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Record W4396917556 · doi:10.1029/2024gl108494

Interpreting the Seasonality of Atmospheric Methane

2024· article· en· W4396917556 on OpenAlexaff
James D. East, Daniel J. Jacob, Nicholas Balasus, A. Anthony Bloom, Lori Bruhwiler, Zichong Chen, Jed O. Kaplan, Loretta J. Mickley, Todd A. Mooring, Elise Penn, Benjamin Poulter, Melissa P. Sulprizio, John R. Worden, Robert M. Yantosca, Zhen Zhang

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSeasonalityMethaneAtmospheric methaneEnvironmental scienceClimatologyAtmospheric sciencesMeteorologyGeographyGeologyChemistryStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract Surface and satellite observations of atmospheric methane show smooth seasonal behavior in the Southern Hemisphere driven by loss from the hydroxyl (OH) radical. However, observations in the Northern Hemisphere show a sharp mid‐summer increase that is asymmetric with the Southern Hemisphere and not captured by the default configuration of the GEOS‐Chem chemical transport model. Using an ensemble of 22 OH model estimates and 24 wetland emission inventories in GEOS‐Chem, we show that the magnitude, latitudinal distribution, and seasonality of Northern Hemisphere wetland emissions are critical for reproducing the observed seasonality of methane in that hemisphere, with the interhemispheric OH ratio playing a lesser role. Reproducing the observed seasonality requires a wetland emission inventory with ∼80 Tg a −1 poleward of 10°N including significant emissions in South Asia, and an August peak in boreal emissions persisting into autumn. In our 24‐member wetland emission ensemble, only the LPJ‐wsl MERRA‐2 inventory has these attributes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.295
Teacher spread0.278 · 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

Citations26
Published2024
Admission routes1
Has abstractyes

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