Arctic clouds play a key role in Arctic climate variability and change; however, contemporary climate models struggle to simulate cloud properties accurately. Model-simulated cloud properties are determined by the physical parameterizations and their interactions within the model configuration. Quantifying effects of individual parameterization changes on model-simulated clouds informs efforts to improve cloud properties in models and provides insights on climate system behavior. This study quantities the influence of individual parameterization schemes on Arctic low cloud properties within the Hadley Centre Global Environmental Model 3 atmospheric model using a suite of experiments where individual parameterization packages are changed one-at-a-time between two configurations: GA6 and GA7.1. The results indicate that individual parameterization changes explain most of the cloud property differences, whereas multiple parameterizations, including non-cloud schemes, contribute to cloud radiative effect differences. The influence of a parameterization change on cloud properties is found to vary by meteorological regime. We employ a three-term decomposition to quantify contributions from (1) regime independent, (2) regime dependent, and (3) the regime frequency of occurrence changes. Decomposition results indicate that each term contributes differently to each cloud property change and that non-cloud parameterization changes make a substantial contribution to the LW and SW cloud radiative effects by modifying clear-sky fluxes differently across regimes. The analysis provides insights on the role of non-cloud parameterizations for setting cloud radiative effects, a model pathway for cloud-atmosphere circulation interactions, and raises questions on the most useful observational approaches for improving models.

Uncertainty in Arctic top-of-atmosphere (TOA) radiative flux observations stems from the low sun angles and the heterogeneous scenes. Advancing our understanding of the Arctic climate system requires improved TOA radiative fluxes. We compare Cloud and Earth’s Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne measurements using two approaches: grid box averages and instantaneously-matched footprints. Both approaches indicate excellent agreement in the longwave and good agreement in the shortwave, within 2 uncertainty considering all error sources (CERES and airborne radiometer calibration, inversion, and sampling). While the SW differences are within 2 uncertainty, both approaches show a ~‑10 W m‑2 average CERES-aircraft flux difference. Investigating the source of this negative difference, we find a substantial sensitivity of the flux differences to the sea ice concentration dataset. Switching from imager-based to passive microwave-based sea ice data in the CERES inversion process reduces the differences in the grid box average fluxes and in the sea ice partly cloudy scene anisotropy in the matched footprints. In the long-term, more accurate sea ice concentration data are needed to reduce CERES TOA SW flux uncertainties. Switching from imager to passive microwave sea ice data, in the short-term, could improve CERES TOA SW fluxes in polar regions, additional testing is required. Our analysis indicates that calibration and sampling uncertainty limit the ability to place strong constraints (<±7%) on CERES TOA fluxes with aircraft measurements.

The observed and projected Arctic warming pattern is characterized by an early winter maximum and a summer minimum. While a robust feature of Arctic climate change, the seasonal expression of surface warming remains incompletely understood. Previous explanations attribute the seasonality to surface energy budget changes induced by climate feedbacks. However, these hypotheses cannot explain key features of the simulated seasonal structure: seasonal heating rate changes and the early winter warming maximum. We find that the increase in the thermal inertia of the Arctic system due to the transition from a lower thermal inertia surface (sea ice cover) to a higher thermal inertia surface (ice free ocean) captures these key seasonal features. Our analysis shows that the early winter Arctic warming maximum results from a slowing of the background surface cooling rate from summer to winter, not from an additional net energy input into the Arctic surface during that time.

Mixed-phase clouds are ubiquitous in the Arctic and play a critical role in Earth’s energy budget at the surface and top of the atmosphere. These clouds typically occupy the lower and midlevel troposphere and are composed of purely supercooled liquid droplets or mixtures of supercooled liquid water droplets and ice crystals. Here, we review progress in our understanding of the factors that control the formation and dissipation of Arctic mixed-phase clouds, including the thermodynamic structure of the lower troposphere, warm and moist air intrusions into the Arctic, large-scale subsidence and aerosol particles. We then provide a brief survey of numerous Arctic field campaigns that targeted local cloud-controlling factors and follow this with specific examples of how the Arctic Cloud Observations Using airborne measurements during polar Day (ACLOUD)/ Physical feedback of Arctic PBL, Sea ice, Cloud And AerosoL (PASCAL) and Airborne measurements of radiative and turbulent FLUXes of energy and momentum in the Arctic boundary layer (AFLUX) field campaigns that took place in the vicinity of Svalbard in 2019 were able to advance our understanding on this topic to demonstrate the value of field campaigns. Finally, we conclude with a discussion of the outlook of future research in the study of Arctic cloud-controlling factors and provide several recommendations for the observational and modelling community to advance our understanding of the role of Arctic mixed-phase clouds in a rapidly changing climate.