Measurements of CFC-11, CFC-12, and HCFC-22 total columns in the atmosphere at the St. Petersburg site in 2009–2019
Bibliographic record
Abstract
Abstract. Monitoring atmospheric anthropogenic halocarbons plays an important role in tracking their atmospheric concentrations in accordance with international agreements on emissions of ozone-depleting substances and, thus, in estimating the ozone layer recovery. Within the Network for the Detection of Atmospheric Composition Change (NDACC), regular Fourier transform infrared (FTIR) measurements can provide information on the abundancies of halocarbons on a global scale. We improved retrieval strategies for deriving the CFC-11 (CCl3F), CFC-12 (CCl2F2), and HCFC-22 (CHClF2) atmospheric columns from IR solar radiation spectra measured by the Bruker IFS125HR spectrometer at the St. Petersburg site (Russia). We used the Tikhonov–Phillips regularization approach for solving the inverse problem with optimized values of regularization parameters. We tested the strategies developed by comparison of the FTIR measurements with independent data. The analysis of the time series of column-averaged dry air mole fractions (Xgas) measured in 2009–2019 gives mean values of 225 pptv (parts per trillion by volume; CFC-11), 493 pptv (CFC-12), and 238 pptv (HCFC-22). Trend values total −0.40 % yr−1 (CFC-11), −0.49 % yr−1 (CFC-12), and 2.12 % yr−1 (HCFC-22). We compared the means, trends, and seasonal variability in XCFC-11, XCFC-12, and XHCFC-22 to that of (1) near-ground volume mixing ratios (VMRs), measured at the observational site Mace Head, Ireland (GVMR), (2) the mean in the 8–12 km layer VMRs, measured by ACE-FTS and averaged over 55–65∘ N latitudes (SVMR), and (3) Xgas values of the Whole Atmosphere Community Climate Model (WACCM) for the St. Petersburg site (WXgas). In general, the comparison of Xgas with the independent data showed a good agreement of their means within the systematic errors of the measurements considered. The trends observed over the St. Petersburg site demonstrate the smaller decrease rates for XCFC-11 and XCFC-12 than that of the independent data and the same increase rate for XHCFC-22. As a whole, Xgas, SVMR, and WXgas showed qualitatively similar seasonal variations, while the GVMR variability is significantly less, and only the WXHCFC-22 variations are essentially smaller than that of XHCFC-22 and SVMRHCFC-22.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".