The sensitivity of CO and aerosol transport to the temporal and vertical distribution of North American boreal fire emissions
Bibliographic record
Abstract
Abstract. Forest fires in Alaska and western Canada represent important sources of aerosols and trace gases in North America. Among the largest uncertainties when modeling forest fire effects are the timing and injection height of biomass burning emissions. Here we simulate CO and aerosols over North America during the 2004 fire season, using the GEOS-Chem chemical transport model. We apply different temporal distributions and injection height profiles to the biomass burning emissions, and compare model results with satellite-, aircraft-, and ground-based measurements. We find that averaged over the fire season, the use of finer temporal resolved biomass burning emissions usually decreases CO and aerosol concentrations near the fire source region, and often enhances long-range transport. Among the individual temporal constraints, switching from monthly to 8-day time intervals for emissions has the largest effect on CO and aerosol distributions, and shows better agreement with measured day-to-day variability. Injection height substantially modifies the surface concentrations and vertical profiles of pollutants near the source region. Compared with CO, the simulation of black carbon aerosol is more sensitive to the temporal and injection height distribution of emissions. The use of MISR-derived injection heights improves agreement with surface aerosol measurements near the fire source. Our results indicate that the discrepancies between model simulations and MOPITT CO measurements near the Hudson Bay can not be attributed solely to the representation of injection height within the model. Frequent occurrence of strong convection in North America during summer tends to limit the influence of injection height parameterizations of fire emissions in Alaska and western Canada with respect to CO and aerosol distributions over eastern North America.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".