DMS emissions from the Arctic marginal ice zone
Bibliographic record
Abstract
Phytoplankton blooms in the Arctic marginal ice zone (MIZ) can be prolific dimethylsulfide (DMS) producers, thereby influencing regional aerosol formation and cloud radiative forcing. Here we describe the distribution of DMS and its precursor dimethylsulfoniopropionate (DMSP) across the Baffin Bay receding ice edge in early summer 2016. Overall, DMS and total DMSP (DMSPt) increased towards warmer waters of Atlantic origin concurrently with more advanced ice-melt and bloom stages. Relatively high DMS and DMSPt (medians of 6.3 and 70 nM, respectively) were observed in the surface layer (0–9 m depth), and very high values (reaching 74 and 524 nM, respectively) at the subsurface biomass maximum (15–30 m depth). Microscopic and pigment analyses indicated that subsurface DMS and DMSPt peaks were associated with Phaeocystis pouchetii, which bloomed in Atlantic-influenced waters and reached unprecedented biomass levels in Baffin Bay. In surface waters, DMS concentrations and DMS:DMSPt ratios were higher in the MIZ (medians of 12 nM and 0.15, respectively) than in fully ice-covered or ice-free conditions, potentially associated with enhanced phytoplanktonic DMSP release and bacterial DMSP cleavage (high dddP:dmdA gene ratios). Mean sea–air DMS fluxes (µmol m–2 d–1) increased from 0.3 in ice-covered waters to 10 in open waters (maximum of 26) owing to concurrent trends in near-surface DMS concentrations and physical drivers of gas exchange. Using remotely sensed sea-ice coverage and a compilation of sea–air DMS flux data, we estimated that the pan-Arctic DMS emission from the MIZ (EDMS, MIZ) was 5–13 Gg S yr–1. North of 80°N, EDMS, MIZ might have increased by around 10 ± 4% yr–1 between 2003 and 2014, likely exceeding open-water emissions in June and July. We conclude that EDMS, MIZ must be taken into account to evaluate plankton-climate feedbacks in 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".