Evaluation of diverse approaches for estimating sea-surface DMS concentration and air–sea exchange at global scale
Bibliographic record
Abstract
Environmental context As climate models increasingly include detailed, process-based models of aerosol formation, they need to represent dimethylsulfide emissions from the ocean. Options for this include data-based climatologies and empirical or process-based models; there are diverse examples of each in the literature. This paper presents the first global-scale comparison of all available approaches and evaluation of their skill relative to observations and their possible roles in future climate models. Abstract Ocean emission and subsequent oxidation of dimethylsulfide (DMS) provides a source of sulfate in the atmosphere, potentially affecting the amount of solar radiation reaching the Earth’s surface through both direct and indirect radiative effects of sulfate aerosols. DMS concentration in the ocean is quite variable with season and location, which in turn leads to high spatial and temporal variability of ocean DMS emissions. This study tested currently available climatologies and empirical and prognostic models of DMS concentration in the surface ocean against each other and against observations. This analysis mainly reveals the limitations of estimating DMS with an empirical model based on variables such as chlorophyll and mixed-layer depth. The various empirical models show very different spatial patterns, and none correlate strongly with observations. There is considerable uncertainty in the spatial and temporal distribution of DMS concentration and flux, and in the global total DMS flux. Global total air–sea flux depends primarily on global mean surface ocean DMS concentration, and the spatial distribution of DMS concentration and the magnitude of the gas exchange coefficient are of secondary importance. Global total flux estimates range from 9 to 34 Tg S year–1, with a best estimate of 18–24 Tg. Both empirical and prognostic models generally underestimate the total compared with the best available data-based estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".