Integrating Ozone–vegetation Damage Schemes into SSiB4/TRIFFID: Evaluation of Six Parameterizations and Refinement of Ozone Decay Process Across Plant Functional Types

Tropospheric ozone (O3) is a major air pollutant in China that threatens vegetation productivity and ecosystem functions. Quantifying O3-induced impacts on photosynthesis and stomatal conductance is crucial for understanding changes in carbon, water, and energy fluxes between the biosphere and atmosphere on regional and global scales. In recent decades, several parameterization schemes have been developed to describe the photosynthetic and stomatal responses to O3 exposure. However, a significant spread remains when applying different schemes in various model frameworks. In this study, we integrated six flux-based O3-vegetation damage parameterizations into SSiB4/TRIFFID, a well-established land surface model coupled with a dynamic vegetation model, to assess the impacts of O3 pollution on terrestrial ecosystems in China during the 2010s. Our results show that O3 pollution led to approximately a 20% reduction in GPP during the 2010s, with discrepancies ranging from 15% to 31% across different schemes. Comparison of the O3 damage schemes revealed substantial differences in vegetation O3 sensitivity across schemes and plant functional types (PFTs). When compared with observations, the newly developed schemes, such as L2024 and LMA-based approaches, showed more reliable O3 sensitivity, as evidenced by smaller biases relative to peer-reviewed observations. This improved performance can be attributed to the inclusion of a broader range of observational and experimental data, as well as key physiological parameters (e.g., LMA) to better capture O3 sensitivity. Furthermore, we found that the L2024 scheme exhibited strong inhibition of photosynthesis in the late growing season due to cumulative O3 exposure. By refining the "decay" process of O3 accumulation using leaf lifespan parameters and applying the "decay" and "heal" processes across all PFTs, we improved the spatial and temporal distribution of Gross Primary Productivity (GPP) simulations. This study highlights the importance of observational evidence and physiological insights in developing O3-vegetation damage parameterizations. Future efforts should focus on expanding observational and experimental data on O3 responses in China’s natural ecosystems to enhance O3 damage assessment and model development.