Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6337-2024
https://doi.org/10.5194/gmd-17-6337-2024
Model description paper
 | 
30 Aug 2024
Model description paper |  | 30 Aug 2024

Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)

Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen

Related authors

Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework
Huajie Zhu, Xiuli Xing, Mousong Wu, Weimin Ju, and Fei Jiang
Biogeosciences, 21, 3735–3760, https://doi.org/10.5194/bg-21-3735-2024,https://doi.org/10.5194/bg-21-3735-2024, 2024
Short summary

Related subject area

Biogeosciences
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Benjamin F. Meyer, João P. Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
Geosci. Model Dev., 18, 4643–4666, https://doi.org/10.5194/gmd-18-4643-2025,https://doi.org/10.5194/gmd-18-4643-2025, 2025
Short summary
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025,https://doi.org/10.5194/gmd-18-4317-2025, 2025
Short summary
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025,https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025,https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025,https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary

Cited articles

An, X. Q., Zhai, S. X., Jin, M., Gong, S., and Wang, Y.: Development of an adjoint model of GRAPES–CUACE and its application in tracking influential haze source areas in north China, Geosci. Model Dev., 9, 2153–2165, https://doi.org/10.5194/gmd-9-2153-2016, 2016. 
Andreas, I. and Kim, P.: FLUXNET2015 DK-Sor Soroe, FLUXNET2015 [data set], https://doi.org/10.18140/FLX/1440155, 2020. 
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., Naik, V., Palmer, M., Plattner, G.-K., and Rogelj, J.: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Technical Summary, https://www.ipcc.ch/report/ar6/wg1/ (last access: April 2023), 2021. 
Arya, P. S.: Introduction to micrometeorology, Elsevier, ISBN 0080489265, 2001. 
Download
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Share