Articles | Volume 13, issue 2
https://doi.org/10.5194/esd-13-833-2022
https://doi.org/10.5194/esd-13-833-2022
Research article
 | 
19 Apr 2022
Research article |  | 19 Apr 2022

Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products

Ruqi Yang, Jun Wang, Ning Zeng, Stephen Sitch, Wenhan Tang, Matthew Joseph McGrath, Qixiang Cai, Di Liu, Danica Lombardozzi, Hanqin Tian, Atul K. Jain, and Pengfei Han

Related authors

Weakening Correlation and Delaying Response Time of Ecosystem Water Use Efficiency to Drought
Zijun Wang, Rong Wu, Yangyang Liu, Zhaoying Zhang, Zhongming Wen, Zhenqian Wang, Stephen Sitch, and Wenping Yuan
EGUsphere, https://doi.org/10.5194/egusphere-2025-6195,https://doi.org/10.5194/egusphere-2025-6195, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Global wildfire patterns and drivers under climate change
Hemraj Bhattarai, Maria Val Martin, Stephen Sitch, David H. Y. Yung, and Amos P. K. Tai
Biogeosciences, 22, 7591–7610, https://doi.org/10.5194/bg-22-7591-2025,https://doi.org/10.5194/bg-22-7591-2025, 2025
Short summary
Uncertainties in fertilizer-induced emissions of soil nitrogen oxide and the associated impacts on ground-level ozone and methane
Cheng Gong, Yan Wang, Hanqin Tian, Sian Kou-Giesbrecht, Nicolas Vuichard, and Sönke Zaehle
Atmos. Chem. Phys., 25, 17009–17025, https://doi.org/10.5194/acp-25-17009-2025,https://doi.org/10.5194/acp-25-17009-2025, 2025
Short summary
HIStory of LAND transformation by humans in South America (HISLAND-SA): annual and 1 km gridded data for soybean, maize, wheat, and rice (1950–2020)
Binyuan Xu, Hanqin Tian, Shufen Pan, Xiaoyong Li, Ran Meng, Óscar Melo, Anne McDonald, María de los Ángeles Picone, Xiao-Peng Song, Edson Severnini, Katharine G. Young, and Feng Zhao
Earth Syst. Sci. Data, 17, 6353–6377, https://doi.org/10.5194/essd-17-6353-2025,https://doi.org/10.5194/essd-17-6353-2025, 2025
Short summary
History of anthropogenic Phosphorus inputs (HaPi) to the terrestrial biosphere from 1860 to 2020
Zihao Bian, Hao Shi, Rui Li, Fei Lun, Francesco Tubiello, Nathaniel Mueller, Shiyu You, Rong Hao, Jiageng Ma, Longhui Li, Changchun Huang, Bing He, Yuanzhi Yao, and Hanqin Tian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-507,https://doi.org/10.5194/essd-2025-507, 2025
Preprint under review for ESSD
Short summary

Cited articles

Alemohammad, S. H., Fang, B., Konings, A. G., Aires, F., Green, J. K., Kolassa, J., Miralles, D., Prigent, C., and Gentine, P.: Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence, Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, 2017. 
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C., Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Sitch, S., Viovy, N., Wiltshire, A., and Zhao, M.: Spatiotemporal patterns of terrestrial gross primary production: A review, Rev. Geophys., 53, 785–818, https://doi.org/10.1002/2015rg000483, 2015. 
Bacour, C., Maignan, F., MacBean, N., Porcar-Castell, A., Flexas, J., Frankenberg, C., Peylin, P., Chevallier, F., Vuichard, N., and Bastrikov, V.: Improving Estimates of Gross Primary Productivity by Assimilating Solar-Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process-Based SIF Model, J. Geophys. Res.-Biogeo., 124, 3281–3306, https://doi.org/10.1029/2019jg005040, 2019. 
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance and terrestrial photosynthesis, Science Advances, 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017. 
Badgley, G., Anderegg, L. D. L., Berry, J. A., and Field, C. B.: Terrestrial gross primary production: Using NIRV to scale from site to globe, Glob. Change Biol., 25, 3731–3740, https://doi.org/10.1111/gcb.14729, 2019. 
Download
Short summary
We comprehensively investigate historical GPP trends based on five kinds of GPP datasets and analyze the causes for any discrepancies among them. Results show contrasting behaviors between modeled and satellite-based GPP trends, and their inconsistencies are likely caused by the contrasting performance between satellite-derived and modeled leaf area index (LAI). Thus, the uncertainty in satellite-based GPP induced by LAI undermines its role in assessing the performance of DGVM simulations.
Share
Altmetrics
Final-revised paper
Preprint