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The role of stomata in regulating photosynthesis and transpiration, and hence governing global biogeochemical cycles and climate, is well-known. Less well-understood, however, is the importance of stomatal control to the exchange of other trace gases between terrestrial vegetation and the atmosphere. Yet these gases determine atmospheric composition, and hence air quality and climate, on scales ranging from local to global, and seconds to decades. Vegetation is a major sink for ground-level ozone via the process of dry deposition and the primary source of many biogenic volatile organic compounds (BVOCs). The rate of dry deposition is largely controlled by the rate of diffusion of a gas through the stomata, and this also governs the emission rate of some key BVOCs. It is critical therefore that canopy-atmosphere exchange models capture the physiological processes controlling stomatal conductance and the transfer of trace gases other than carbon dioxide and water vapour. We incorporate three of the most widely used coupled stomatal conductance-photosynthesis models into the one-dimensional multi-layer FORest Canopy-Atmosphere Transfer (FORCAsT1.0) model to assess the importance of choice of parameterisation on simulated ozone deposition rates. Modelled GPP and stomatal conductance across a broad range of ecosystems differ by up to a factor of two between the best and worst performing model configurations. This leads to divergences in seasonal and diel profiles of ozone deposition velocity of up to 30% and deposition rate of up to 13%, demonstrating that the choice of stomatal conductance parameterisation is critical in accurate quantification of ozone deposition.

Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14_psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14_p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased

Tropospheric ozone is a dangerous atmospheric pollutant for forest ecosystems when it penetrates stomata. Thresholds for ozone-risk assessment are based on accumulated stomatal ozone fluxes such as the Phytotoxic Ozone Dose (POD). In order to identify the effect of ozone on a Holm oak forest in central Italy, four flux-based ozone impact response functions were implemented and tested in a multi-layer canopy model AIRTREE and evaluated against Gross Primary Productivity (GPP) obtained from observations of Eddy Covariance fluxes of CO2. To evaluate if a clear phytotoxic threshold exists and if it changes during the year, six different detoxifying thresholds ranging between 0 and 5 nmol O3 m-2 s-1 were tested. The use of species-specific rather than more general response functions based on plant functional types (PFT) increased model accuracy (RMSE reduced by up to 8.5%). In the case of linear response functions, a threshold of 1 nmol m-2 s-2 produced the best results for simulations of the whole year, although the tolerance to ozone changed seasonally, with higher tolerance (5 nmol m-2 s-1 or no ozone impact) for Winter and Spring and lower thresholds in Summer and Fall (0-1 nmol m-2 s-1). A “dynamic threshold” obtained by extracting the best daily threshold values from a range of different simulations helped reduce model overestimation of GPP by 213 g C m-2 y-1 and reduce RMSE up to 7.7%. Finally, a nonlinear ozone correction based on manipulative experiments produced the best results when no detoxifying threshold was applied (0 nmol O3 m-2 s-1), suggesting that nonlinear functions fully account for ozone detoxification. The evidence of seasonal changes in ozone tolerance points to the need for seasonal thresholds to predict ozone damage and highlights the importance of performing more species-specific manipulative experiments to derive response functions for a broad range of plant species.

Tropospheric ozone is a dangerous atmospheric pollutant for forest ecosystems when it penetrates stomata. Thresholds for ozone-risk assessment are based on accumulated stomatal ozone fluxes such as the Phytotoxic Ozone Dose (POD). In order to identify the effect of ozone on a Holm oak forest in central Italy, four flux-based ozone impact response functions were implemented and tested in a multi-layer canopy model AIRTREE and evaluated against Gross Primary Productivity (GPP) obtained from observations of Eddy Covariance fluxes of CO2. To evaluate if a clear phytotoxic threshold exists and if it changes during the year, six different detoxifying thresholds ranging between 0 and 5 nmol O3 m-2 s-1 were tested. The use of species-specific rather than more general response functions based on plant functional types (PFT) increased model accuracy (RMSE reduced by up to 8.5%). In the case of linear response functions, a threshold of 1 nmol m-2 s-2 produced the best results for simulations of the whole year, although the tolerance to ozone changed seasonally, with higher tolerance (5 nmol m-2 s-1 or no ozone impact) for Winter and Spring and lower thresholds in Summer and Fall (0-1 nmol m-2 s-1). A “dynamic threshold” obtained by extracting the best daily threshold values from a range of different simulations helped reduce model overestimation of GPP by 213 g C m-2 y-1 and reduce RMSE up to 7.7%. Finally, a nonlinear ozone correction based on manipulative experiments produced the best results when no detoxifying threshold was applied (0 nmol O3 m-2 s-1), suggesting that nonlinear functions fully account for ozone detoxification. The evidence of seasonal changes in ozone tolerance points to the need for seasonal thresholds to predict ozone damage and highlights the importance of performing more species-specific manipulative experiments to derive response functions for a broad range of plant species.

The role of stomata in regulating photosynthesis and transpiration, and hence governing global biogeochemical cycles and climate, is well-known. Less well-understood, however, is the importance of stomatal control to the exchange of other trace gases between terrestrial vegetation and the atmosphere. Yet these gases determine atmospheric composition, and hence air quality and climate, on scales ranging from local to global, and seconds to decades. Vegetation is a major sink for ground-level ozone via the process of dry deposition and the primary source of many biogenic volatile organic compounds (BVOCs). The rate of dry deposition is largely controlled by the rate of diffusion of a gas through the stomata, and this also governs the emission rate of some key BVOCs. It is critical therefore that canopy-atmosphere exchange models capture the physiological processes controlling stomatal conductance and the transfer of trace gases other than carbon dioxide and water vapour. We incorporate three of the most widely used coupled stomatal conductance-photosynthesis models into the one-dimensional multi-layer FORest Canopy-Atmosphere Transfer (FORCAsT1.0) model to assess the importance of choice of parameterisation on simulated ozone deposition rates. Modelled GPP and stomatal conductance across a broad range of ecosystems differ by up to a factor of two between the best and worst performing model configurations. This leads to divergences in seasonal and diel profiles of ozone deposition velocity of up to 30% and deposition rate of up to 13%, demonstrating that the choice of stomatal conductance parameterisation is critical in accurate quantification of ozone deposition.

Isoprene concentrations were measured at four heights below, within, and above the forest canopy in Wytham Woods (United Kingdom) throughout the summer of 2018 using custom‐built gas chromatographs (the iDirac). These observations were complemented with selected ancillary variables, including air temperature, photosynthetically active radiation, occasional leaf gas exchange measurements, and satellite retrievals of normalized difference vegetation and water indices. The campaign overlapped with a long and uninterrupted heatwave accompanied by moderate drought. Peak isoprene concentrations during the heatwave‐drought were up to a factor of 4 higher than those before or after. Higher temperatures during the heatwave could not account for all the observed isoprene; the enhanced abundances correlated with drought stress. Leaf‐level emissions confirmed this and also included compounds associated with ecosystem stress. This work highlights that a more in‐depth understanding of the effects of drought stress is required to better characterize isoprene emissions.