Long-term (2010–2021) lidar observations of stratospheric aerosols at Wuhan, China
Bibliographic record
Abstract
Abstract. Stratospheric aerosols are long-lived and play a critical role in the global radiation budget. Over the past decade, contributions to stratospheric aerosols from different sources have changed due to weaker volcanic activity and more frequent wildfire events. However, long-term observations of stratospheric aerosols and monitoring of major emission events remain insufficient, particularly at middle and low latitudes. In this study, we analyze the vertical distribution, optical properties, and radiative forcing of stratospheric aerosols using observations from a ground-based polarization lidar in Wuhan (30.5° N, 114.4° E) from 2010 to 2021. The stratospheric aerosol optical depth (sAOD) generally stabilized around 0.0023 without significant annual variation. 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 4.8 times the background level during the stratospheric-quiescent period (January 2013 to August 2017). Tracers of smoke from the Canadian wildfire in the summer of 2017 was observed twice: at 19–21 km on 14–17 September and at 20–23 km on 28–31 October, with plume-isolated AOD of 0.002–0.010 and 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 plume-isolated AOD of 0.007 and a mean δp of 0.13. Regarding seasonal variation, the sAOD in the cold half-year (0.0026) is 24 % larger than in the warm half-year (0.0021) due to stronger meridional transport of stratospheric aerosols from the tropics to middle latitudes. The stratospheric radiative forcing was -0.05 W·m-2 during the stratospheric-quiescent period and increased to -0.28 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 important database for the validation of model outputs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".