Marine cold-air outbreaks over the Norwegian–Barents and Labrador seas: observational datasets from the ACAO and M-Phase aircraft campaigns

The response of shallow mid-and high-latitude mixed-phase clouds to changes in climate remains highly uncertain and is an important factor in the overall cloud feedback uncertainty. The uncertainty highlights the need for in-situ observations of mixed-phase cloud microphysical properties along with aerosol and dynamical parameters. Here, we report datasets associated with two aircraft measurement campaigns that were conducted to collect observations of shallow mixed-phase clouds in two regions with contrasting sea surface temperatures and aerosol loadings. The ACAO (Arctic Cold Air Outbreak) campaign was conducted in March 2022 in the Barents and Norweigian seas, while the M-Phase (resolving climate sensitivity associated with shallow mixed phase cloud in the oceanic mid-to high-latitudes) campaign was conducted during October–November 2022 in the Labrador Sea. During the campaigns, 12 flights were conducted in ACAO and 15 in M-Phase. The focus of both campaigns was to characterize aerosol properties, cloud microphysics, thermodynamic structure, and atmospheric dynamics within cold-air outbreak (CAO) cloud systems, as CAOs offer a natural laboratory to quantify mixed-phase aerosol-cloud interactions and cloud dynamics in a well-defined synoptic flow. Of the flights conducted, 11 captured CAO conditions in ACAO and 12 in M-Phase. This paper describes the datasets and how the data were collected. Information on the structure and contents of the files within the datasets are provided in the paper to aid the accessibility of the datasets to new users. We also provide an overview of the large-scale conditions corresponding to the individual flights, which help data-users to choose which flights to focus on. Data from both campaigns are published online and relevant links are provided in the data accessibility section. These datasets offers an excellent opportunity to enhance our understanding of how aerosol particles interact with mixed-phase cloud microphysics, which is essential to reduce uncertainty in cloud-feedbacks.