Summertime pollution events in the Arctic and potential implications
Bibliographic record
Abstract
Arctic summertime aerosols are examined here on the basis of column integrated and surface aerosol measurements made at Barrow (North Slope of Alaska) between 1998 and 2003. Although the site generally exhibits low aerosol burden in the summer, events of high loadings occur 8 days per summer. During the pollution episodes, the potential source contribution function from Russia is dominant (being about 40%). The source locations in Russia are mainly situated in the central and eastern parts. South Asia, Europe and North America each contribute 6% to the observed high aerosol loading. Source locations in south Asia lie in northern China and northern Japan, while those in Europe lie mainly in northern U.K. and Estonia. The North American sources are situated in northern Canada and Alaska. Over the 6‐year period, 10 ± 4 days per summer season show elevated levels of surface aerosol absorption. The pollution events with the highest aerosol absorption appear to be associated with smoke from wild fires burning in northwest Canada. Diurnally averaged top of the atmosphere direct radiative forcing ΔFTOA (550 nm) at Barrow lie between −1.50 W m⁻² and 1.19 W m⁻² in summer with an annual mean of −0.53 ± 0.11 W m⁻². Given low Arctic summertime surface albedo (<30%), a positive ΔFTOA results when the single scattering albedo is 0.85 or lower. Summertime direct surface radiative forcing (550 nm) ranges between −3.2 W m⁻² and −29 W m⁻² for observed cases of aerosol optical depth at the site.
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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 | 0.001 |
| 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) | 0.001 | 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".