Targeted model developments to improve consistency of observational constraints and reduce aerosol forcing uncertainty

Uncertainty in aerosol radiative forcing remains high, which limits confidence in climate projections. Climate models depend on uncertain input parameters, and observations are used to constrain this uncertainty by ruling out parameter values that are unlikely. However, it is often not possible to tune a model to agree with multiple observations at the same time. Even when parameter uncertainty is sampled widely, some observational constraints are mutually inconsistent; i.e., they require opposing parameter values and therefore cannot be used together. These inconsistencies in observational constraints indicate structural issues in the way that aerosol processes are modelled, and limit how far parameter uncertainty ranges (and thus forcing estimates) can be reduced.

In this presentation, we describe how targeted model developments can help address inconsistencies in observational constraints and thereby help to reduce forcing uncertainty. In previous work, we introduced a workflow to detect potential structural inconsistencies by using a perturbed parameter ensemble (PPE) of the UK Earth System Model. The workflow revealed inter‑region and inter‑variable inconsistencies. For example, sulfate aerosol concentrations in different regions could not be consistently constrained, and constraint of aerosol optical depth degraded model performance for sulfate concentrations.

Here, we extend that approach using a new PPE that includes targeted structural changes designed to address the identified deficiencies. We reapply the same observational constraints and test whether the consistency of constraints improves, and whether there are any remaining structural deficiencies. We also assess the connection to aerosol radiative forcing: whether constraints that previously led to opposing aerosol radiative forcing values now align, and whether that alignment leads to more consistent forcing (and less uncertain) values across observational constraints.

This work demonstrates a practical path to directly target two interlinked causes of model uncertainty (parametric and structural): use model-observation inconsistencies to diagnose potential structural errors, implement targeted model developments, and iterate. The outcome is an evidence‑based development cycle that aims to make more observations usable simultaneously, reduce parametric and structural uncertainty, and ultimately contribute to reducing uncertainty in climate projections.