Preprints
https://doi.org/10.5194/egusphere-2025-4795
https://doi.org/10.5194/egusphere-2025-4795
07 Oct 2025
 | 07 Oct 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Detection of structural deficiencies in a global aerosol model to explain limits in parametric uncertainty reduction

Léa M. C. Prévost, Leighton A. Regayre, Jill S. Johnson, Doug McNeall, Sean Milton, and Kenneth S. Carslaw

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 region 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 them. This approach offers a pathway for evidence-based model development that supports more robust uncertainty reduction and improves climate projection skill.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

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Léa M. C. Prévost, Leighton A. Regayre, Jill S. Johnson, Doug McNeall, Sean Milton, and Kenneth S. Carslaw

Status: open (until 18 Nov 2025)

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Léa M. C. Prévost, Leighton A. Regayre, Jill S. Johnson, Doug McNeall, Sean Milton, and Kenneth S. Carslaw
Léa M. C. Prévost, Leighton A. Regayre, Jill S. Johnson, Doug McNeall, Sean Milton, and Kenneth S. Carslaw
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Short summary
Climate models rely on uncertain adjustable parameters. We tested millions of combinations of these inputs to see how well the model matches real-world data. We found that no single set of inputs can match several observations at the same time, which suggests that the issue lies in the model itself. We developed a method to detect these conflicts and trace them back trace them to their source. The aim is to help modellers target improvements that reduce uncertainty in climate projections.
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