Importance of deposition processes in simulating the seasonality of the Arctic black carbon aerosol
Bibliographic record
Abstract
Anthropogenic aerosol components in the Arctic troposphere, such as black carbon (BC), show a strong seasonal variation characterized by a peak in later winter and early spring. The seasonality, however, is not properly simulated by most existing global aerosol models. Using the Canadian global air quality model with an online aerosol algorithm‐Global Environmental Multiscale model with Air Quality processes (GEM‐AQ), this work investigates the mechanisms of the seasonal variation of the Arctic BC. Through enhancements to parameterizations of wet and dry depositions in the Canadian Aerosol Module (CAM), the GEM‐AQ model is able to simulate the observed seasonality of BC over the Arctic. The observed seasonality of Arctic BC is mainly attributed to the seasonal changes in aerosol wet scavenging. Seasonal injection of aerosols (e.g., BC from the European and the former USSR sectors and to a less extent from the North Atlantic sector) also contributes to the seasonality of Arctic aerosols in the lower troposphere. Although dry deposition has little effect on the seasonal pattern of BC in the Arctic lower troposphere, it significantly changes BC surface concentration in the Arctic. The enhanced model suggests an annual budget of BC deposition to the Arctic of 0.11 Tg, a 10% increase over the original estimation. The enhanced GEM‐AQ model also suggests that the below‐cloud scavenging dominates the contribution of BC removal over the Arctic with an estimation of 48% for 2001, whereas the contributions of in‐cloud scavenging and dry deposition contribute about 27% and 25%, respectively. The estimated global BC burden is 0.28 Tg, which implies a global average BC lifetime of 9.2 days, whereas the AeroCom project suggests a range of 4.9–11.4 days.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".