records from Greenland ice cores, archiving e.g. d18O as a proxy for surface temperature, show that sudden climatic shifts called Dansgaard–Oeschger events (DO) occurred repeatedly during the last glacial interval. They comprised substantial warming of the Arctic region from cold to milder conditions. Concomitant abrupt changes in the dust concentrations of the same ice cores suggest that sudden reorganisations of the hemispheric-scale atmospheric circulation have accompanied the warming events. Genuine bistability of the North Atlantic climate system is commonly hypothesised to explain the existence of stadial (cold) and interstadial (milder) periods in Greenland. However, the physical mechanisms that drove abrupt transitions from the stadial to the interstadial state, and more gradual yet still abrupt reverse transitions, remain debated. Here, we conduct a one-dimensional data-driven analysis of the Greenland temperature and atmospheric circulation proxies under the purview of stochastic processes. We take the Kramers–Moyal equation to estimate each proxy’s drift and diffusion terms within a Markovian model framework. We then assess noise contributions beyond Gaussian white noise. The resulting stochastic differential equation (SDE) models feature a monostable drift for the Greenland temperature proxy and a bistable one for the atmospheric circulation proxy. Indicators of discontinuity in stochastic processes suggest to include higher-order terms of the Kramers–Moyal equation when modelling the Greenland temperature proxy’s evolution. This constitutes a qualitative difference in the characteristics of the two time series, which should be further investigated from the standpoint of climate dynamics.
536 _ _ |a 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112)
|0 G:(DE-HGF)POF4-1121
|c POF4-112
|f POF IV
|x 0
536 _ _ |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)
|0 G:(DE-Juel1)HDS-LEE-20190612
|c HDS-LEE-20190612
|x 1
536 _ _ |a HGF-ZT-I-0029 - Helmholtz UQ: Uncertainty Quantification - from data to reliable knowledge (HGF-ZT-I-0029)
|0 G:(DE-Ds200)HGF-ZT-I-0029
|c HGF-ZT-I-0029
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Morr, Andreas
|0 P:(DE-HGF)0
|b 1
|e Corresponding author
700 1 _ |a Lehnertz, Klaus
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Lind, Pedro G.
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Boers, Niklas
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Witthaut, Dirk
|0 P:(DE-Juel1)162277
|b 5
700 1 _ |a Gorjão, Leonardo Rydin
|0 P:(DE-HGF)0
|b 6
773 _ _ |a 10.1007/s00382-025-07880-9
|g Vol. 63, no. 12, p. 465
|0 PERI:(DE-600)1471747-5
|n 12
|p 465
|t Climate dynamics
|v 63
|y 2025
|x 0930-7575
856 4 _ |u https://juser.fz-juelich.de/record/1052666/files/s00382-025-07880-9.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1052666
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)162277
913 1 _ |a DE-HGF
|b Forschungsbereich Energie
|l Energiesystemdesign (ESD)
|1 G:(DE-HGF)POF4-110
|0 G:(DE-HGF)POF4-112
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-100
|4 G:(DE-HGF)POF
|v Digitalisierung und Systemtechnik
|9 G:(DE-HGF)POF4-1121
|x 0
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-27
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2024-12-27
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-27
915 _ _ |a DEAL Springer
|0 StatID:(DE-HGF)3002
|2 StatID
|d 2024-12-27
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-27
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2024-12-27
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2024-12-27
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b CLIM DYNAM : 2022
|d 2024-12-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-27
920 _ _ |l no
920 1 _ |0 I:(DE-Juel1)ICE-1-20170217
|k ICE-1
|l Modellierung von Energiesystemen
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)ICE-1-20170217
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21