Carbon dioxide trends in the mesosphere and lower thermosphere
Bibliographic record
Abstract
Abstract We investigated trends of carbon dioxide (CO2) in the upper atmosphere, using data from the Atmosphere Chemistry Experiment Fourier Transform Spectrometer and from the Sounding of the Atmosphere using Broadband Emission Radiometry. Recent analyses of these measurements had indicated that CO2 above approximately 90 km appeared to be increasing about twice as fast as it was in the lower atmosphere. Models could not reproduce this differential CO2 trend, calculating instead that the proportional CO2 increase is approximately constant with altitude. We found three issues with the methodologies used to derive trends from CO2 profiles: the way that seasonal changes and sampling are accounted for in the analysis, referred to as deseasonalizing; the registration of profiles in pressure versus altitude coordinates; and data quality indicators. Each of these can have significant effects on the derivation of trends. We applied several deseasonalizing procedures, using both pressure and altitude coordinates, also used a time series fit without deseasonalizing, and applied data quality filters. The derived trends were approximately constant with pressure or altitude, about 5.5% per decade, consistent with lower atmosphere CO2 trends, and consistent with model calculations. We conclude that the difference between the trend of CO2 above the CO2 homopause and the trend in the lower, well‐mixed atmosphere is not statistically significant.
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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 | 0.001 |
| 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".