Recent evolution of stratospheric aerosol load from ground-based lidars and satellites: impact of volcanic eruptions and wildfires.
Bibliographic record
Abstract
During the last 2 years (2018-2019) a series of volcanic eruptions led to remarkable enhancements in stratospheric aerosol load. These are eruptions of Ambae (July 2018, Vanuatu), Raikoke (June 2019, Russia) and Ulawun (July 2019, Papua New Guinea). In this study we examine the evolution of the stratospheric aerosol bulk optical properties following these events in consideration of large-scale stratospheric circulation. We use long-term aerosol records by ground-based lidars in both hemispheres together with global observations by various satellite missions (OMPS-LP, SAGE III, OSIRIS, CALIOP) and discuss the consistency between these datasets. In addition, we evaluate the preliminary lower stratosphere aerosol product by ESA Aeolus mission through intercomparison with ground-based lidars. The 28-yr Observatoire de Haute Provence (OHP) lidar record shows that Raikoke eruption has led to the strongest enhancement of stratospheric aerosol optical depth (SAOD) in the northern extratropics since Pinatubo eruption. Satellite observations suggest that the stratospheric plume of Raikoke has dispersed throughout the entire Northern hemisphere and ascended up to 27 km altitude. The eruption of Ulawun in the tropics has further boosted the stratospheric aerosol load and by Fall 2019, the global mean SAOD was a factor of 2.5 higher than its background level. At the turn of the year 2020, while both Raikoke and Ulawun aerosols were still present in the stratosphere, a dramatic bushfire event accompanied by vigorous fire-induced thunderstorms (PyroCb) in eastern Australia caused a massive injection of smoke into the stratosphere. The early detections of stratospheric smoke by OMPS-LP suggest that the zonal-mean SAOD perturbation caused by this event exceeds the previous record-breaking PyroCb-related perturbation after the British Columbia fires in August 2017. We use satellite observations of aerosol and trace gases (H2O, CO) to characterize the stratospheric impact of the wildfires and contrast it with that of volcanic eruptions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".