MétaCan
Menu
Back to cohort
Record W2752568105

Applications of Adjoint Modelling in Chemical Composition: Studies of Tropospheric Ozone at Middle and High Northern Latitudes

2014· dissertation· en· W2752568105 on OpenAlexfundaboutno aff
Thomas Walker

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersCanadian Space AgencyCentre National de la Recherche ScientifiqueCentre National d’Etudes SpatialesNatural Sciences and Engineering Research Council of CanadaEuropean CommissionCanadian Foundation for Climate and Atmospheric SciencesUniversity of TorontoStrong
KeywordsTropospheric ozoneTroposphereOzoneAtmospheric sciencesClimatologyLatitudeMiddle latitudesEnvironmental scienceMeteorologyAtmospheric compositionChemical compositionHigh latitudeGeographyChemistryGeologyAtmosphere (unit)Organic chemistryGeodesy
DOInot available

Abstract

fetched live from OpenAlex

Ozone is integral to tropospheric chemistry, and understanding the processes controlling its distribution is important in climate and air pollution contexts. The GEOS-Chem global chemical transport model and its adjoint are used to interpret the impacts of midlatitude precursor emissions and atmospheric transport on the tropospheric ozone distribution at middle and high northern latitudes. In the Arctic, the model reproduces seasonal cycles of peroxyacetyl nitrate (PAN) and ozone measured at the surface, and observed ozone abundances in the summer free troposphere. Source attribution analysis suggests that local photochemical production, ≤ 0.25 ppbv/day, driven by PAN decomposition accounts for more than 50% of ozone in the summertime Arctic boundary layer. In the mid-troposphere, photochemical production accounts for 30-40% of ozone, while ozone transported from midlatitudes contributes 25-35%. Adjoint sensitivity studies link summertime ozone production to anthropogenic, biomass burning, soil, and lightning emissions between 50N-70N. Over Alert, Nunavut, the sensitivity of mid-tropospheric ozone to lightning emissions sometimes exceeds that to anthropogenic emissions. Over the eastern U.S., numerous models overestimate ozone in the summertime boundary layer. An inversion analysis, using the GEOS-Chem four-dimensional variational data assimilation system, optimizes emissions of NOx and isoprene. Inversion results suggest the model bias cannot be explained by discrepancies in these precursor emissions. A separate inversion optimizes rates of key chemical reactions including ozone deposition rates, which are parameterized and particularly uncertain. The inversion suggests a factor of 2-3 increase in deposition rates in the northeastern U.S., decreasing the ozone bias from 17.5 ppbv to 6.0 ppbv. This analysis, however, is sensitive to the model boundary layer mixing scheme. Several inversion analyses are conducted to estimate lightning NOx emissions over North America in August 2006, using ozonesonde data. The high-resolution nested version of GEOS-Chem is used to better capture variability in the ozonesonde data. The analyses suggest North American lightning NOx totals between 0.076-0.204 Tg N. A major challenge is that the vertical distribution of the lightning source is not optimized, but the results suggest a bias in the vertical distribution. Reliably optimizing the three-dimensional distribution of lightning NOx emissions requires more information than the ozonesonde dataset contains.

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)0.001
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: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)Same topicAtmospheric chemistry and aerosolsFrench-language works237,207