Bibliographic record
Abstract
Abstract. Sulfur compounds in the upper troposphere and lower stratosphere (UTLS) impact the atmosphere radiation budget, either directly as particles or indirectly as precursor gas for new particle formation. In situ measurements in the UTLS are rare but are important to better understand the impact of the sulfur budget on climate. The BLUESKY mission in May and June 2020 explored an unprecedented situation. (1) The UTLS experienced extraordinary dry conditions in spring 2020 over Europe, in comparison to previous years, and (2) the first lockdown of the COVID-19 pandemic caused major emission reductions from industry, ground, and airborne transportation. With the two research aircraft HALO and Falcon, 20 flights were conducted over central Europe and the North Atlantic to investigate the atmospheric composition with respect to trace gases, aerosol, and clouds. Here, we focus on measurements of sulfur dioxide (SO2) and particulate sulfate (SO 42- ) in the altitude range of 8 to 14.5 km which show unexpectedly enhanced mixing ratios of SO2 in the upper troposphere and of SO 42- in the lowermost stratosphere. In the UT, we find SO2 mixing ratios of (0.07±0.01) ppb, caused by the remaining air traffic, and reduced SO2 sinks due to low OH and low cloud fractions and to a minor extent by uplift from boundary layer sources. Particulate sulfate showed elevated mixing ratios of up to 0.33 ppb in the LS. We suggest that the eruption of the volcano Raikoke in June 2019, which emitted about 1 Tg SO2 into the stratosphere in northern midlatitudes, caused these enhancements, in addition to Siberian and Canadian wildfires and other minor volcanic eruptions. Our measurements can help to test models and lead to new insights in the distribution of sulfur compounds in the UTLS, their sources, and sinks. Moreover, these results can contribute to improving simulations of the radiation budget in the UTLS with respect to sulfur effects.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.251 | 0.197 |
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".