Hygroscopicity and composition of Alaskan Arctic CCN during April 2008
Bibliographic record
Abstract
Abstract. We present a comprehensive characterization of cloud condensation nuclei (CCN) sampled in the Alaskan Arctic during the 2008 Aerosol, Radiation, and Cloud Processes affecting Arctic Climate (ARCPAC) project, a component of the POLARCAT and International Polar Year (IPY) initiatives. Four distinct air mass types were sampled including relatively pristine Arctic background conditions as well as biomass burning and anthropogenic pollution plumes. Despite differences in chemical composition, inferred aerosol hygroscopicities were fairly invariant and ranged from κ = 0.1–0.3 over the atmospherically-relevant range of water vapor supersaturations studied. Analysis of the individual mass spectral m/z 43 and 44 peaks from an aerosol mass spectrometer show the organic aerosols sampled to be well-oxygenated, consistent with with long-range transport and aerosol aging processes. However, inferred hygroscopicities are less than would be predicted based on previous parameterizations of biogenic oxygenated organic aerosol, suggesting an upper limit on organic aerosol hygroscopicity above which κ is less sensitive to the O:C ratio. Most Arctic aerosol act as CCN above 0.1 % supersaturation, although the data suggest the presence of an externally-mixed, non-CCN-active mode comprising approximately 0–20 % of the aerosol number. CCN closure was assessed using measured size distributions, bulk chemical composition measurements, and assumed aerosol mixing states; CCN predictions tended toward overprediction, with the best agreement (± 0–20 %) obtained by assuming the aerosol to be externally-mixed with soluble organics. Closure also varied with CCN concentration, and the best agreement was found for CCN concentrations above 100 cm−3 with a 1.5- to 3-fold overprediction at lower concentrations.
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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 | 0.000 | 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) | 0.000 | 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".