Unravelling Surface Seawater DMS Concentration and Sea‐To‐Air Flux Changes After Sea Ice Retreat in the Western Arctic Ocean
Bibliographic record
Abstract
Abstract The receding of the seasonal ice cover in the Arctic due to climate change has been predicted by models to increase climate‐active biogenic trace gas emissions, specifically those of dimethylsulfide (DMS). However, insufficient DMS measurements are currently available to either support or refute this hypothesis and to fully understand the various responses of oceanic DMS in a rapidly changing Arctic Ocean environment. Here, we present high‐resolution surface water DMS data collected in the summer of 2014 in combination with a suite of ancillary variables including sea ice cover, salinity, and nutrients. We show that surface seawater DMS concentrations, generally below 0.5 nmol L −1 , remained unchanged in the Canada Basin after sea ice retreat probably due to insufficient nutrients supply to the upper mixed layer and resulting low primary production. Moreover, in the Chukchi shelf region, DMS concentrations decreased following a phytoplankton bloom due to the rapid depletion and slow resupply of nutrients. Although the DMS sea‐to‐air fluxes were not high from a global perspective, they increased by a factor of 4‐fold after sea ice retreat in the Arctic Ocean high latitudes. This increase in DMS flux was mainly driven by increased wind speed. This work provides unique observations and insights on how surface seawater DMS and flux to the atmosphere may change in the future Arctic Ocean.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".