© Author(s) 2014. CC Attribution 3.0 License. Biogenic volatile organic compounds (BVOCs) are essential in atmospheric chemistry because of their chemical reactions that produce and destroy tropospheric ozone, their effects on aerosol formation and growth, and their potential influence on global warming. As one of the important BVOC groups, monoterpenes have been a focus of scientific attention in atmospheric research. Detailed regional measurements and model estimates are needed to study emission potential and the monoterpene budget on a global scale. Since the use of empirical measurements for upscaling is limited by many physical and biological factors, such as genetic variation, temperature and light, water availability, seasonal changes, and environmental stresses, comprehensive inventories over larger areas are difficult to obtain. We applied the boundary-layer-chemistry-transport model SOSA (model to Simulate the concentrations of Organic vapours and Sulphuric Acid) to investigate Scots pine (Pinus sylvestris) monoterpene emissions in a boreal coniferous forest at the SMEAR (Station for Measuring forest Ecosystem-Atmosphere Relations) II site, southern Finland. SOSA was applied to simulate monoterpene emissions with three different emission modules: the semiempirical G95, MEGAN (Model of Emissions of Gases and Aerosols from Nature) 2.04 with improved descriptions of temperature and light responses and including also carbonyl emissions, and a process-based model SIM-BIM (Seasonal Isoprenoid synthase Model - Biochemical Isoprenoid biosynthesis Model). For the first time, the emission models included seasonal and diurnal variations in both quantity and chemical species of emitted monoterpenes, based on parameterizations obtained from field measurements. Results indicate that modelling and observations agreed reasonably well and that the model can be used for investigating regional air chemistry questions related to monoterpenes. The predominant modelled monoterpene concentrations, α-pinene and Δ3-carene, are consistent with observations.