In Europe, more than one quarter of the adult population and one third of the children suffer from pollinosis, but the geographical variability is large. In Belgium, at least ~ 10% of the people develop allergies due to birch pollen. These patients may benefit from a forecasting system that raises alerts when episodes with huge amount of airborne birch pollen grains are expected. Such a forecast system for birch pollen was established for the Belgian territory in 2023 based on the pollen emission and transport model System for Integrated modeLling of Atmospheric coMposition (SILAM). The question, however, is which uncertainty in modelling and forecasting airborne pollen levels can be expected? Here, we assess the uncertainty in modelling airborne birch pollen levels near the surface using SILAM in a Monte Carlo error approach summarized by the relative Coefficient of Variation (CV%) as descriptive statistic for the season of 2018 in Belgium. For the major inputs that drive the birch pollen model—the amount and location of birch trees (0.1° × 0.1° map), the start and end of the birch pollen season (1° × 1° map), and the ripening temperature of birch catkins—sets of 100 randomly sampled data layers were prepared for running SILAM 100 times. For each set of model input, 100 spatio-temporal maps of airborne birch pollen levels were produced and its spread was quantified by the CV%. We show that the spatial uncertainty of pollen emissions sources in SILAM is substantially high, but that the uncertainties of the parameters determining the start and end of the season are at least equally important. By accumulating the effects of all investigated model input uncertainties including the impact of the catkins-ripening temperature, CV% values of 50% and more are obtained when quantifying the variation of the modelled airborne birch pollen levels. These errors are in line with reported values from the current reference method for monitoring airborne birch pollen grains near the surface.