Trends of Key Greenhouse Gases as Measured in 2009–2022 at the FTIR Station of St. Petersburg State University
Bibliographic record
Abstract
Key long-lived greenhouse gases (CO2, CH4, and N2O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO2, CH4, and N2O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO2, CH4, and N2O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr−1 for CO2; (0.46 ± 0.02) % yr−1 for CH4; (0.28 ± 0.01) % yr−1 for N2O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH4 and N2O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".