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Record W4414484120 · doi:10.5194/egusphere-2025-4080

Annual Growth Rates of Column-Averaged CO <sub>2</sub> Inferred from Total Carbon Column Observing Network (TCCON)

2025· preprint· en· W4414484120 on OpenAlexafffund
Nasrin Mostafavi Pak, Jonas Hachmeister, Markus Rettinger, Matthias Buschmann, Nicholas M. Deutscher, David Griffith, Laura T. Iraci, Xin Lan, Erin McGee, Isamu Morino, David F. Pollard, Coleen M. Roehl, Kimberly Strong, Rigel Kivi, P. O. Wennberg

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersJapan Aerospace Exploration AgencyNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyArmstrong Flight Research CenterJet Propulsion LaboratoryEnvironment and Climate Change CanadaMinistry of Business, Innovation and EmploymentNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyEurostarsStrong
KeywordsSouthern HemisphereNorthern HemisphereLatitudeGrowth rateMiddle latitudesCarbon cycleAtmosphere (unit)

Abstract

fetched live from OpenAlex

Abstract. Monitoring annual atmospheric CO2 growth rates is a key constraint on assessing the long-term effectiveness of emission reduction strategies. We analyzed annual growth rates of column-averaged dry-air mole fractions of CO2 (XCO2) using long-term data from 12 sites within the Total Carbon Column Observing Network (TCCON), spanning four regions: the Arctic, two Northern Hemisphere midlatitude bands (40–50° N and 30–40° N), and the Southern Hemisphere. While in situ ground-based measurements provide detailed records of near-surface CO2 concentrations, XCO2 reflects the column-averaged abundance across the entire atmosphere, offering a complementary perspective. We compared TCCON-derived growth rates with ground-based in situ observations from the Mauna Loa Observatory (MLO). Three calculation methods—Monthly Mean (MM), Fourier Fit residuals (FF), and Dynamic Linear Model (DLM)—were evaluated, with particular attention to the Eureka site, where polar night introduces substantial data gaps. In addition, the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis product was used to assess consistency with TCCON-based growth rates and to evaluate each method’s robustness to missing data. Among the methods tested, the DLM approach proved most resilient to data gaps. Regionally averaged CO2 growth rates, calculated from 2010 or from the earliest available data through 2024, ranged from approximately 2.33 to 2.40 ppm per year. The most prominent signal was associated with the 2015–2016 El Niño–Southern Oscillation (ENSO) event, during which growth rates increased by up to 1.7 ppm per year. The impact of COVID-19-related emission reductions in 2020 was also examined: a decline of 0.4 ppm per year was observed in the 30–40° N region, whereas other regions showed no significant decline. Correlation analysis between growth rates and ENSO strength revealed significant relationships in the Southern Hemisphere and at Mauna Loa, but not in northern mid- or high-latitude regions.

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 score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

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

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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