Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3017-2024
https://doi.org/10.5194/essd-16-3017-2024
Data description paper
 | 
28 Jun 2024
Data description paper |  | 28 Jun 2024

Dataset of spatially extensive long-term quality-assured land–atmosphere interactions over the Tibetan Plateau

Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li

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Revised manuscript not accepted
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Cited articles

An, Y., Meng, X., Zhao, L., Li, Z., Wang, S., Shang, L., Chen, H., Lyu, S., Li, G., and Ma, Y.: Performance of GLASS and MODIS Satellite Albedo Products in Diagnosing Albedo Variations during Different Time Scales and Special Weather Conditions in the Tibetan Plateau, Remote Sens.-Basel, 12, 2456, https://doi.org/10.3390/rs12152456, 2020. 
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Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D. J., and Xu, L.: Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biol., 14, 1854–1876, https://doi.org/10.1111/j.1365-2486.2008.01606.x, 2008. 
Che, T., Li, X., Liu, S., Li, H., Xu, Z., Tan, J., Zhang, Y., Ren, Z., Xiao, L., Deng, J., Jin, R., Ma, M., Wang, J., and Yang, X.: Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China, Earth Syst. Sci. Data, 11, 1483–1499, https://doi.org/10.5194/essd-11-1483-2019, 2019. 
Chen, F., Ding, L., Piao, S., Zhou, T., Xu, B., Yao, T., and Li, X.: The Tibetan Plateau as the engine for Asian environmental change: the Tibetan Plateau Earth system research into a new era, Sci. Bull., 66, 1263–1266, https://doi.org/10.1016/j.scib.2021.04.017, 2021. 
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Short summary
Current models and satellites struggle to accurately represent the land–atmosphere (L–A) interactions over the Tibetan Plateau. We present the most extensive compilation of in situ observations to date, comprising 17 years of data on L–A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region, and it enables new investigations of fine-scale L–A processes and their mechanistic drivers.

 
 
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