Modeling volatile isoprenoid emissions – a story with split ends

Abstract Accurate prediction of plant‐generated volatile isoprenoid fluxes is necessary for reliable estimation of atmospheric ozone and aerosol formation potentials. In recent years, significant progress has been made in understanding the environmental and physiological controls on isoprenoid emission and in scaling these emissions to canopy and landscape levels. We summarize recent developments and compare different approaches for simulating volatile isoprenoid emission and scaling up to whole forest canopies with complex architecture. We show that the current developments in modeling volatile isoprenoid emissions are “split‐ended” with simultaneous but separated efforts in fine‐tuning the empirical emission algorithms and in constructing process‐based models. In modeling volatile isoprenoid emissions, simplified leaf‐level emission algorithms (Guenther algorithms) are highly successful, particularly after scaling these models up to whole regions, where the influences of different ecosystem types, ontogenetic stages, and variations in environmental conditions on emission rates and dynamics partly cancel out. However, recent experimental evidence indicates important environmental effects yet unconsidered and emphasize, the importance of a highly dynamic plant acclimation in space and time. This suggests that current parameterizations are unlikely to hold in a globally changing and dynamic environment. Therefore, long‐term predictions using empirical algorithms are not necessarily reliable. We show that process‐based models have large potential to capture the influence of changing environmental conditions, in particular if the leaf models are linked with physiologically based whole‐plant models. This combination is also promising in considering the possible feedback impacts of emissions on plant physiological status such as mitigation of thermal and oxidative stresses by volatile isoprenoids. It might be further worth while to incorporate main features of these approaches in regional empirically‐based emission estimations thereby merging the “split ends”.