Simulated increases of future Arctic dimethylsulfide ocean concentrations, emissions and high-flux events
Bibliographic record
Abstract
Simulations from a regional ocean and sea ice model are presented to analyze the potential impacts of climate change on dimethylsulfide (DMS) ocean concentrations and emissions in the Arctic Ocean during the 21st century for a scenario of strong warming (RCP8.5, 2016–2085). The model used includes sulfur biogeochemistry in both the ocean and sea ice, representing the production of dimethylsulfoniopropionate and its conversion to DMS. Simulated DMS concentrations and emissions increase overall in the future throughout the Arctic. Substantial increases of summer ocean surface DMS concentrations and emissions are projected in the shallow continental shelves of the Eastern Arctic, due to a large reduction of sea ice cover. In the Central and Western Arctic, moderate increases of spring DMS production are trapped below sea ice even in the late 21st century. In deep basins, despite ice-free summers in the future, simulated DMS emissions are low, as DMS production occurs mostly below the mixed layer and remains at depth. The strong temporal variability of near-surface winds results in bursts of DMS emissions lasting a few days, with sea-to-air fluxes up to 10 times higher than the monthly median emissions rate. These spikes of DMS emissions occur throughout the Arctic, indicating an episodic impact of DMS on climate in areas of low mean DMS emissions. The simulated frequency of high-flux events increases during the 21st century in both spring and summer in almost all regions of the Arctic. However, the model is not capable of representing rapid out-gassing events during sea ice break-up, and improvements in the representation of leads are still necessary to fully assess the role of sea ice DMS production. With the ongoing decrease in anthropogenic sulfur emissions, these results suggest a future amplification of the role of DMS in aerosol and cloud formation in the Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".