Fifteen Years of HFC‐134a Satellite Observations: Comparisons With SLIMCAT Calculations
Bibliographic record
Abstract
Abstract The phase out of anthropogenic ozone‐depleting substances such as chlorofluorocarbons under the terms of the Montreal Protocol led to the development and worldwide use of hydrofluorocarbons (HFCs) in refrigeration, air conditioning, and as blowing agents and propellants. Consequently, over recent years, the atmospheric abundances of HFCs have dramatically increased. HFCs are powerful greenhouse gases and are now controlled under the terms of the 2016 Kigali Amendment to the Montreal Protocol. HFC‐134a is currently the most abundant HFC in the atmosphere, breaking the 100 ppt barrier in 2018, and can be measured in the Earth's atmosphere by the satellite remote‐sensing instrument ACE‐FTS (Atmospheric Chemistry Experiment‐Fourier Transform Spectrometer), which has been measuring since 2004. This work uses the ACE‐FTS v4.0 data product to investigate global distributions and trends of HFC‐134a. These measurements are compared with a simulation of SLIMCAT, a state‐of‐the‐art three‐dimensional chemical transport model, which is constrained by global surface HFC‐134a measurements. The agreement between observation and model is good, although in the tropical troposphere ACE‐FTS measurements are biased low by up to 10–15 ppt. The overall ACE‐FTS global trend of HFC‐134a for the altitude range 5.5–24.5 km and 2004–2018 time period is approximately linear with a value of 4.49 ± 0.02 ppt/year, slightly lower than the corresponding SLIMCAT trend of 4.66 ppt/year. Using a simple box model, we also estimate the annual global emissions and burdens of HFC‐134a from the model data, indicating that emissions of HFC‐134a have increased almost linearly, reaching 236 Gg by 2018.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".