Climate warming increases global oceanic dimethyl sulfide emissions
Bibliographic record
Abstract
Oceanic dimethyl sulfide (DMS) is the largest natural source of atmospheric sulfur. DMS is biologically produced in seawater and emitted into the atmosphere, where its oxidation products contribute to aerosol formation with consequences for cloud albedo and the Earth's radiative budget and climate. Climate model projections of how DMS emissions change with global warming are largely uncertain, even contradictory. Here, we use machine-learning models trained with biome-resolved global observations to simulate seawater DMS concentrations (1850 to 2100) using physico-chemical and biological predictors from eight CMIP6 models. The scatter in current projections is largely reduced, and globally averaged seawater DMS concentrations are predicted to decrease in the coming decades. However, global DMS emissions will increase due to rising surface wind speeds and sea surface temperatures which contradicts the current AR6 assessment that the DMS flux will reduce in the future. Concurrence of increasing DMS emissions and declining anthropogenic sulfur dioxide emissions suggests an increase in the relative importance of DMS to sulfate aerosol formation and its climate cooling impact.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".