Sensitivity of Wintertime Arctic Black Carbon to Removal Processes and Regional Alaskan Sources
Bibliographic record
Abstract
Abstract Air pollutants are primarily transported from midlatitude emission regions in winter and early spring, leading to elevated concentrations of aerosols, including black carbon (BC), in the Arctic, a phenomenon known as Arctic haze. The Weather Research and Forecasting model coupled with chemistry is used to investigate potential causes of uncertainties in modeling Arctic BC for winter 2014. The model captures observed variability in BC at surface sites, reproducing BC concentrations at Zeppelin but showing a low bias at Tiksi, Alert, and Utqiaġvik/Barrow. The influence of removal processes on model BC biases is explored by switching off dry or wet deposition. Wet deposition, during transport in the North Atlantic storm track, and locally over Svalbard, dominates BC removal at Zeppelin, while wet removal in the Pacific storm track influences BC at Alert and Utqiaġvik/Barrow. Dry removal over Asian source regions and Alaska affects BC at Utqiaġvik/Barrow, and is larger than wet removal at Tiksi due to the proximity of local/regional anthropogenic sources. Regional runs over northern Alaska in late January show improved simulated BC compared to observations at Utqiaġvik/Barrow, in part, due to better resolution of removal processes, and local/regional emissions. Sensitivity runs also show that regional Alaskan sources, notably from the North Slope of Alaska oil fields, may be contributing 30%–50%, on average, to observed BC at Utqiaġvik/Barrow in January and February 2014, with the remainder from outside the region. These findings highlight the importance of local Arctic emissions, and the need for improved emission inventories in the Arctic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".