1. Long-term (2010–2021) lidar observations of stratospheric aerosols in Wuhan, China
- Author
-
Y. He, D. Jing, Z. Yin, K. Ohneiser, and F. Yi
- Subjects
Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
This study analyzes the vertical distribution, optical properties, radiative forcing, and several perturbation events of stratospheric aerosols using observations from a ground-based polarization lidar in Wuhan (30.5° N, 114.4° E) from 2010 to 2021. The background stratospheric aerosol optical depth (sAOD) was 0.0044 ± 0.0019 at 532 nm, as calculated during a stratosphere-quiescent period from January 2013 to August 2017. In addition, several cases of volcanic aerosol and wildfire-induced smoke were observed. Volcanic aerosols from the Nabro (2011) and Raikoke (2019) eruptions (both in boreal summer) increased the sAOD to 2.9 times the background level. Tracers of smoke from the Canadian wildfire in the summer of 2017 were observed twice, at 19–21 km on 14–17 September and at 20–23 km on 28–31 October, with a plume-isolated aerosol optical depth (AOD) of 0.002–0.010 and a particle linear depolarization ratio δp of 0.14–0.18, indicating the dominance of non-aged smoke particles. During these summertime events, the injected stratospheric aerosols were captured by the large-scale Asian monsoon anticyclone (AMA), confining the transport pathway to mid-latitude Asia. On 8–9 November 2020, smoke plumes originating from the California wildfire in October 2020 appeared at 16–17 km, with a mean δp of 0.13. Regarding seasonal variation, the sAOD in the cold half-year (0.0054) is 69 % larger than in the warm half-year (0.0032) due to stronger meridional transport of stratospheric aerosols from the tropics to middle latitudes. The stratospheric radiative forcing was −0.11 W m−2 during the stratosphere-quiescent period and increased to −0.31 W m−2 when volcanic aerosols were largely injected. These findings contribute to our understanding of the sources and transport patterns of stratospheric aerosols over mid-latitude Asia and serve as an important database for the validation of model outputs.
- Published
- 2024
- Full Text
- View/download PDF