Modeling of biomass smoke injection into the lower stratosphere by a large forest fire (Part I): reference simulation
Bibliographic record
Abstract
Abstract. Wildland fires in boreal regions have the potential to initiate deep convection, so-called pyro-convection, due to their release of sensible heat. Under favorable atmospheric conditions, large fires can result in pyro-convection that transports the emissions into the upper troposphere and the lower stratosphere. Here, we present three-dimensional model simulations of the injection of fire emissions into the lower stratosphere by pyro-convection. These model simulations are constrained and evaluated with observations obtained from the Chisholm fire in Alberta, Canada, in 2001. The active tracer high resolution atmospheric model (ATHAM) is initialized with observations obtained by radiosonde. Information on the fire forcing is obtained from ground-based observations of the mass and moisture of the burned fuel. Based on radar observations, the pyro-convection reached an altitude of about 13 km, well above the tropopause, which was located at about 11.2 km. The model simulation yields a similarly strong convection with an overshoot of the convection above the tropopause. The main outflow from the pyro-convection occurs at about 10.6 km, but a significant fraction (about 8%) of the emitted mass of the smoke aerosol is transported above the tropopause. In contrast to regular convection, the region with maximum updraft velocity in the pyro-convection is located close to the surface above the fire. This results in high updraft velocities >10 m s−1 at cloud base. The temperature anomaly in the plume decreases rapidly with height from values above 50 K at the fire to about 5 K at about 3000 m above the fire. While the sensible heat released from the fire is responsible for the initiation of convection in the model, the release of latent heat from condensation and freezing dominates the overall energy budget. Emissions of water vapor from the fire do not significantly contribute to the energy budget of the convection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 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".