Assessing the effects of surface ozone on forest GPP: a vegetation model approach using JULES

Tropospheric Ozone (O3) is a secondary pollutant known for its positive radiative forcing and detrimental effects on air quality, human health, and ecosystems. In plants, O3 acts as a strong oxidant, affecting cellular and molecular processes, e.g. modifying rubisco activity, reducing stomatal conductance, and inducing early leaf senescence. This study aims to evaluate the effects of O3 on Gross Primary Production (GPP) at six forest sites: five European sites (Belgium, France, Finland and Italy) and one tropical site in the Congo Basin. We employed a modelling approach, contrasting simulations of GPP with and without the influence of O3 using the Joint UK Land Environment Simulator (JULES), a land surface model used to study soil-vegetation-atmosphere interactions. The JULES model was calibrated for each site, adjusting key parameters, using historical climate data and soil properties to align with each location's specific environmental and vegetation characteristics. Therefore, we forced the model using measurements of local tropospheric O3, CO2, and meteorological variables. We conducted two simulations for each site: one representing the existing O3 levels observed at each site and another under O3-free conditions. This comparative approach enabled us to isolate the specific effects of O3 on GPP to quantify this effect. Our findings reveal a difference in the sensitivity of the contrasting forest ecosystems to O3 exposure. The correlation values between modelled GPP with O3 and observed GPP vary between 0.786 in Castelporziano, Italy and 0.933 in Hyytiälä, Finland. Consequently, the European sites, encompassing a range of climatic and ecological conditions, displayed diverse responses to O3, and the GPP reduction varies along the different sites. The GPP reduction due to O3 exposure varied across sites, ranging from -1.52% in Hyytiälä, Finland, to -9.79% in Castelporziano, Italy. This study shows the necessity of long-term monitoring datasets combined with process-based models to understand better the O3 impacts at several ecosystems.