Surface alignment to improve observational constraint and reduce predicted aerosol radiative forcing uncertainty

The effects of aerosols (small particles suspended in the air) on the Earth’s energy balance since pre-industrial times (aerosol radiative forcing) has significantly and repeatedly dominated the uncertainty in reported estimates of global temperature change from the Intergovernmental Panel on Climate Change (IPCC). Climate models are used to simulate the global distribution of aerosols and predict the aerosol radiative forcing. However, these models are extremely computationally expensive to run and such predictions are very uncertain since the values of the models' many inputs (parameters) are unknown. Expert elicited model parameter ranges form a multi-dimensional parameter uncertainty space of the climate model to explore. It is not feasible to densely sample this space directly, but by using Perturbed Parameter Ensembles (PPEs) and statistical methodologies (emulation, uncertainty quantification, and history matching) we can rigorously explore the effects of parametric uncertainty and then look to constrain it to a set of plausible models (parameter combinations) using real-world observations.

Constraining the uncertainty in aerosol forcing is a substantial challenge as the forcing itself cannot be observed directly. Hence, we must constrain model uncertainty using other observable quantities, feeding these constraints through to forcing predictions. Previous studies have shown limited success in this endeavour due to the differences in parameter sensitivity between observable variables and forcing, and the effects of ‘equifinality’ (compensating errors). Even if parameter sensitivities are shared, this does not automatically mean that constraint will feed through from the observable to the forcing, as the connections between the inputs and these outputs (observable / forcing) may align differently in the corresponding multi-dimensional response surfaces over the parameter space.

In this work, we propose an 'alignment measure' as an approach to determine the potential of a constraint on an observable quantity to provide constraint on the forcing. This measure involves analysing response surface alignment over parameter uncertainty space for a pair of variables through comparison of the surface partial derivatives. We will introduce the measure and show the application of it for the constraint of aerosol forcing in a large PPE from the UK Earth System Model. By understanding surface alignment in the model, this measure can lead to strategic observational constraint and improved uncertainty reduction of this complex climate response.