Source attribution of Arctic black carbon constrained by aircraft and surfacemeasurements
Bibliographic record
Abstract
Abstract. Black carbon (BC) contributes to both degraded air quality and Arctic warming, however sources of Arctic BC and their geographic contributions remain uncertain. We interpret a series of recent airborne and ground-based measurements with the GEOS-Chem global chemical transport model and its adjoint to attribute the sources of Arctic BC. The springtime airborne measurements performed by the NETCARE campaign in 2015 and the PAMARCMiP campaigns in 2009 and 2011 offer BC vertical profiles extending to > 6 km across the Arctic and include profiles above Arctic ground monitoring stations. Long-term ground-based measurements are examined from multiple methods (thermal, laser incandescence and light absorption) at Alert (2011–2013), Barrow (2009–2015) and Ny-Ålesund (2009–2014) stations. Our simulations with the addition of gas flaring emissions are consistent with ground-based measurements of BC concentrations at Alert and Barrow in winter and spring (rRMSE < 13 %), and with airborne measurements of BC vertical profile across the Arctic (rRMSE = 17 %). Sensitivity simulations suggest that anthropogenic emissions in eastern and southern Asia are the largest source of the Arctic BC column both in spring (56 %) and annually (37 %), with larger contributions aloft than near the surface (e.g. a contribution of 66 % between 400–700 hPa and of 46 % below 900 hPa in spring). Anthropogenic emissions from northern Asia contribute considerable BC to the lower troposphere (a contribution of 27 % in spring and of 43 % annually below 900 hPa). Biomass burning has a substantial contribution to Arctic BC below 400 hPa of 25 % annually, despite minor influence in spring ( 50 %) in winter and those from eastern and southern Asia are the largest in spring (~ 40 %). At Ny-Ålesund, anthropogenic emissions from Europe (~ 30 %) and northern Asia (~ 30 %) are major sources in winter and early spring. Biomass burning from North America is the most important contributor to surface BC at all stations in summer, especially at Barrow where North American biomass burning contributes more than 90 % of BC in July and August. Our adjoint simulations indicate pronounced spatial and seasonal heterogeneity in the contribution of emissions to the Arctic BC column concentrations with noteworthy contributions from emissions in eastern China (15 %) and western Siberia (6.5 %). Although uncertain, gas flaring emissions from oilfields in western Siberia could have a striking impact (13 %) on Arctic BC loadings in January, comparable to the total influence of continental Europe and North America (6.5 % each in January).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".