Pyrocumulonimbus Events over British Columbia in 2017: An ensemble model study of parameter sensitivities and climate impacts of wildfire smoke in the stratosphere
Bibliographic record
Abstract
Pyrocumulonimbus (pyroCb) are fire-triggered or fire-augmented thunderstorms and can by transporting a large amount of smoke particles into the lower stratosphere. With satellite remote sensing measurements, the plumes from pyroCb events over British Columbia in 2017 were observed in the lower stratosphere for about 8-10 months after the smoke injections. Several previous studies used global climate models to investigate the physical parameters for the 2017 pyroCb events, but the conclusions show strong model dependency. In this study, we use Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) and complete an ensemble of runs exploring three injection parameters: smoke amount, the ratio of black carbon to smoke, and injection height. Additionally, we consider the heterogeneous reaction of ozone and primary organic matter. According to the satellite daily observed aerosol optical depth, we find that the best ensemble member is the simulation with 0.2 Tg of smoke, 2% of which is black carbon, a 13.5 km smoke injection height, and a 10 -6 probability factor of the heterogenous reaction of ozone and primary organic matter. We use the Random Forest machine learning technique to quantify the relative importance of each parameter in accurately simulating the 2017 pyroCb events and find that the injection height is the most critical feature. Due to the long lifetime and wide transport of stratospheric aerosols, the estimated e-folding time of smoke aerosols in the stratosphere is about 178 days, and the global averaged shortwave surface cooling is -0.334 W m -2 for about 10 months.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".