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Record W2015111192 · doi:10.5194/acp-6-2273-2006

Interactive chemistry in the Laboratoire de Météorologie Dynamique general circulation model: model description and impact analysis of biogenic hydrocarbons on tropospheric chemistry

2006· article· en· W2015111192 on OpenAlexfundno aff
Gerd Folberth, Didier Hauglustaine, Juliette Lathière, Fabien Brocheton

Bibliographic record

VenueAtmospheric chemistry and physics · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEuropean CommissionCanadian Foundation for Climate and Atmospheric Sciences
KeywordsIsopreneTroposphereTropospheric ozoneAtmospheric sciencesOzoneChemistryAtmospheric chemistryMethaneBiosphereClimatologyEnvironmental scienceEnvironmental chemistryOrganic chemistryGeology

Abstract

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Abstract. We present a description and evaluation of LMDz-INCA, a global three-dimensional chemistry-climate model, pertaining to its recently developed NMHC version. In this substantially extended version of the model a comprehensive representation of the photochemistry of non-methane hydrocarbons (NMHC) and volatile organic compounds (VOC) from biogenic, anthropogenic, and biomass-burning sources has been included. The tropospheric annual mean methane (9.2 years) and methylchloroform (5.5 years) chemical lifetimes are well within the range of previous modelling studies and are in excellent agreement with estimates established by means of global observations. The model provides a reasonable simulation of the horizontal and vertical distribution and seasonal cycle of CO and key non-methane VOC, such as acetone, methanol, and formaldehyde as compared to observational data from several ground stations and aircraft campaigns. LMDz-INCA in the NMHC version reproduces tropospheric ozone concentrations fairly well throughout most of the troposphere. The model is applied in several sensitivity studies of the biosphere-atmosphere photochemical feedback. The impact of surface emissions of isoprene, acetone, and methanol is studied. These experiments show a substantial impact of isoprene on tropospheric ozone and carbon monoxide concentrations revealing an increase in surface O3 and CO levels of up to 30 ppbv and 60 ppbv, respectively. Isoprene also appears to significantly impact the global OH distribution resulting in a decrease of the global mean tropospheric OH concentration by approximately 0.7×105 molecules cm-3 or roughly 8% and an increase in the global mean tropospheric methane lifetime by approximately seven months. A global mean ozone net radiative forcing due to the isoprene induced increase in the tropospheric ozone burden of 0.09 W m-2 is found. The key role of isoprene photooxidation in the global tropospheric redistribution of NOx is demonstrated. LMDz-INCA calculates an increase of PAN surface mixing ratios ranging from 75 to 750 pptv and 10 to 250 pptv during northern hemispheric summer and winter, respectively. Acetone and methanol are found to play a significant role in the upper troposphere/lower stratosphere (UT/LS) budget of peroxy radicals. Calculations with LMDz-INCA show an increase in HOx concentrations region of 8 to 15% and 10 to 15% due to methanol and acetone biogenic surface emissions, respectively. The model has been used to estimate the global tropospheric CO budget. A global CO source of 3019 Tg CO yr-1 is estimated. This source divides into a primary source of 1533 Tg CO yr-1 and secondary source of 1489 Tg CO yr-1 deriving from VOC photooxidation. Global VOC-to-CO conversion efficiencies of 90% for methane and between 20 and 45% for individual VOC are calculated by LMDz-INCA.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0630.011

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 head0.220
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations266
Published2006
Admission routes1
Has abstractyes

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