Integrating ozone–vegetation damage schemes into SSiB4/TRIFFID: evaluation of six parameterizations and refinement of ozone decay process across plant functional types

Tropospheric ozone (O 3 ) is a major air pollutant that threatens vegetation productivity and terrestrial ecosystems. Quantifying O 3 -induced impacts on photosynthesis and stomatal conductance is crucial for understanding biosphere–atmosphere interactions at regional and global scales. In recent decades, several parameterization schemes have been developed to describe the photosynthetic and stomatal responses to O 3 exposure. However, substantial discrepancies remain when applying different schemes in various model frameworks. In this study, we integrated six flux-based O 3 –vegetation damage parameterizations into SSiB4/TRIFFID, a well-established dynamic global vegetation model, to assess the impacts of O 3 pollution on vegetation photosynthesis in China during the 2010s. Our results indicate that O 3 pollution led to approximately a 21 % reduction in GPP during the 2010s, with discrepancies ranging from 17 %–27 % across different schemes. Comparison of the O 3 damage schemes revealed substantial differences in plant O 3 sensitivity across schemes and plant functional types (PFTs). When evaluated against observations, the newly developed Li2024 parameterization – which features non-linear response formulations – and the trait-informed approaches based on leaf mass per area (LMA) both reproduce observed O 3 sensitivity more closely, as reflected in their consistently smaller biases. 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 O 3 sensitivity. Furthermore, we found that the implementation of Li2024 scheme in SSiB4/TRIFFID exhibited strong inhibition of photosynthesis in the late growing season due to cumulative O 3 exposure. By refining the “decay” process of O 3 accumulation using leaf lifespan parameters and applying the “decay” and “healing” processes across all PFTs, we improved the spatial and temporal distribution of gross primary productivity (GPP) simulations. This study highlights the importance of observations and physiological insights in developing O 3 –vegetation damage parameterizations. Future efforts should focus on expanding observational and experimental data on O 3 responses in China's natural ecosystems to enhance O 3 damage assessment and model development.