Modeling of biomass smoke injection into the lower stratosphere by a large forest fire (Part II): sensitivity studies
Bibliographic record
Abstract
Abstract. The Chisholm forest fire that burned in Alberta, Canada, in May 2001 resulted in injection of substantial amounts of smoke into the lower stratosphere. We used the cloud-resolving plume model ATHAM (Active Tracer High resolution Atmospheric Model) to investigate the importance of different contributing factors to the severe intensification of the convection induced by the Chisholm fire and the subsequent injection of biomass smoke into the lower stratosphere. The simulations show strong sensitivity of the pyro-convection to background meteorology. This explains the observed coincidence of the convective blow-up of the fire plume and the passage of a synoptic cold front. Furthermore, we performed model sensitivity studies to the rate of release of sensible heat and water vapor from the fire. The release of sensible heat by the fire plays a dominant role for the dynamic development of the pyro-cumulonimbus cloud (pyroCb) and the height to which smoke is transported. The convection is very sensitive to the heat flux from the fire. The emissions of water vapor play a less significant role for the injection height but enhance the amount of smoke transported beyond the tropopause level. The aerosol burden in the plume has a strong impact on the microphysical structure of the resulting convective cloud. The dynamic evolution of the pyroCb, however, is only weakly sensitive to the abundance of cloud condensation nuclei (CCN) from the fire. In contrast to previous findings by other studies of convective clouds, we found that fire CCN have a negative effect on the convection dynamics because they give rise to a delay in the freezing of cloud droplets. Even in a simulation without fire CCN, there is no precipitation formation within the updraft region of the pyroCb. Enhancement of convection by aerosols as reported from studies of other cases of convection is therefore not found in our study.
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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) | 0.000 | 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".