Impacts of atmospheric transport and biomass burning on the interannual variation in black carbon aerosols over the Tibetan Plateau
Bibliographic record
Abstract
Abstract. Atmospheric black carbon (BC) in the Tibetan Plateau (TP) can largely impact regional and global climate. Still, studies on the interannual variation in atmospheric BC over the TP and associated variation in BC sources and controlling factors are rather limited. In this study, we characterize the variations in atmospheric BC over the TP surface layer through analysis of 20-year (1995–2014) simulations from a global chemical transport model, GEOS-Chem. The results show that, of all areas in the TP, surface BC concentrations are highest over the eastern and southern TP, where surface BC are susceptible respectively to BC transport from East Asia and South Asia. Combining the GEOS-Chem simulations and trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we assess the contributions of different source regions to surface BC in the TP. On the 20-year average, over 90 % surface BC in the TP comes from South Asia (47 %) and East Asia (46 %). Regarding seasonal variation in foreign influences, South Asia and East Asia are dominant source regions in winter and summer, respectively, in terms of both magnitude and affected areas in the TP. In spring and autumn, the influences from the two source regions are somewhat comparable. Interannually, surface BC over the TP is largely modulated by atmospheric transport of BC from foreign regions year-round and by biomass burning in South Asia, mostly in spring. We find that the extremely strong biomass burning in South Asia in the spring of 1999 greatly enhanced surface BC concentrations in the TP (31 % relative to the climatology). We find that the strength of the Asian monsoon correlates significantly with the interannual variation in the amount of BC transported to the TP from foreign regions. In summer, strong East Asian summer monsoon and South Asian summer monsoon tend to, respectively, increase BC transport from central China and northeast South Asia to the TP. In winter, BC transport from central China is enhanced in years with strong East Asia winter monsoon or Siberian High. A strong Siberian High can also increase BC transport from northern South Asia to the TP. This study underscores the impacts of atmospheric transport and biomass burning on the interannual variation in surface BC over the TP. It reveals a close connection between the atmospheric transport of BC from foreign regions to the TP and the Asian monsoon.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".