16‐year simulation of Arctic black carbon: Transport, source contribution, and sensitivity analysis on deposition
Bibliographic record
Abstract
Arctic regional climate is influenced by the radiative impact of aerosol black carbon (BC) both in the atmosphere and deposited on the snow and ice covered surfaces. The NIES (National Institute for Environmental Studies) global atmospheric transport model was used, with BC emissions from mid‐latitude fossil fuel and biomass burning source regions, to simulate BC concentrations with 16 year period. The model‐simulated BC agreed well with the BC observations, including the trends and seasonality, at three Arctic sites: Alert (Nunavut, Canada), Barrow (Alaska, USA), and Zepplin, Ny‐Ålesund (Svalbard, Norway). The equivalent black carbon (EBC, absorption inferred BC) observations at the three Arctic locations showed an overall decline of 40% from 1990 to 2009; with most change occurring during early 1990s. Model simulations confirmed declining influence on near surface BC contribution by 70% , and atmospheric BC burden by one half from the Former Soviet Union (FSU) BC source region over 16 years. In contrast, the BC contribution from the East Asia (EA) region has little influence at the surface but atmospheric Arctic BC burden increased by 3 folds. Modelled dry deposition is dominant in the Arctic during wintertime, while wet deposition prevails at all latitudes during summer. Sensitivity analyses on the dry and wet deposition schemes indicate that parameterizations need to be refined to improve on the model performance. There are limitations in the model due to simplified parameterizations and remaining model uncertainties, which requires further exploration of source region contributions, especially from growing EA source region to Arctic BC levels in the future is warranted.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".