Dimethyl sulfide and its role in aerosol formation and growth in the Arctic summer – a modelling study
Bibliographic record
Abstract
Abstract. Atmospheric dimethyl sulfide, DMS(g), is a climatically important sulfur compound and is the main source of biogenic sulfate aerosol in the Arctic atmosphere. DMS(g) production and emission to the atmosphere increase during the summer due to greater ice-free sea surface and higher biological activity. We implemented DMS(g) in the GEM-MACH model (GEM: Global Environmental Multiscale – Environment and Climate Change Canada's (ECCC) numerical weather forecast model, MACH: ECCC's Modelling Air quality and CHemistry – chemistry and aerosol microphysics) for the Arctic region, and compared model simulations with DMS(g) measurements made in Baffin Bay and the Canadian Arctic Archipelago in July and August 2014. Two sea water DMS(aq) datasets were used as input to the simulations: (1) DMS(aq) climatology dataset based on seawater concentration measurements (Lana et al., 2011) and (2) DMS(aq) dataset based on satellite detection (Galí et al., 2018). In general, GEM-MACH simulations underpredict DMS(g) measurements, likely due to negative biases in both DMS(aq) datasets. Yet, higher correlation and smaller bias were obtained with the satellite dataset. Agreement with the observations improved by replacing climatological values with in situ measured DMS(aq) concurrently with atmospheric observations over Baffin Bay and Lancaster Sound area in July 2014. The addition of DMS(g) to the GEM-MACH model resulted in a significant increase in atmospheric SO2 for some regions in the Canadian Arctic (up to 100 %). Analysis of the size-segregated sulfate aerosol in the model shows that a significant increase in sulfate mass occurs for particles with a diameter smaller than 200 nm due to formation and growth of biogenic aerosol at high latitudes (> 70° N). The enhancement in sulfate particles is most significant in the size range of 50 to 100 nm, however, this enhancement is stronger in the 200–1000 nm size range at lower latitudes (
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".