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Record W2097506650 · doi:10.5194/acp-15-11477-2015

Transport pathways of peroxyacetyl nitrate in the upper troposphere and lower stratosphere from different monsoon systems during the summer monsoon season

2015· article· en· W2097506650 on OpenAlexaff
Suvarna Fadnavis, K. Semeniuk, Martin G. Schultz, Michael Kiefer, Anoop S. Mahajan, Luca Pozzoli, S. Sonbawane

Bibliographic record

VenueAtmospheric chemistry and physics · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsYork University
FundersIndian Institute of Technology Madras
KeywordsStratosphereTropospherePeroxyacetyl nitrateClimatologyMonsoonAtmospheric sciencesEast Asian MonsoonEnvironmental scienceExtratropical cycloneChemical transport modelTrace gasNOxGeologyChemistryCombustion

Abstract

fetched live from OpenAlex

Abstract. The Asian summer monsoon involves complex transport patterns with large-scale redistribution of trace gases in the upper troposphere and lower stratosphere (UTLS). We employ the global chemistry–climate model ECHAM5–HAMMOZ in order to evaluate the transport pathways and the contributions of nitrogen oxide species peroxyacetyl nitrate (PAN), NOx and HNO3 from various monsoon regions, to the UTLS over southern Asia and vice versa. Simulated long-term seasonal mean mixing ratios are compared with trace gas retrievals from the Michelson Interferometer for Passive Atmospheric Sounding aboard ENVISAT(MIPAS-E) and aircraft campaigns during the monsoon season (June–September) in order to evaluate the model's ability to reproduce these transport patterns. The model simulations show that there are three regions which contribute substantial pollution to the South Asian UTLS: the Asian summer monsoon (ASM), the North American monsoon (NAM) and the West African monsoon (WAM). However, penetration due to ASM convection reaches deeper into the UTLS compared to NAM and WAM outflow. The circulation in all three monsoon regions distributes PAN into the tropical latitude belt in the upper troposphere (UT). Remote transport also occurs in the extratropical UT where westerly winds drive North American and European pollutants eastward where they can become part of the ASM convection and lifted into the lower stratosphere. In the lower stratosphere the injected pollutants are transported westward by easterly winds. Sensitivity experiments with ECHAM5–HAMMOZ for simultaneous NOx and non-methane volatile organic compounds (NMVOCs) emission change (−10 %) over ASM, NAM and WAM confirm similar transport. Our analysis shows that a 10 % change in Asian emissions transports ~ 5–30 ppt of PAN in the UTLS over Asia, ~ 1–10 ppt of PAN in the UTLS of northern subtropics and mid-latitudes, ~ 7–10 ppt of HNO3 and ~ 1–2 ppb of ozone in UT over Asia. Comparison of emission change over Asia, North America and Africa shows that the highest transport of HNO3 and ozone occurs in the UT over Asia and least over Africa. The intense convective activity in the monsoon regions is associated with lightning and thereby the formation of additional NOx. This also affects the distribution of PAN in the UTLS. Simulations with and without lightning show an increase in the concentrations of PAN (~ 40 %), HNO3 (75 %), NOx (70 %) and ozone (30 %) over the regions of convective transport. Lightning-induced production of these species is higher over equatorial Africa and America compared to the ASM region. This indicates that the contribution of anthropogenic emissions to PAN in the UTLS over the ASM is higher than that of lightning.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.013
GPT teacher head0.187
Teacher spread0.174 · 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

Citations39
Published2015
Admission routes1
Has abstractyes

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