The importance of oceanic emissions for modelling Arctic aerosols and clouds
Bibliographic record
Abstract
Emissions of primary aerosols and aerosol precursors from the ocean are key for the Arctic climate. Among those, secondary aerosols from oceanic dimethylsulfide (DMS) are a key species for aerosol-radiation and aerosol-cloud interactions. However, the representation of DMS in atmospheric models is challenging, which generates large uncertainties in the Arctic aerosol budget. In this work we evaluate the sensitivity of simulated Arctic aerosols and clouds in the WRF-Chem atmospheric chemistry model, over a complete annual cycle, to (1) the representation of DMS chemistry in the atmosphere and (2) the oceanic DMS concentration product used as boundary condition. For (2), we compare the results obtained using the Lana et al. (2011) global climatology versus dedicated simulations of the Arctic Ocean biogeochemistry with NEMO-CSIB. We find that aerosol number concentrations can change by up to more than 100%, including over sea ice, depending on the model configuration, with a greater sensitivity to the chemistry mechanism than to the oceanic DMS product. This change is negative in the summer, which leads to decreased cloud droplet number and increased (decreased, respectively) shortwave (longwave, respectively) radiation at the surface over sea ice. The opposite effect is found in late spring and autumn. Overall, we find that using a more complex chemistry and better description of Arctic Ocean DMS has an impact on the surface energy budget of +4 W/m2 on average for the year 2018, both over sea ice and the open ocean. This configuration also performs best compared to observations. Additional experiments evaluating the changes of aerosol number under future oceanic DMS concentrations, potential emissions of DMS through sea ice, and the role of methanesulfonic acid (MSA) nucleation in summertime aerosol number concentration are presented. This work demonstrates the importance of accurately modeling DMS for simulations of the Arctic aerosol budget and climate, and the value-added of forcing atmospheric models with ocean biogeochemistry simulations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".