Evaluation of annual trends in carbon cycle variables  simulated by CMIP6 Earth system models in China

Li, Ziyang; Zou, Lidong; Zhao, Anzhou; Zhang, Haigang; Yue, Feng; Luo, Zhe; Bian, Rui; Xu, Ruihao

doi:10.5194/gmd-18-8703-2025

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.

https://doi.org/10.5194/gmd-18-8703-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 22

Methods for assessment of models

|

19 Nov 2025

Methods for assessment of models |

| 19 Nov 2025

Evaluation of annual trends in carbon cycle variables simulated by CMIP6 Earth system models in China

Ziyang Li, Lidong Zou, Anzhou Zhao, Haigang Zhang, Feng Yue, Zhe Luo, Rui Bian, and Ruihao Xu

Supplement

https://doi.org/10.5194/gmd-18-8703-2025-supplement

Data sets

CSIF Yao Zhang https://doi.org/10.6084/m9.figshare.6387494.v2

MOD17A3H MODIS/Terra Net Primary Production Yearly L4 Global 500 m SIN Grid V006 S. Running et al. https://doi.org/10.5067/MODIS/MOD17A3H.006

Global daily 0.05 deg spatiotemporal continuous land surface temperature dataset (2002-2022) T. Zhao and P. Yu https://doi.org/10.11888/Meteoro.tpdc.271663

MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V006 C. iMiceli et al. https://doi.org/10.5067/MODIS/MOD44B.006

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.