Evaluation of Four Cloud Microphysical Schemes Simulating Arctic Low-Level Clouds Observed During the ACLOUD Experiment

Abstract

We investigated the microphysical characteristics of low-level Arctic clouds using four cloud microphysics parameterization schemes (Morrison, WDM6, NSSL, and P3) implemented in the Polar-optimized Weather Research and Forecasting (PWRF) model. Our assessment was based on a comparison with data collected during the Arctic Cloud Observations Using Airborne Measurements during the Polar Day (ACLOUD) experiment, which occurred near Svalbard between May and June 2017. During the ACLOUD campaign, a substantial number of clouds were observed, primarily influenced by adiabatic motions and sensible/latent heat fluxes that led to air masses warming up by 4 degrees C as they traversed over the sea ice and ocean transition zone. Among the parameterization schemes tested, the Morrison and WDM6 schemes demonstrated superior performance overall, showing frequency bias (FB) values closer to 1 (1.07 and 1.13) and high log-odds ratios (0.50 and 0.48) in cloud occurrence predictions, indicating good agreement with observed data. In contrast, the NSSL and P3 schemes exhibited higher FB values (1.30 and 1.56) with lower log-odds ratios (0.17 and 0.16), indicating an overestimation of cloud occurrence. The WDM6 scheme produced higher ice-mixing ratios compared to Morrison and NSSL schemes, while the latter two tended to generate more snow and graupel. The NSSL scheme showed the least bias in simulating ice water content (IWC) in mixed-phase clouds; however, all schemes generally underestimated both liquid water content (LWC) and IWC. Notably, significant deviations in IWC were observed at an altitude of 1.2 km compared to observations, attributed to differences in temperature thresholds for ice formation. This study emphasizes the importance of developing cloud parameterization in the Arctic based on observations to improve the accuracy of estimating cloud impacts on Arctic climate under rapid Arctic warming trends.