Opposite cloud responses to extreme Arctic pollution: sensitivity to cloud microphysics, or a bug?

Aerosol-cloud interactions remain one of the largest uncertainties in global climate modelling. This uncertainty arises because of the dependence of aerosol-cloud interactions on many tightly coupled atmospheric processes; the non-linear response of clouds to aerosol perturbations across different regimes; and the challenge of extracting robust signals from noisy meteorological observations. The problem is particularly acute in the Arctic, where sparse observational coverage limits model constraints, pristine conditions can lead to unexpected behaviour, and key processes remain poorly understood.

A common way to tackle the challenge of uncertainties arising from aerosol-cloud interactions in climate simulations is to conduct sensitivity experiments using cloud and aerosol microphysics schemes based on different assumptions and parameterisations. By comparing these experiments, key results can be constrained by sampling the range of unavoidable structural uncertainties in the models. Here, we apply this approach to a case study of an extreme, polluted warm air mass in the Arctic that was measured during the MOSAiC Arctic expedition in 2020. We simulated the event in the WRF-Chem-Polar regional climate model both with and without the anthropogenic aerosols from the strong pollution event to study the response of clouds and surface radiative balance. To understand the sensitivity of our results to the choice of model configuration, we tested two distinct, widely-used cloud microphysics schemes.

Initial results showed that the two schemes simulated opposite cloud responses: one predicted a surface cooling from the pollution that was reasonably in line with our expectations of the event, while the other predicted the opposite behaviour in the cloud response and an associated surface warming. These opposing effects seemed to suggest that structural uncertainties in the two schemes relating to clean, Arctic conditions was so strong that it even obscured our ability to understand the overall sign of the surface radiative response to the pollution.

However, since significant model development was required to couple these two cloud microphysics schemes to the aerosol fields in our model, there was another explanation that we couldn’t rule out: a bug in the scheme that was producing the more unexpected results. In this talk, we will explore the challenges of simulating the Arctic climate with a state-of-the-art chemistry-climate model and highlight how examples like this underscore the value of our recent efforts to align our collaborative model development with software engineering principles and Open Science best practices.