Detailed Information on Publication Record
2024
Methodological constrains of tree-ring stable isotope chronologies
AROSIO, Tito, Max TORBENSON, Tatiana BEBCHUK, Alexander KIRDYANOV, Jan ESPER et. al.Basic information
Original name
Methodological constrains of tree-ring stable isotope chronologies
Authors
AROSIO, Tito, Max TORBENSON, Tatiana BEBCHUK, Alexander KIRDYANOV, Jan ESPER, Takeshi NAKATSUKA, Masaki SANO, Otmar URBAN, Kurt NICOLUSSI, Markus LEUENBERGER and Ulf BÜNTGEN (276 Germany, belonging to the institution)
Edition
Quaternary Science Reviews, Elsevier Ltd, 2024, 0277-3791
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10511 Environmental sciences
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 4.000 in 2022
Organization unit
Faculty of Science
UT WoS
001285833500001
Keywords in English
Climate reconstructions; Proxy data; Stable isotopes; Spectral properties; Tree rings
Tags
Tags
International impact, Reviewed
Změněno: 16/8/2024 11:24, Mgr. Marie Šípková, DiS.
Abstract
V originále
Tree-ring stable isotope (TRSI) chronologies that combine information from living and relict wood have the potential to capture long-term trends that might be missing in traditional tree-ring width and maximum latewood density measurements. Our understanding of the possible effects of different methods to develop TRSI chronologies is, however, still incomplete. Here, we compare and evaluate five such methods applied to three multi-millennial-long oxygen isotope (δ18O) TRSI datasets from central Europe, the European Alps and Japan: (a) raw data, (b) cohort correction, (c) interactive mean correction, (d) outlier correction, and (e) series normalization. We show that the spectral properties preserved in the final TRSI chronologies not only depend on the data used, but also on the techniques applied. Method (a) is particularly prone to outliers if the sample size is low. Method (b) may create artificial steps and trends when single measurement series share similar start dates and/or when end and start dates are systematically skewed. Methods (c) and (d) yield similar results for annually resolved data, yet (d) is more suitable for temporally pooled datasets and less sensitive to potential biological age effects. Method (e) removes any low-frequency signal. Our findings demonstrate the risks and rewards of different TRSI chronology development techniques that must be carefully adapted to both, the data used and the question posed.