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Interactions between aerosols, clouds, and radiation remain a major source of uncertainty in effective radiative forcing (ERF), limiting the accuracy of climate projections. This study aims to quantify parametric uncertainties in aerosol-cloud and aerosol-radiation interactions using a perturbed parameter ensemble (PPE) of 221 simulations with the ECHAM6.3-HAM2.3 climate model, varying 23 aerosol-related parameters that control emissions, removal, chemistry, and microphysics.The resulting global mean aerosol ERF is -1.24 W m-2 (5-95 percentile: -1.56 to -0.89 W m-2). Uncertainty in ERF is dominated by sulfate-related processes, biomass burning, aerosol size, and natural emissions. For aerosol-cloud interactions, dimethyl sulfide (DMS) and biomass burning emissions are key drivers, whereas sulfate chemistry and dry deposition exert the strongest influence on aerosol-radiation interactions. Structural uncertainty is difficult to characterize, and this study focuses primarily on evaluating parametric uncertainty. The leading sources of ERF parametric uncertainty identified here are consistent with those found in other PPE studies, highlighting common sensitivities across climate models.Comparison with POLDER-3/PARASOL satellite retrievals reveals persistent model biases in aerosol optical depth (AOD), & Aring;ngstr & ouml;m exponent (AE), and single-scattering albedo (SSA), many of which fall within the parametric uncertainty range. Sulfate-related processes account for over 40 % of AOD uncertainty, while AE and SSA are most sensitive to DMS, sea salt, and black carbon parameters. Correlation analysis between key parameters and observables indicates that several biases may be reduced by tuning through physically consistent parameter adjustments for bias reduction. Our results highlight the need for combined efforts in parameter optimization and structural model development to improve confidence in aerosol-forcing estimates and future climate projections.

来源平台：ATMOSPHERIC CHEMISTRY AND PHYSICS