A modern pollen dataset from lake surface sediments on the central and western Tibetan Plateau

Abstract Modern pollen datasets are essential for pollen-based quantitative paleoclimate (e.g. precipitation) reconstructions, which can aid in better understanding recent climate change and its underlying forcing mechanisms. A modern pollen dataset based on surface sediments from 90 lakes in the shrub, meadow, steppe and desert regions of the central and western Tibetan Plateau (TP) was established to fill geographical gaps left by previous datasets. Ordination analyses of pollen data and climatic parameters revealed that annual precipitation is the dominant factor in modern pollen distribution on the central and western TP. A regional transfer function for annual precipitation was developed with weighted averaging partial least squares (WA-PLS), which suggests a good inference power of the modern pollen dataset for annual precipitation. A case study in which the transfer function was effectively applied to a fossil pollen record from Tangra Yumco on the central TP for paleoprecipitation reconstruction demonstrated the significance of the modern pollen dataset in a lower data region for paleoclimate change studies. Data from this study, including pollen data for each sample and information on the sampled sites (location, altitude and climate data), are openly available via the Zenodo portal (10.5281/zenodo.8008474, Ma et al., 2023).

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: A modern pollen dataset from lake surface sediments on the central and western Tibetan Plateau ; volume:16 ; number:1 ; year:2024 ; pages:311-320 ; extent:10 ; Earth system science data ; 16, Heft 1 (2024), 311-320 (gesamt 10)

Beteiligte, Orts- und Zeitangaben

• Urheber: Ma, Qingfeng ; Zhu, Liping ; Ju, Jianting ; Wang, Junbo ; Wang, Yong ; Huang, Lei ; Haberzettl, Torsten

Weitere Informationen

• DOI: 10.5194/essd-16-311-2024
• URN: urn:nbn:de:101:1-2024011803282371547202
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:27 MESZ