2026-10-14T21:11:07Z https://www.scidb.cn/oai

10.57760/sciencedb.39501 2026-06-11T14:37:27Z

2026-06-11 A long-term (1950–2024) machine learning-corrected land surface temperature dataset over the Tibetan Plateau doi:10.57760/sciencedb.39501 en The Tibetan Plateau (TP), a critical amplifier of global climate change and regulator of Asia’s water resources, requires accurate land surface temperature (LST) records. However, current ERA5-Land reanalysis data exhibit substantial biases across this complex terrain. Here, we fuse high-density meteorological observations (1980–2020) with ERA5-Land using an ensemble machine learning approach, generating a corrected, high-resolution (0.1°) monthly LST dataset spanning 1950–2024. Validation demonstrates transformative improvements: the coefficient of determination (R²) increases from 0.79 to 0.96 and the root-mean-square error (RMSE) decreases from 9.26°C to 2.33°C. Critically, the corrected dataset uncovers a significantly stronger warming trend (0.32°C per decade, p < 0.05) than indicated by ERA5-Land (0.24°C per decade, p< 0.05). Warming is most pronounced in winter (locally exceeding 0.80°C per decade) and exhibits marked spatial heterogeneity, with maxima in western Ngari and minima in the southeastern TP. This pervasive ERA5-Land cold bias, particularly acute in cryosphere-sensitive high-altitude zones, necessitates urgent recalibration of climate models and impact projections dependent on these data. Our dataset provides an indispensable benchmark for understanding accelerated TP warming and its profound consequences for Asian water security, cryosphere loss, and global climate feedbacks. Tibetan Plateau; Land Surface Temperature; ERA5-Land; Machine Learning; Warming Trend Zhaoliang Zeng Zhanxiang Zhao Zhaohua Liu Ke Gui Yaqiang Wang Wenhua Zhang Ruqing Zhu Minghu Ding Chao He Yan Zhao Huizheng Che PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank