Role of dimethyl sulfide on the formation and growth of aerosols, and its impact on liquid clouds in the Arctic summer
Bibliographic record
Abstract
Atmospheric dimethyl sulfide, DMS, is the main biogenic source of sulfate particles in the Arctic atmosphere. Sulfate particles have a net cooling effect, which can partially offset Arctic warming from absorbing aerosols, such as black carbon. As efficient cloud condensation nuclei (CCN), sulfate particles are also able to influence the cloud’s microphysical properties. DMS production and emission to the atmosphere increase during the Arctic summer, due to a greater ice-free sea surface area and higher biological activity. In the model simulation of a field campaign conducted over the Canadian high Arctic during the summer of 2014 (NETCARE; Abbatt et al. 2019), the inclusion of DMS in the model, GEM-MACH, resulted in a significant increase, up to 100%, in the modelled atmospheric SO2 in some regions of the Canadian Arctic. Analysis of the modelled size-segregated aerosol sulfate indicated that DMS has the most significant impact on particles in the size range of 50 – 200 nm in this case. Simulations have shown that localized regions of high seawater DMS can have a significant impact on atmospheric concentrations. Further investigation of DMS impact on the Arctic summer cloud microphysics was carried out by using a fully coupled version of GEM-MACH. Overall, the model simulations show that the inclusion of DMS in model leads to an increase in cloud droplet number concentrations (CDNC) and a decrease in droplet mean mass diameters (MMD), and has no significant effects on liquid water content (LWC). The impact of DMS on Canadian weather forecasts will be evaluated using operational forecast tools.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".