Preprints
https://doi.org/10.5194/egusphere-2023-1955
https://doi.org/10.5194/egusphere-2023-1955
27 Sep 2023
 | 27 Sep 2023

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

Abstract. Modeling and predicting changes in the function and structure of the terrestrial biosphere and its feedbacks to climate change strongly depends on our ability to accurately represent interactions of the carbon and water cycles, and energy exchange. However, carbon fluxes, hydrological status and energy exchange simulated by process-based terrestrial ecosystem models are subject to significant uncertainties, largely due to the poorly calibrated parameters related to various processes. In this work, an adjoint-based data assimilation system (Nanjing University Carbon Assimilation System, NUCAS) was developed, which is capable of assimilating multiple observations to optimize process parameters of a satellite data driven ecosystem model–BEPS (Boreal Ecosystem Productivity Simulator). Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS on seven sites by assimilating the carbonyl sulfide (COS) fluxes, which were tightly related to the stomatal conductance and photosynthesis. Results showed that NUCAS is able to achieve a consistent fit to COS observations across various ecosystems. Comparing prior simulations with validation datasets, we found that the assimilation of COS can significantly improve the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture. We also showed that the NUCAS is capable of constraining parameters from multiple sites simultaneously and achieving a good consistency to the single-site assimilation. Our results demonstrate that COS can provide strong constraints on parameters relevant to water, energy and carbon processes with the data assimilation system, and open new perspectives for better understanding of the ecosystem carbon, water and energy exchanges.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2023-1955', Juan Antonio Añel, 12 Oct 2023
    • CC1: 'Reply on CEC1', Mousong Wu, 12 Oct 2023
    • AC3: 'Reply on CEC1', Huajie Zhu, 15 Feb 2024
  • RC1: 'Comment on egusphere-2023-1955', Anonymous Referee #1, 13 Nov 2023
    • AC1: 'Reply on RC1', Huajie Zhu, 15 Feb 2024
  • RC2: 'Comment on egusphere-2023-1955', Anonymous Referee #2, 05 Dec 2023
    • AC2: 'Reply on RC2', Huajie Zhu, 15 Feb 2024
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen

Model code and software

Nanjing University Carbon Assimilation System (NUCAS v1.0) Mousong Wu https://zenodo.org/record/8288751

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

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Short summary
In this work, Nanjing University Carbon Assimilation System (NUCAS) was developed. Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS. The assimilation of COS significantly improved the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture, demonstrating that COS can provide strong constraints on parameters relevant to water, energy and carbon processes.