Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8703-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-8703-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
19 Nov 2025
Methods for assessment of models |
| 19 Nov 2025
| 19 Nov 2025
Evaluation of annual trends in carbon cycle variables simulated by CMIP6 Earth system models in China
Ziyang Li
Institute of Applied Artificial Intelligence of The Guangdong-Hongkong-Macao Greater Bay, Shenzhen Polytechnic University, Shenzhen 518055, China
School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China
School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China
Lidong Zou
CORRESPONDING AUTHOR
Institute of Applied Artificial Intelligence of The Guangdong-Hongkong-Macao Greater Bay, Shenzhen Polytechnic University, Shenzhen 518055, China
School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China
Anzhou Zhao
CORRESPONDING AUTHOR
School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China
Haigang Zhang
Institute of Applied Artificial Intelligence of The Guangdong-Hongkong-Macao Greater Bay, Shenzhen Polytechnic University, Shenzhen 518055, China
Feng Yue
School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510062, China
Zhe Luo
School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China
Rui Bian
School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China
Ruihao Xu
School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China
Related authors
Anzhou Zhao, Ziyang Li, Lidong Zou, Jiansheng Wu, Kayla Stan, and Arturo Sanchez-Azofeifa
Earth Syst. Dynam., 16, 1935–1957, https://doi.org/10.5194/esd-16-1935-2025, https://doi.org/10.5194/esd-16-1935-2025, 2025
Short summary
Short summary
In this context, we used satellite observations of vegetation to evaluate long-term trends (2003–2019) in China simulated by dynamic global vegetation models (DGVMs). While these models capture the seasonal patterns of leaf area index (LAI) and gross primary production (GPP), they cannot accurately spatially represent the trend performance of LAI and GPP. The models tend to underestimate forests, overestimate grasslands, and struggle to represent cropland changes accurately.
Anzhou Zhao, Ziyang Li, Lidong Zou, Jiansheng Wu, Kayla Stan, and Arturo Sanchez-Azofeifa
Earth Syst. Dynam., 16, 1935–1957, https://doi.org/10.5194/esd-16-1935-2025, https://doi.org/10.5194/esd-16-1935-2025, 2025
Short summary
Short summary
In this context, we used satellite observations of vegetation to evaluate long-term trends (2003–2019) in China simulated by dynamic global vegetation models (DGVMs). While these models capture the seasonal patterns of leaf area index (LAI) and gross primary production (GPP), they cannot accurately spatially represent the trend performance of LAI and GPP. The models tend to underestimate forests, overestimate grasslands, and struggle to represent cropland changes accurately.
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Short summary
To understand how well current Earth system models simulate the natural world, we compared the models' outputs against measurements from satellites. Our results show these models struggle to accurately capture trends in variables related to carbon cycle, because the models can’t respond to human and environmental influences. This evaluation is crucial because improving these models will lead to more reliable forecasts of how ecosystems and the climate will change in the future.
To understand how well current Earth system models simulate the natural world, we compared the...
