Diagnosing sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales
Bibliographic record
Abstract
Abstract. The marine biogenic gas dimethylsulfide (DMS) can modulate regional and global climate by enhancing aerosol light scattering and seeding cloud formation. However, the lack of time- and space-resolved estimates of DMS concentration and emission hampers the assessment of its climatic effects. Here we present DMSSAT, a new remote sensing algorithm that relies on the nonlinear relationship between DMS, its phytoplanktonic precursor dimethylsulfoniopropioante (DMSPt) and plankton light exposure. The DMSSAT algorithm is computationally light and can be easily optimized for global and regional scales. At the global scale, it reproduces the main climatological features of DMS seasonality across contrasting biomes with remarkable skill compared to previous algorithms. Shortcomings of the global-scale optimized algorithm are the propagation of regional biases in remotely sensed chlorophyll (causing underestimation of DMS in the Southern Ocean) and the inability to reproduce high DMS/DMSPt ratios in late summer and fall in specific regions (which suggests the need to account for additional DMS drivers). Our work also highlights the shortcomings of interpolated DMS climatologies, caused by sparse and biased in situ sampling. Time series of DMSSAT between 2003–2016 in northern subpolar regions show wide interannual variability in the magnitude and timing of the annual DMS peak(s), demonstrating the need to move beyond the climatological view in studies of ocean-atmosphere interactions. By providing time- and space-resolved estimates of DMS emission, DMSSAT can leverage atmospheric chemistry and climate models and advance our understanding of plankton-aerosol-cloud interactions in the context of global change.
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 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) | 0.001 | 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 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".