MétaCan
Menu
Back to cohort
Record W2092299277 · doi:10.5194/acp-15-1521-2015

Sensitivity of top-down CO source estimates to the modeled vertical structure in atmospheric CO

2015· article· en· W2092299277 on OpenAlexafffund
Zhe Jiang, Dylan B. A. Jones, H. M. Worden, Daven K. Henze

Bibliographic record

VenueAtmospheric chemistry and physics · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersCanadian Space AgencyNational Oceanic and Atmospheric AdministrationNational Aeronautics and Space Administration
KeywordsTroposphereSubtropicsEnvironmental scienceAtmospheric sciencesClimatologyMixed layerIsopreneInversion (geology)Chemical transport modelGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract. We assessed the sensitivity of regional CO source estimates to the modeled vertical CO distribution by assimilating multi-spectral MOPITT (Measurements of Pollution In The Troposphere) V5J CO retrievals with the GEOS-Chem model. We compared the source estimates obtained by assimilating the CO profiles and the surface layer retrievals from June 2004 to May 2005. Because the surface layer retrievals are less sensitive to CO in the free troposphere, it is expected that they should provide constraints in the CO source estimates that are less sensitive to the vertical structure of CO in the free troposphere. The inferred source estimates all suggest a reduction in CO emissions in the tropics and subtropics, and an increase in the extratropics over the a priori estimates. The tropical decreases were particularly pronounced for regions where the biogenic source of CO was dominant, suggesting an overestimate of the a priori isoprene source of CO in the model. We found that the differences between the regional source estimates inferred from the profile and surface layer retrievals for 2004–2005 were small, generally less than 10% for the main continental regions, except for estimates for southern Asia, North America, and Europe. Because of discrepancies in convective transport in the model, the CO source estimates for India and southeastern Asia inferred from the CO profiles were significantly higher than those estimated from the surface layer retrievals during June–August 2004. On the other hand, the profile inversion underestimated the CO emissions from North America and Europe compared to the assimilation of the surface layer retrievals. We showed that vertical transport of air from the North American and European boundary layers is slower than from other continental regions, and thus air in the free troposphere from North America and Europe in the model is more chemically aged, which could explain the discrepancy between the source estimates inferred from the profile and surface layer retrievals. We also examined the impact of the OH distribution on the source estimates and found that the discrepancies between the source estimates obtained with two OH fields were larger when using the profile data, which is consistent with greater sensitivity to the more chemically aged air in the free troposphere. Our findings indicate that regional CO source estimates are sensitive to the vertical CO structure. They suggest that diagnostics to assess the age of air from the continental source regions should help interpret the results from CO source inversions. Our results also suggest that assimilating a broader range of composition measurements to provide better constraint on tropospheric OH and the biogenic sources of CO is essential for reliable quantification of the regional CO budget.

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)0.004
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.207
Teacher spread0.202 · 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

Citations45
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueAtmospheric chemistry and physicsSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207