Injection in the lower stratosphere of biomass fire emissions followed by long-range transport: a MOZAIC case study
Bibliographic record
Abstract
Abstract. This paper analyses a stratospheric injection by deep convection of biomass fire emissions over North America (Alaska, Yukon and Northwest Territories) on 24 June 2004 and its long-range transport over the eastern coast of the United States and the eastern Atlantic. The case study is based on airborne MOZAIC observations of ozone, carbon monoxide, nitrogen oxides and water vapour during the crossing of the southernmost tip of an upper level trough over the Eastern Atlantic on 30 June and on a vertical profile over Washington DC on 30 June, and on lidar observations of aerosol backscattering at Madison (University of Wisconsin) on 28 June. Attribution of the observed CO plumes to the boreal fires is achieved by backward simulations with a Lagrangian particle dispersion model (FLEXPART). A simulation with the Meso-NH model for the source region shows that a boundary layer tracer, mimicking the boreal forest fire smoke, is lofted into the lowermost stratosphere (2–5 pvu layer) during the diurnal convective cycle at isentropic levels (above 335 K) corresponding to those of the downstream MOZAIC observations. It is shown that the order of magnitude of the time needed by the parameterized convective detrainment flux to fill the volume of a model mesh (20 km horizontal, 500 m vertical) above the tropopause with pure boundary layer air would be about 7.5 h, i.e. a time period compatible with the convective diurnal cycle. Over the area of interest, the maximum instantaneous detrainment fluxes deposited about 15 to 20% of the initial boundary layer tracer concentration at 335 K. According to the 275-ppbv carbon monoxide maximum mixing ratio observed by MOZAIC over Eastern Atlantic, such detrainment fluxes would be associated with a 1.4–1.8 ppmv carbon monoxide mixing ratio in the boundary layer over the source region.
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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 | 0.000 |
| 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 | 0.001 |
| 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".