Detection of potential structural deficiencies in a global aerosol model using a perturbed parameter ensemble

Abstract. Understanding and reducing uncertainty in model-based estimates of aerosol radiative forcing is crucial for improving climate projections. A key challenge is that differences between model output and observations can stem from uncertainties in input parameters (parametric uncertainty) or from deficiencies in model code and configuration (structural uncertainty), and these two causes are difficult to distinguish. Structural deficiencies limit efforts to reduce parametric uncertainty through observational constraint because they prevent models from being simultaneously consistent with multiple observations. However, no framework exists to detect structural deficiencies and assess their impact on parametric uncertainty. We propose a workflow to identify structural inconsistencies between observational constraints and diagnose potential structural deficiencies. Using a perturbed parameter ensemble, we sample uncertainty in aerosols, clouds, and radiation in the UK Earth System Model (UKESM), and evaluate model bias against in-situ observations of sulfate aerosol, sulfur dioxide, aerosol optical depth, and particle number concentration across Europe. Applying observational constraints reveals inconsistencies that no combination of the perturbed parameters can resolve. For example, sulfate concentrations in different regions cannot be matched simultaneously, and enforcing a compromise between regions reduces skill across most variables. Additional examples include an inter-region inconsistency in SO2 and an inter-variable inconsistency between aerosol optical depth and sulfate. By examining the parameter sets retained by constraints, we trace inconsistencies to the parameterisations that may cause them and propose targeted changes to address the underlying deficiency. This approach offers a pathway for evidence-based model development that supports more robust uncertainty reduction and improves climate projection skill.