Dimethyl sulfide in the summertime Arctic atmosphere: measurements and source sensitivity simulations
Bibliographic record
Abstract
Abstract. Dimethyl sulfide (DMS) plays a major role in the global sulfur cycle. In addition, its atmospheric oxidation products contribute to the formation and growth of atmospheric aerosol particles, thereby influencing cloud condensation nuclei (CCN) populations and thus cloud formation. The pristine summertime Arctic atmosphere is strongly influenced by DMS. However, atmospheric DMS mixing ratios have only rarely been measured in the summertime Arctic. During July–August, 2014, we conducted the first high time resolution (10 Hz) DMS mixing ratio measurements for the eastern Canadian Archipelago and Baffin Bay as one component of the Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments (NETCARE). DMS mixing ratios ranged from below the detection limit of 4 to 1155 pptv (median 186 pptv) during the 21-day shipboard campaign. A transfer velocity parameterization from the literature coupled with coincident atmospheric and seawater DMS measurements yielded air–sea DMS flux estimates ranging from 0.02 to 12 µmol m−2 d−1. Air-mass trajectory analysis using FLEXPART-WRF and sensitivity simulations with the GEOS-Chem chemical transport model indicated that local sources (Lancaster Sound and Baffin Bay) were the dominant contributors to the DMS measured along the 21-day ship track, with episodic transport from the Hudson Bay System. After adjusting GEOS-Chem oceanic DMS values in the region to match measurements, GEOS-Chem reproduced the major features of the measured time series but was biased low overall (2–1006 pptv, median 72 pptv), although within the range of uncertainty of the seawater DMS source. However, during some 1–2 day periods the model underpredicted the measurements by more than an order of magnitude. Sensitivity tests indicated that non-marine sources (lakes, biomass burning, melt ponds, and coastal tundra) could make additional episodic contributions to atmospheric DMS in the study region, although local marine sources of DMS dominated. Our results highlight the need for both atmospheric and seawater DMS data sets with greater spatial and temporal resolution, combined with further investigation of non-marine DMS sources for the Arctic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".