Improving Estimates of Dynamic Global Marine DMS and Implications for Aerosol Radiative Effect
Bibliographic record
Abstract
Abstract Dimethyl sulfide (DMS) is the predominant natural sulfur source and plays a pivotal role in regulating global climate. However, the current method for estimating seawater DMS concentrations has limitations, and the existing DMS‐induced radiative effect heavily relies on bottom‐up DMS climatologies. This study aims to improve the method for estimating seawater DMS concentrations as well as to evaluate its induced aerosol direct radiative effect (DRE) and indirect radiative effect (IRE) using a state‐of‐the‐art aerosol microphysics scheme integrated with a chemical transport model. The predicted seawater DMS concentrations based on data‐driven methods were verified with multi‐year in situ measurements, revealing a marked reduction in mean bias by over 80%. Results show that our estimates generally indicate lower seawater DMS concentrations (1.48–1.88 μmol/m3) compared to previous seawater DMS climatologies, with differences ranging from −37% to 11%, and that interannual variability in DMS concentrations is varies significantly, particularly in polar regions. The DRE and cloud‐albedo IRE induced by DMS were −0.06 and −0.19 W/m2, respectively, representing a cooling effect on radiative effect that was weaker by 31.4% and 27.0% of those derived from the commonly used bottom‐up DMS climatology. The comprehensive evaluation of the model's performance of atmospheric DMS prediction based on global‐scale observations shows a significant improvement after using our estimates. Thus, we conclude that the global DMS fluxes provided in the past are overestimated, including its resulting DMS radiative effect, which highlights the need for refining the estimation of global aerosol radiative effect to enhance the accuracy of assessing aerosol‐induced climate impacts.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".