Boreal forest fires in 1997 and 1998: a seasonal comparison using transport model simulations and measurement data
Bibliographic record
Abstract
Abstract. Forest fire emissions have a strong impact on the concentrations of trace gases and aerosols in the atmosphere. In order to quantify the influence of boreal forest fire emissions on the atmospheric composition, the fire seasons of 1997 and 1998 are compared in this paper. Fire activity in 1998 was very strong, especially over Canada and Eastern Siberia, whereas it was much weaker in 1997. According to burned area estimates the burning in 1998 was more than six times as intense as in 1997. Based on hot spot locations derived from ATSR (Along Track Scanning Radiometer) data and official burned area data, fire emissions were estimated and their transport was simulated with a Lagrangian tracer transport model. Siberian and Canadian forest fire tracers were distinguished to investigate the transport of both separately. The fire emissions were transported even over intercontinental distances. Due to the El Niño induced meteorological situation, transport from Siberia to Canada was enhanced in 1998. Siberian fire emissions were transported towards Canada and contributed concentrations more than twice as high as those due to Canada's own CO emissions by fires. In 1998 both tracers arrive at higher latitudes over Europe, which is due to a higher North Atlantic Oscillation (NAO) index in 1998. The simulated emission plumes are compared to CMDL (Climate Monitoring and Diagnostics Laboratory) CO2 and CO data, Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI) data and Global Ozone Monitoring Experiment (GOME) tropospheric NO2 and HCHO columns. All the data show clearly enhanced signals during the burning season of 1998 compared to 1997. The results of the model simulation are in good agreement with ground-based as well as satellite-based measurements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".