Preprints
https://doi.org/10.5194/essd-2024-555
https://doi.org/10.5194/essd-2024-555
31 Mar 2025
 | 31 Mar 2025
Status: this preprint is currently under review for the journal ESSD.

Pollen-based reconstruction of spatially-explicit vegetation cover over the Tibetan Plateau since the last deglaciation

Pengchao Zhang, Yi Luo, Dan Liu, Xiaoyi Wang, and Tao Wang

Abstract. Spatiotemporally contiguous paleo-vegetation reconstructions are essential for studying climate-vegetation interactions, providing critical data for paleoclimate modeling, and refining past land cover in Earth System Models (ESMs) and scenarios of anthropogenic land-cover changes (ALCCs). Here, we present the first spatiotemporally contiguous paleo-vegetation cover dataset for the Tibetan Plateau, spanning from the last deglaciation (16 ka) to the preindustrial era. This dataset was achieved using two sets of random forest (RF) models: one focused on temporal reconstructions (RF-temporal) and the other on spatial reconstructions (RF-spatial). RF-temporal reconstructs temporal trends from 61 fossil pollen records across the Tibetan Plateau, while RF-spatial interpolates site-based cover, producing a dataset with a spatial resolution of 0.5° × 0.5° and a temporal resolution of 400 years. The dataset provides estimates of vegetation cover, along with standard errors, for three vegetation types (vegetation, woody plant, and herbaceous plant). To illustrate, we present the temporal trends and spatial distribution of vegetation cover for these vegetation types, comparing them with the vegetation cover used in ESMs. We further discuss the dataset’s reliability and applications, along with the discrepancies between our reconstructed results and those used in ESMs, highlighting possible reasons for these differences.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Pengchao Zhang, Yi Luo, Dan Liu, Xiaoyi Wang, and Tao Wang

Status: open (until 07 May 2025)

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Pengchao Zhang, Yi Luo, Dan Liu, Xiaoyi Wang, and Tao Wang
Pengchao Zhang, Yi Luo, Dan Liu, Xiaoyi Wang, and Tao Wang

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Short summary
Understanding climate-vegetation interactions is key to predicting environmental changes. We reconstructed past vegetation cover on the Tibetan Plateau, a climate-sensitive and ecologically vital region, using machine learning. This spatiotemporal dataset, spanning 16,000 years, reveals vegetation changes and improves understanding of climate dynamics. It also provides crucial data for climate models to simulate past and future impacts, enhancing predictions of vegetation-climate feedbacks.
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