Macrophysical and Microphysical Properties and Processes in Arctic Fog
Principal Investigator: Adele Igel, University of California, Davis
The primary objective of the work is to characterize Arctic fog from DOE ARM observations, including at the North Slope Alaska (NSA), at Oliktok Point (OLI), and during the MOSAiC campaign with a specific goal of better understanding the processes controlling the lifecycle and properties of fog, including supercooled, mixed-phase, and ice fog. The characteristics of fog and its lifecycle have not been assessed previously at NSA or OLI. Low clouds such as fog have a large impact on the surface energy budget which is important for processes such as sea ice melt in the Arctic. Low clouds and fog have been recently referred to as “a blind zone in our knowledge of the radiation budget”. This study will address this blind zone through both better identification of fog and very low cloud periods in the ARM Arctic databases and improved process understanding of Arctic fog. The characterization of Arctic fog will occur through analysis of the extensive ARM observational database including both in situ and remote sensing observations of fog/clouds. Our main focus will be on the NSA site given its 26-year observational record, but the analysis will be supplemented by data at OLI and contrasted with data from MOSAiC which occurred in a more remote region of the Arctic. The exceptionally long data record at NSA will allow us to assess many aspects of the fog lifecycle, including the thermodynamic conditions under which it forms and dissipates, the relationship between fog visibility, fog phase, and aerosol conditions, the impact of fog/low clouds on the surface energy budget, etc. as a function of the season. As part of the work we plan to create an improved low cloud top product for the DOE ARM database. Fog tops will be assessed with lidar measurements when fog top does not extend to the first radar range gate and the presence of cloud in the lidar record will be confirmed with in situ visibility measurements. The observational analysis will be complemented by model simulations to assess the hypotheses that arise.