Assimilation of carbonyl sulfide

• Published in: Geoscientific model development Vol. 17; no. 16; pp. 6337 - 12673
• Main Authors: Zhu, Huajie, Wu, Mousong, Jiang, Fei, Vossbeck, Michael, Kaminski, Thomas, Xing, Xiuli, Wang, Jun, Ju, Weimin, Chen, Jing M
• Format: Journal Article
• Language: English
• Published: Copernicus GmbH 30.08.2024
• Subjects: Analysis; Climatic changes; Energy trading; Sulfides
• ISSN: 1991-959X

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. In this work, an adjoint-based data assimilation system (Nanjing University Carbon Assimilation System; NUCAS v1.0) was developed, which is capable of assimilating multiple observations to optimize process parameters of a satellite-data-driven ecosystem model - the Biosphere-atmosphere Exchange Process Simulator (BEPS). Data assimilation experiments were conducted to investigate the robustness of NUCAS and to test the feasibility and applicability of assimilating carbonyl sulfide (COS) fluxes from seven sites to enhance our understanding of stomatal conductance and photosynthesis. Results showed that NUCAS is able to achieve a consistent fit to COS observations across various ecosystems, including evergreen needleleaf forest, deciduous broadleaf forest, C.sub.3 grass, and C.sub.3 crop. Comparing model simulations with validation datasets, we found that assimilating COS fluxes notably improves the model performance in gross primary productivity and evapotranspiration, with average root-mean-square error (RMSE) reductions of 23.54 % and 16.96 %, respectively. We also showed that NUCAS is capable of constraining parameters through assimilating observations from two sites simultaneously and achieving a good consistency with single-site assimilation. Our results demonstrate that COS can provide constraints on parameters relevant to water, energy, and carbon processes with the data assimilation system and opens new perspectives for better understanding of the ecosystem carbon, water, and energy exchanges.