A trajectory analysis of atmospheric transport of black carbon aerosols to Canadian High Arctic in winter and spring (1990–2005)
Bibliographic record
Abstract
Abstract. Black carbon (BC) particles accumulated in the Arctic troposphere and deposited over snow have significant effects on radiative forcing of the Arctic regional climate. Applying cluster analysis technique on 10-day backward trajectories, transport pathways affecting Alert (82.5° N, 62.5° W), Nunavut in Canada are identified in this work, along with the associated transport frequency. Based on the atmospheric transport frequency and the estimated BC emission intensity from surrounding regions, a linear regression model is constructed to investigate the inter-annual variations of BC observed at Alert in January and April, representative of winter and spring respectively, between 1990 and 2005. Strong correlations are found between BC concentrations predicted with the regression model and measured at Alert for both seasons (R2 equals 0.77 and 0.81 for winter and spring, respectively). Results imply that atmospheric transport and BC emission are the major contributors to the inter-annual variations in BC concentrations observed at Alert in the cold seasons for the 16-year period. Based on the regression model the relative contributions of regional BC emissions affecting Alert are attributed to the Eurasian sector, composed of the European Union and the former USSR, and the North American sector. Considering both seasons, the model suggests that Eurasia is the major contributor to the near-surface BC levels at the Canadian High Arctic site with an average contribution of over 85% during the 16-year period. In winter, the atmospheric transport of BC aerosols from Eurasia is found to be even more predominant with a multi-year average of 94%. The model estimates smaller contribution from the Eurasian sector in spring (70%) than that in winter. It is also found that the change in Eurasian contributions depends mainly on the reduction of emission intensity, while the changes in both emission and atmospheric transport contributed to the inter-annual variation of North American contributions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".