Links between atmospheric aerosols and sea state in the Arctic Ocean
Bibliographic record
Abstract
Sea spray emission is the largest mass flux of aerosols to the atmosphere with important impact on atmospheric radiative transfer. However, large uncertainties still exit in constraining this mass flux and its climate forcing, in particular in the Arctic, where sea ice and relatively low wind speed in summer constitute a significantly different regime compared to the global ocean. Sea state conditions and marine boundary layer stability are also critical variables, but their contribution is often overlooked. Here we present concurrent observations of sea state using a novel stereo camera system, of sea spray through coarse mode aerosols, and of meteorological variables to determine boundary layer stability in the Barents and Kara Seas during the 2021 Arctic Century Expedition. Our findings reveal that aerosol concentrations were highest over open waters, closely correlating with wave height, followed by wind speed, wave steepness, and wave age. Notably, these correlations were stronger under unstable marine boundary layer conditions, reflecting immediate sea spray generation. By analysing various combinations of sea and atmospheric variables, we identified the wave height Reynolds number as the most effective indicator of atmospheric sea spray concentration, explaining 57% of its variability in unstable conditions. Our study underscores the need to consider sea state, wind, and boundary layer conditions together to accurately estimate atmospheric sea spray concentrations in the Arctic. • Sea surface state relation to coarse mode aerosol was studied over the Arctic Ocean. • Aerosol concentration correlated stronger to sea state in unstable boundary layer. • Wave height Reynolds number best parametrized the aerosol concentration variability.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".