Stratospheric chlorine processing after the 2020 Australian wildfires derived from satellite data
Bibliographic record
Abstract
. These fires provided a novel opportunity to evaluate heterogeneous reactions on organic aerosols in the context of stratospheric chlorine and ozone depletion chemistry. It has long been known that heterogeneous chlorine (Cl) activation occurs on the polar stratospheric clouds (PSCs; liquid and solid particles containing water, sulfuric acid, and in some cases nitric acid) that are found in the stratosphere, but these are only effective for ozone depletion chemistry at temperatures below about 195 K (i.e., largely in the polar regions during winter). Here, we develop an approach to quantitatively assess atmospheric evidence for these reactions using satellite data for both the polar (65 to 90°S) and the midlatitude (40 to 55°S) regions. We show that heterogeneous reactions apparently even happened at temperatures at 220 K during austral autumn on the organic aerosols present in 2020 in both regions, in contrast to earlier years. Further, increased variability in HCl was also found after the wildfires, suggesting diverse chemical properties among the 2020 aerosols. We also confirm the expectation based upon laboratory studies that heterogeneous Cl activation has a strong dependence upon water vapor partial pressure and hence atmospheric altitude, becoming much faster close to the tropopause. Our analysis improves the understanding of heterogeneous reactions that are important for stratospheric ozone chemistry under both background and wildfire conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".