Vertically-resolved source contributions to climate-relevant aerosol properties in Southern Greenlandic fjord systems
Bibliographic record
Abstract
Abstract. Greenlandic fjords are rapidly changing environments where ocean, ice, land, and atmosphere interact, yet aerosol sources and their role in cloud formation remain relatively unconstrained. To address this gap, we conducted an intensive field campaign in Narsaq, Southern Greenland, during summer 2023 as part of the GreenFjord project, combining ground-based sampling with a tethered balloon (24 flights to ~ 700 m). Over six weeks, we measured meteorology, aerosol size distributions, particle and CCN number concentrations, as well as optical properties. Aerosol characteristics varied markedly in time and altitude, reflecting four main sources: fresh anthropogenic pollution, aged background aerosols from local anthropogenic and marine origins, transported biomass burning, and new particle growth events. Local pollution dominated ~ 50 % of the campaign, while growth events and Canadian wildfire plumes each contributed ~ 8 %; the remainder (~ 34 %) reflected aged marine background aerosols. Number size distributions were typically Aitken-mode dominated, presumably due to the frequently observed growth events. Biomass burning advection, in contrast, was marked by accumulation-mode particles. During plume periods, median CCN concentrations at 0.5 % supersaturation increased by a factor of 1.7–3.7 relative to median background concentrations, while median absorption and scattering coefficients increased by factors of 1.8–4.0 and 1.4–4.8, respectively. The enhancement factors are similar to or even exceed the enhancements from local anthropogenic pollution and highlight the substantial role that long-range transported biomass burning may have in modulating aerosol–cloud–radiation interactions over southern Greenland.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".