Local and long-range transported sources of natural aerosols in southern Greenlandic fjord systems
Bibliographic record
Abstract
The Arctic is warming up to four times faster than the global average with fragile fjord ecosystems in the relatively warm Southern Greenland being especially sensitive to changes across various facets of the environment. With longer and warmer summer melt periods leading to increased glacial melt with marine and land-terminating glaciers slowly receding, the potential of sediments from newly exposed glacial outwash plains to be aerosolized increases. At the same time biological productivity in the ocean is changing. Hence, the composition and sources of atmospheric aerosols responsible for the formation of clouds in this region are evolving and we expect this to influence both the cloud condensation nuclei (CCN) and Ice Nucleating Particle (INP) populations. Given the complex terrain and mixture of ice, ocean and land in fjord systems, the dispersion of aerosols and gases originating at the surface is subject to lower atmosphere stability and dynamics before they can reach cloud level. In this presentation, we will show results from a comprehensive and extensive field campaign in the Kullajeq province of Southern Greenland in June-August 2023. We will present vertical aerosol size distributions, particle number concentrations and absorption measurements taken using a tethered balloon in addition to complementary ground based online aerosol measurements. Two key sources of aerosols will be discussed: near-daily local new particle formation (NPF), and long-range transported Canadian wildfire plumes. We will explore the following questions: Are aerosols from fjords and increased biological productivity the source of the frequent NPF observed in Narsaq, and how do aerosols from distant sources such as Canadian biomass burning effect the aerosol population in 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".