Stratospheric Injection of Massive Smoke Plume From Canadian Boreal Fires in 2017 as Seen by DSCOVR‐EPIC, CALIOP, and OMPS‐LP Observations
Bibliographic record
Abstract
Abstract A carbonaceous aerosol plume associated with wildfires in British Columbia in August 2017 reached the stratosphere a few days following the fires onset. The Earth Polychromatic Imaging Camera (EPIC) sensor onboard the Deep Space Climate Observatory (DSCOVR) satellite observed the spatial and temporal evolution of the plume for about 6 weeks. EPIC's near‐hourly observations of the Earth's sunlit disk allowed for monitoring the smoke plume several times per day. High ultraviolet aerosol index values pointed to the presence of an aerosol layer in the upper troposphere and lower stratosphere, confirmed with Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar observations. EPIC aerosol optical depth retrievals and CALIOP information on layer height were used to estimate the stratospheric portion of the aerosol mass, reaching a maximum of 268 ± 86 Kt 3 days after the onset of the wildfires. About 10% of this aerosol mass reached the stratosphere on 13 August, whereas the remaining 90% did so over the following 2 days. Observed daytime and inferred nighttime rates of stratospheric aerosol mass increase showed a more rapid daytime increase, consistent with variability in diurnal insolation, suggesting that solar radiation driven processes may have contributed to the observed stratospheric aerosol mass increase. Global modeling results support the possible role of diabatic aerosol self‐lofting. Ozone Mapping and Profiler Suite Limb Profiler (OMPS‐LP) aerosol retrievals showed the spread of stratospheric aerosols in the Northern Hemisphere, reaching peak concentration between October 2017 and January 2018, when about half the Northern Hemisphere experienced a detectable enhancement of stratospheric aerosols with respect to preinjection conditions. The lifetime and aerosol mass of this smoke event were comparable to those of a moderate volcanic eruption.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".