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Record W3024565231 · doi:10.5194/acp-20-13591-2020

Impacts of atmospheric transport and biomass burning on the inter-annual variation in black carbon aerosols over the Tibetan Plateau

2020· article· en· W3024565231 on OpenAlexaff
Han Han, Yue Wu, Jane Liu, Tianliang Zhao, Bingliang Zhuang, Honglei Wang, Yichen Li, Huimin Chen, Ye Zhu, Hongnian Liu, Qin’geng Wang, Shu Li, Tijian Wang, Min Xie, Mengmeng Li

Bibliographic record

VenueAtmospheric chemistry and physics · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaEuropean Space AgencyNational Oceanic and Atmospheric AdministrationMinistry of Science and Technology of the People's Republic of ChinaHarvard University
KeywordsHYSPLITPlateau (mathematics)Environmental scienceEast AsiaClimatologyChemical transport modelAtmospheric sciencesSeasonalityLagrangianAerosolMonsoonBiomass burningAtmospheric circulationGeographyChinaMeteorologyGeologyEcology

Abstract

fetched live from OpenAlex

Atmospheric black carbon (BC) in the Tibetan Plateau (TP) can largely impact regional and global climate. Still, studies on the inter-annual 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 surface BC concentrations over the TP vary largely in space and by season, reflecting complicated interplays of BC sources from different origins. Of all areas in the TP, surface BC concentrations are highest over the eastern and southern TP, where surface BC is susceptible to BC transport from East Asia and South Asia, respectively. Applying a backward-trajectory method that combines BC concentrations from GEOS-Chem and trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we assess the contributions of worldwide source regions to surface BC in the TP. We estimate that on the 20-year average, 77 % of surface BC in the TP comes from South Asia (43 %) and East Asia (35 %). Regarding seasonal variation in non-local influences, South Asia and East Asia are dominant source regions in winter and summer, respectively, in terms of the amount of BC imported. However, in terms of affected areas in the TP, South Asia is the dominant contributor throughout the year. Inter-annually, surface BC over the TP is largely modulated by atmospheric transport of BC from non-local 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 inter-annual variation in the amount of BC transported to the TP from non-local regions. In summer, a stronger East Asian summer monsoon and a stronger South Asian summer monsoon tend to, respectively, lead to more BC transport from central China and north-eastern South Asia to the TP. In winter, BC transport from central China is enhanced in years with a strong East Asian winter monsoon or a strong Siberian High. A stronger Siberian High can also bring more BC from northern South Asia to the TP. This study underscores the impacts of atmospheric transport and biomass burning on the inter-annual variation in surface BC over the TP. It reveals a close connection between the Asian monsoon and atmospheric transport of BC from non-local regions to the TP.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.194
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2020
Admission routes1
Has abstractyes

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