C044-0006
Wintertime Aerosol Observations during MOSAiC

Monday, 14 December 2020
Poster
Ivo Beck1, Andrea Baccarini2, Lauriane Quéléver3, Tuija Jokinen3, Tiia Laurila3, Markus Lampimäki3, Mikko Sipilä3, Markku Tapio Kulmala3, Zoé Brasseur3, Tuukka Petäjä3 and Julia Schmale1, (1)Swiss Federal Institute of Technology Lausanne, School of Architecture, Civil and Environmental Engineering ENAC, Lausanne, Switzerland, (2)Paul Scherrer Institute, Villigen, Switzerland, (3)University of Helsinki, Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, Helsinki, Finland
Abstract:
Aerosols influence Arctic climate directly through absorption and scattering of sunlight and indirectly, through their effect on cloud radiative properties. However, aerosol-cloud interactions in the Arctic remain poorly understood and information on aerosol properties is insufficient, especially during the winter season. The lack of knowledge on Arctic aerosol properties is one of the reasons why climate models struggle to simulate Arctic clouds and their effects on the regional climate. In view of the accelerated warming in the Arctic compared to lower latitudes, it is important to improve our understanding of aerosol sources and processes in this region throughout all seasons.

The year-long MOSAiC expedition (Multidisciplinary Drifting Observatory of the Study of Arctic Climate) is a unique opportunity to improve our understanding of Arctic aerosol processes, from fall freeze up through the summer melt.

In this contribution, we present the first results from observations made during the polar winter as part of the MOSAiC expedition. We focus on aerosol number concentration, size distribution and microphysical properties (e.g. hygroscopicity) and characterize their evolution and variability during the winter season. Our observations provide useful insights to contrast local sources, such as blowing snow, with long range transport influencing Arctic haze formation. We compare our results from the central Arctic Ocean to lower latitude Arctic permanent observatories.

We also present the realization of a pollution mask to identify contaminations from, e.g., the ship exhaust and separate them from clean data. For this purpose, we evaluate the performances of different techniques, including machine-learning techniques, in detecting polluted data.