The characterization of long-range transported North American biomass burning plumes: what can a multi-wavelength Mie–Raman-polarization-fluorescence lidar provide?

Abstract

Abstract. This article presents a study of long-range transported biomass burning aerosols (BBA) originated from the North American wildfires in September 2020. The BBA plumes presented in this study were in the troposphere and underwent 1–2 weeks of aging before arriving at the lidar station ATOLL (ATmospheric Observatory of LiLle) in northern France. A novel lidar-derived dataset, 2α+3β+3δ+ϕ (α: extinction coefficient; β: backscatter coefficient; δ: particle linear depolarization ratio, i.e., PLDR; ϕ: fluorescence capacity), is provided for the characterization of BBA. The fluorescence capacity is an intensive aerosol parameter describing the ability of aerosols in producing fluorescence when exposed to UV excitation. In our BBA observations, obvious variations in aerosol intensive parameters, reflecting the variability of BBA properties, were detected. The PLDRs varied from less than 0.03 at all wavelengths to 0.15–0.22 and 0.12–0.16, respectively, at 355 and 532 nm. The extinction related Angström exponent was within the range of −0.3 to 1.0 and the fluorescence capacity was 1.0 × 10−4–4.0 × 10−4. Lidar ratio as low as 24 ± 4 sr (50 ± 8 sr) was observed in the BBA plumes at 355 (532) nm on 17–18 September, which was lower than most previously observed aged BBAs. These variations are likely correlated with the combustion process, the lifting of BBA plumes and the conditions (temperature, humidities, etc.) in the aging process. In addition, our results indicate BBA could act as ice nucleating particles in tropospheric conditions. The lidar fluorescence channel proves to be an important added value in aerosol characterization and aerosol–cloud interactions studies, due to its high sensitivity. With the increase in wildfire occurrence and intensity, BBAs become a more and more important atmospheric component. In this context, we show the potential of our novel lidar-derived dataset for aged BBA particles' characterization and for the understanding of their role in cloud processes.