Summertime observations of ultrafine particles and cloudcondensation nuclei from the boundary layer to the free tropospherein the Arctic
Bibliographic record
Abstract
Abstract. The Arctic is extremely sensitive to climate change. Shrinking sea ice extent increases the area covered by open ocean during Arctic summer, which impacts the surface albedo and aerosol and cloud properties among many things. In this context extensive aerosol measurements (aerosol composition, particle number and size, cloud condensation nuclei, and trace gases) were made during 11 flights of the NETCARE July, 2014 airborne campaign conducted from Resolute Bay, Nunavut (74N, 94W). Flights routinely included vertical profiles from about 60 to 3000 m a.g.l. as well as several low-level horizontal transects over open ocean, fast ice, melt ponds, and polynyas. Here we discuss the vertical distribution of ultrafine particles (UFP, particle diameter, dp: 5–20 nm), size distributions of larger particles (dp: 20 nm to 1 μm), and cloud condensation nuclei (CCN, supersaturation = 0.6 %) in relation to meteorological conditions and underlying surfaces. UFPs were observed predominantly within the boundary layer, where concentrations were often several hundreds to a few thousand particles per cubic centimeter. Occasionally, particle concentrations below 10 cm−3 were found. The highest UFP concentrations were observed above open ocean and at the top of low-level clouds, whereas numbers over ice-covered regions were substantially lower. Overall, UFP formation events were frequent in a clean boundary layer with a low condensation sink. In a few cases this ultrafine mode extended to sizes larger than 40 nm, suggesting that these UFP can grow into a size range where they can impact clouds and therefore climate.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".