Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-9111-2022
https://doi.org/10.5194/gmd-15-9111-2022
Model evaluation paper
 | 
20 Dec 2022
Model evaluation paper |  | 20 Dec 2022

Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)

Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan

Related authors

Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206,https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript under review for GMD
Short summary
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024,https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
How to reconstruct aerosol-induced diffuse radiation scenario for simulating GPP in land surface models? An evaluation of reconstruction methods with ORCHIDEE_DFv1.0_DFforc
Yuan Zhang, Olivier Boucher, Philippe Ciais, Laurent Li, and Nicolas Bellouin
Geosci. Model Dev., 14, 2029–2039, https://doi.org/10.5194/gmd-14-2029-2021,https://doi.org/10.5194/gmd-14-2029-2021, 2021
Short summary
Modeling the impacts of diffuse light fraction on photosynthesis in ORCHIDEE (v5453) land surface model
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020,https://doi.org/10.5194/gmd-13-5401-2020, 2020
Short summary

Related subject area

Biogeosciences
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025,https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025,https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025,https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025,https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025,https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary

Cited articles

Alekseychik, P., Korrensalo, A., Mammarella, I., Vesala, T., and Tuittila, E. S.: Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed-species boreal mire complex, Geophys. Res. Lett., 44, 5836–5843, https://doi.org/10.1002/2017GL073884, 2017. 
Bastrikov, V., MacBean, N., Bacour, C., Santaren, D., Kuppel, S., and Peylin, P.: Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2), Geosci. Model Dev., 11, 4739–4754, https://doi.org/10.5194/gmd-11-4739-2018, 2018. 
Bonan, G. B.: Climate Change and Terrestrial Ecosystem Modeling, Cambridge University Press, https://doi.org/10.1017/9781107339217, 2019. 
Botta, A., Viovy, N., Ciais, P., and Friedlingstein, P.: A global prognostic scheme of leaf onset using satellite data, Glob. Change Biol., 6, 709–726, 2000. 
Download
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Share