Dimethyl sulfide control of the clean summertime Arctic aerosol and cloud
Bibliographic record
Abstract
Abstract One year of aerosol particle observations from Alert, Nunavut shows that new particle formation (NPF) is common during clean periods of the summertime Arctic associated with attendant low condensation sinks and with the presence of methane sulfonic acid (MSA), a product of the atmospheric oxidation of dimethyl sulfide (DMS). The clean aerosol time periods, defined using the distribution of refractory black carbon number concentrations, increase in frequency from June through August as the anthropogenic influence dwindles. During the clean periods, the number concentrations of particles that can act as cloud condensation nuclei (CCN) increase from June through August suggesting that DMS, and possibly other oceanic organic precursors, exert significant control on the Arctic summertime submicron aerosol, a proposition supported by simulations from the GEOS-Chem-TOMAS global chemical transport model with particle microphysics. The CCN increase for the clean periods across the summer is estimated to be able to increase cloud droplet number concentrations (CDNC) by 23–44 cm-3, comparable to the mean CDNC increase needed to yield the current global cloud albedo forcing from industrial aerosols. These results suggest that DMS may contribute significantly to modification of the Arctic summer shortwave cloud albedo, and they offer a reference for future changes in the Arctic summer aerosol.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".