Impact of wildfire smoke on Arctic cirrus formation, part 2: simulation of MOSAiC 2019−2020 cases

• Main Authors: Ansmann, Albert, Jimenez, Cristofer, Knopf, Daniel A., Roschke, Johanna, Bühl, Johannes, Ohneiser, Kevin, Engelmann, Ronny
• Format: Text
• Language: English
• Published: 2024
• Subjects: Arctic; Breaker
• Online Access: https://doi.org/10.5194/egusphere-2024-2009; https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2009/

A simulation study on the impact of wildfire smoke (aged organic aerosol particles) on cirrus formation in the central Arctic is presented. The simulations in this part 2 of a series of two articles complement the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) field observations, presented and discussed in part 1. The measurements were performed with lidar and radar aboard the ice breaker Polarstern at latitudes > 85° N during the winter half year 2019–2020. Main goal of the MOSAiC data analysis in part 1 was to gather a consistent set of indications for an impact of the observed aged Siberian wildfire smoke on the formation of embedded ice clouds. The combination of (a) mostly low ice crystal number concentration (ICNC) of 0.1–10 L −1 in almost all of the observed cirrus cloud virga, pointing to heterogeneous ice nucleation, (b) typically high ice saturation ratios in the upper part of the analyzed cirrus systems of around 1.3–1.4, and (c) significantly enhanced levels of smoke pollution characterized by particle surface area concentrations of the order of 5–15 µm 2 cm −3 corroborate our hypothesis that wildfire smoke particles served as ice nucleating particles (INPs) in Arctic cirrus with typical cloud top temperatures of −60 to −75 °C. The observed high ice saturation ratios suggest relatively inefficient ice-active aerosol particles, as expected in the case of wildfire smoke. Main goal of the simulations in part 2 is to gain a deeper insight into the potential smoke influence on cirrus formation as a function of aerosol and meteorological conditions (temperature, relative humidity) and by considering realistic gravity wave characteristics (updraft speed, wave amplitude). The modeling effort uses lidar-derived values of INP number concentration as input and ICNC values retrieved from combined lidar-radar observations for comparison with the simulation results. The ...