Canopy emissions of volatile hydrocarbons such as isoprene and monoterpenes play an important role in air chemistry. They depend on various environmental conditions, are highly species-specific and are expected to be affected by global change. In order to estimate future emissions of these isoprenoids, differently complex models are available. However, seasonal dynamics driven by phenology, enzymatic activity, or drought stress strongly modify annual ecosystem emissions. Although these impacts depend themselves on environmental conditions, they have yet received little attention in mechanistic modelling. In this paper we propose the application of a mechanistic method for considering the seasonal dynamics of emission potential using the “Seasonal Isoprenoid synthase Model” (Lehning et al., 2001). We test this approach with three different models (GUENTHER, Guenther et al., 1993; NIINEMETS, Niinemets et al., 2002a; BIM2, Grote et al., 2006) that are developed for simulating light-dependent monoterpene emission. We also suggest specific drought stress representations for each model. Additionally, the proposed model developments are co ... mehrmpared with the approach realized in the MEGAN (Guenther et al., 2006) emission model. Models are applied to a Mediterranean Holm oak (Quercus ilex) site with measured weather data. The simulation results demonstrate that the consideration of a dynamic emission potential has a strong effect on annual monoterpene emission estimates. The investigated models, however, show different sensitivities to the procedure for determining this seasonality impact. Considering a drought impact reduced the differences between the applied models and decreased emissions at the investigation site by approxi- mately 33% on average over a 10 year period. Although this overall reduction was similar in all models, the sensitivity to weather conditions in specific years was different. We conclude that the proposed implementations of drought stress and internal seasonality strongly reduce estimated emissions and indicate the measurements that are needed to further evaluate the models.