Bow echo structures, a subset of mesoscale convective systems (MCSs), are often poorly forecast within deterministic numerical weather prediction model simulations. Among other things, this may be due to the inherent low predictability associated with bow echoes, deficient initial conditions (ICs), and inadequate parameterization schemes. Four different ensemble configurations assessed the sensitivity of the MCSs’ simulated reflectivity and radius of curvature to the following: perturbations in initial and lateral boundary conditions using a global dataset, different microphysical schemes, a stochastic kinetic energy backscatter (SKEB) scheme, and a mix of the previous two. One case is poorly simulated no matter which IC dataset or microphysical parameterization is used. In the other case, almost all simulations reproduce a bow echo. When the IC dataset and microphysical parameterization is fixed within a SKEB ensemble, ensemble uncertainty is smaller. However, while differences in the location and timing of the MCS are reduced, variations in convective mode remain substantial. Results suggest the MCS’s positioning is influenced primarily by ICs, but its mode is most sensitive to the model error uncertainty. Hence, correct estimation of model error uncertainty on the storm scale is crucial for adequate spread and the probabilistic forecast of convective events.