Improved FTIR retrieval strategy for HCFC-22 (CHClF <sub>2</sub> ), comparisons with in situ and satellite datasets with the support of models, and determination of its long-term trend above Jungfraujoch
Bibliographic record
Abstract
Hydrochlorofluorocarbons (HCFCs) are the first, but temporary, substitution products for the strong ozone-depleting chlorofluorocarbons (CFCs). HCFC consumption and production are currently regulated under the Montreal Protocol on Substances that Deplete the Ozone Layer and their emissions have started to stabilize or even decrease. As HCFC-22 (CHClF 2 ) is by far the most abundant HCFC in today's atmosphere, it is crucial to continue to monitor the evolution of its atmospheric concentration. In this study, we describe an improved HCFC-22 retrieval strategy from ground-based high-resolution Fourier transform infrared (FTIR) solar spectra recorded at the high-altitude scientific station of Jungfraujoch, the Swiss Alps, 3580 m a.m.s.l. (above mean sea level). This new strategy distinguishes tropospheric and lower-stratospheric partial columns. Comparisons with independent datasets, such as the Advanced Global Atmospheric Gases Experiment (AGAGE) and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), supported by models, such as the Belgian Assimilation System for Chemical ObErvation (BASCOE) and the Whole Atmosphere Community Climate Model (WACCM), demonstrate the validity of our tropospheric and lower-stratospheric long-term time series. A trend analysis on the datasets used here, now spanning 30 years, confirms the last decade's decline in the HCFC-22 growth rate. This updated retrieval strategy can be adapted for other ozone-depleting substances (ODSs), such as CFC-12. Measuring or retrieving ODS atmospheric concentrations is essential for scrutinizing the fulfilment of the globally ratified Montreal Protocol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".