Understanding uncertainties in Arctic aerosol representation in climate models

Abstract. Arctic amplification and its persistent underestimation in climate models underscore the importance of accurate representation of local Arctic feedback processes. Previous studies evaluating model data against measurements showed the importance of including local emissions, such as iodic acid and organic vapours, for an accurate representation of aerosols in the high Arctic. The MOSAiC expedition has produced a full year of data in the high Arctic, providing an opportunity to evaluate the performance of climate models in this region across strongly contrasting seasonal conditions. We evaluate four CMIP6 models and the chemistry-transport model TM5 using this data. CMIP6 models fail to capture the observed seasonal cycle and generally underestimate aerosol number concentration (CN), with the strongest underestimation in summer. To understand the cause of these model deficiencies, we conduct a sensitivity analysis using an ensemble of TM5 experiments by perturbing individual parameters and three reasons were identified. In summer, missing regional new particle formation (NPF) sources are the primary cause of the underestimation. Including methanesulphonic acid driven NPF improved the magnitude and seasonality of simulated CN. In winter and early spring, the model is missing aerosol sources such as blowing snow and lead emissions. During the Arctic haze period, the model underestimates the aerosol background concentration, possibly due to an underestimation of long-range transported aerosols. With cloud condensation nuclei (CCN), we observe a persistent underestimation even during periods of CN overestimation. These results identify gaps in Arctic aerosol representation in climate models that need to be addressed to improve climate projections.