Lidar observations of pyrocumulonimbus smoke plumes in the UTLS over Tomsk (Western Siberia, Russia) from 2000 to 2017
Bibliographic record
Abstract
Abstract. Large volcanic eruptions with the volcanic explosivity index (VEI) ≥ 3 are widely known to be the strongest source of long-lived aerosol in the upper troposphere and lower stratosphere (UTLS). However, the latest studies have revealed that massive forest (bush) fires represent another strong source of short-term (but intense) aerosol perturbations in the UTLS if combustion products from the fires reach these altitudes via convective ascent within pyrocumulonimbus clouds (pyroCbs). PyroCbs, generated by boreal wildfires in North America and northeastern Asia and injecting smoke plumes into the UTLS, have been intensively studied using both ground- and space-based instruments since the beginning of the 21st century. In this paper, we focus on aerosol layers observed in the UTLS over Tomsk (56.48∘ N, 85.05∘ E, Western Siberia, Russia) that could be smoke plumes from such pyroCb events occurring in the 2000–2017 period. Using the HYSPLIT trajectory analysis, we have reliably assigned nine aerosol layers to 8 out of more than 100 documented pyroCb events, the aftereffects of which could potentially be detected in the UTLS over Tomsk. All the eight pyroCb events occurred in the USA and Canada: one event per year occurred in 2000, 2002, 2003, 2013, 2015, and 2016, whereas two events occurred in 2017. No plumes from pyroCbs originating in the boreal zone of Siberia and the Far East (to the east of Tomsk) were observed in the UTLS over Tomsk between 2000 and 2017. We conclude that the time durations for pyroCb plumes to be detected in the UTLS using ground-based lidars are less than about a month, i.e., plumes from pyroCbs generated by wildfires to the east of Tomsk can significantly diffuse before reaching the Tomsk lidar station by the westerly zonal transport of air masses. A comparative analysis of the contributions from pyroCb events and volcanic eruptions with VEI ≥ 3 to aerosol loading of the UTLS over Tomsk showed the following. Plumes from two or more pyroCbs that have occurred in North America in a single year are able to markedly increase the aerosol loading compared to the previous year. The annual average value of the integrated aerosol backscatter coefficient Bπ,532a increased by 14.8 % in 2017 compared to that in 2016 due to multiple pyroCbs occurring in British Columbia (Canada) in August 2017. The aftereffects of pyroCb events are comparable to those of volcanic eruptions with VEI ≤ 3, but even multiple pyroCbs can hardly compete with volcanic eruptions with VEI = 4.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".