Forecasts of biogenic trace gases in the planetary boundary layer (PBL) are highly affected by simulated emission and transport processes. The Po region during the PEGASOS campaign in summer 2012 provides challenging, yet common, conditions for simulating biogenic gases in the PBL. This study identifies and quantifies principal sources of forecast uncertainties induced by various model configurations under these conditions. Specifically, the effects of model configuration on different processes affecting atmospheric distributions of biogenic trace gas distributions are analyzed based on a priori available information. The investigation is based on the EURopean Air pollution Dispersion - Inverse Model (EURAD-IM) chemistry transport model employing the Model for Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN 2.1) biogenic emission module and Regional Atmospheric Chemistry Mechanism - Mainz Isoprene Mechanism (RACM-MIM) as the gas phase chemistry mechanism. Two major sources of forecast uncertainties are identified in this study. Firstly, biogenic emissions appear to be exceptionally sensitive to land surface properties inducing total variations in local concentrations of up to 1 order of magnitude. Moreover, these sensitivities are found to be highly similar for different gases and almost constant during the campaign, varying only diurnally. Secondly, the model configuration also highly influences regional flow patterns with significant effects on pollutant transport and mixing. This effect was corroborated by diverging source regions of a representative air mass and thus applies also to non-biogenic gases. As a result, large sensitivities to model configuration are found for surface concentrations of isoprene, as well as OH, affecting reactive atmospheric chemistry. Especially in areas with small-scale emission patterns, changes in the model configuration are able to induce significantly different local concentrations. The amount and complexity of sensitivities found in this study demonstrate the need to consider forecast uncertainties in chemical transport models with a special focus on biogenic emissions and pollutant transport.