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

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 a 95 % confidence interval, for seven vegetation types (total vegetation, woody vegetation, herbaceous vegetation, coniferous forest, broadleaved forest, alpine steppe, and alpine meadow). 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, limitations, and applications, along with the discrepancies between our reconstructed results and those used in ESMs, highlighting possible reasons for these differences. This dataset presented here can be downloaded from https://doi.org/10.5281/zenodo.16908779 (Zhang, 2024).