Changes in aerosols since the preindustrial era have altered the top-of-the-atmosphere radiation balance by scattering and absorbing solar radiation (ARI) and indirectly interacting with clouds (ACI), known as aerosol effective radiative forcing (ERF_aer). ERF_aer persistently remains one of the most uncertain components in climate projections, due to imperfect representations of aerosol and cloud processes in climate models. Here, we construct a perturbed parameter ensemble (PPE) with the aerosol–climate model ICON2.6.4–A–HAM2.3 (hereafter ICON–HAM) to quantify key sources of ERF_aer uncertainty. We perturb 42 aerosol and cloud parameters over 383 PPE members. Parametric uncertainties in aerosol and cloud processes yield an ERF_aer of −1.04Wm^−2, with a 90 % credible range of −1.42 to −0.65 Wm^−2 for the period 2024–2025. The parameters related to emissions (anthropogenic sulfur dioxide, natural dimethyl sulfide, and emitted particle size) dominate ACI uncertainty and hence ERF_aer uncertainty (80 %), while absorption-related parameters (anthropogenic black carbon emissions and aerosol refractive indices) drive ARI uncertainty (60 %). Cloud parameters account for 13 % of ERF_aer uncertainty, mainly via convection and entrainment processes. The sensitivity analysis of model diagnostics to parameters reveals that many present-day aerosol and cloud observables share dominant causes of uncertainty with ACI and ARI forcing, highlighting the potential for constraining ERF_aer using existing space- and ground-based measurements. Notably, model biases against SPEXone and MODIS observations coincide spatially with parametric uncertainties, suggesting that much of these biases may be mitigated through appropriate constraint with observations, while the remainder requires structural model developments in combination with improved observations.