Modelling wintertime Arctic Haze and sea-spray aerosols

• Main Authors: Ioannidis, Eleftherios, Law, Kathy S., Raut, Jean-Christophe, Marelle, Louis, Onishi, Tatsuo, Kirpes, Rachel M., Upchurch, Lucia, Massling, Andreas, Skov, Henrik, Quinn, Patricia K., Pratt, Kerri A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Arctic; Barrow; Alaska
• Online Access: https://doi.org/10.5194/egusphere-2022-310; https://noa.gwlb.de/receive/cop_mods_00060969; https://egusphere.copernicus.org/preprints/egusphere-2022-310/egusphere-2022-310.pdf

Anthropogenic and natural emissions contribute to enhanced concentrations of aerosols, so-called Arctic Haze in the Arctic winter and early spring. Models still have difficulties reproducing available observations. Whilst most attention has focused on the contribution of anthropogenic aerosols, there has been less focus on natural components such as sea-spray aerosols (SSA), including sea-salt sulphate and marine organics, which can make an important contribution to fine and coarse mode aerosols, particularly in coastal areas. Models tend to underestimate sub-micron and overestimate super-micron SSA in polar regions, including in the Arctic region. Quasi-hemispheric runs of the Weather Research Forecast model, coupled with chemistry model (WRF-Chem) are compared to aerosol composition data at remote Arctic sites to evaluate the model performance simulating wintertime Arctic Haze. Results show that the model overestimates sea-salt (sodium and chloride) and nitrate and underestimates sulphate aerosols. Inclusion of more recent wind-speed and sea-surface temperature dependencies for sea-salt emissions, as well as inclusion of marine organic and sea-salt sulphate aerosol emissions leads to better agreement with the observations during wintertime. The model captures better the contribution of SSA to total mass for different aerosol modes, ranging from 20–93 % in the observations. The sensitivity of modelled SSA to processes influencing SSA production are examined in regional runs over northern Alaska (United States) where the model underestimates episodes of high SSA, particularly in the sub-micron, that were observed in winter 2014 during field campaigns at the Barrow Observatory, Utqiaġvik. A local source of marine organics is also included following previous studies showing evidence for an important contribution from marine emissions. Model results show relatively small sensitivity to aerosol dry removal with more sensitivity (improved biases) to using a higher wind speed dependence based on sub-micron data ...