Earth Infrared Radiation Spectra During Global Warming Hiatus
Bibliographic record
Abstract
Background: Since 1997-98, observations of annual mean surface temperature have shown a slowdown of global temperature increases, suggesting a hiatus in global warming. Given this finding, we are interested in diagnosing trends in the Earth’s outgoing longwave radiation (OLR) spectrum throughout the last decade. Methods: We calculated the trend in OLR measured by the Atmospheric Infrared Sounder (AIRS) aboard NASA’s Aqua satellite between 2003 and 2013, and compared these results with the trend in atmospheric and surface temperature and tropospheric absolute humidity, obtained from AIRS retrieval product and from the ECMWF (European Center for Medium range Weather Forecasting) Re-Analysis (ERA) interim product. We also isolated the greenhouse effect from the OLR trend by subtracting the amount of surface radiation emitted from the total radiation received by the sounder. Results: The OLR trend is negative in the CO2 absorption band, negative in the window spectral region, and positive in the water vapor band. The trend in surface and tropospheric temperature is negative, as is the trend in tropospheric absolute humidity. The greenhouse effect is increasing in the CO2 band, generally slightly increasing in the window region, and decreasing in the H2O band. Conclusion: Our results show that the CO2 forcing was still present globally through the last decade, with steadily increasing effects. Contributors to the negative trend in OLR in the window region are a small decrease in surface temperature and a strong decrease in tropospheric temperature, where tropospheric H2O emit radiation to space. The decreasing effect of water vapor in the H2O band is due to decreasing tropospheric humidity. This analysis will allow us to detect the changes in greenhouse gas forcing, to examine the correlated surface temperature response, and to study changes and effects in tropospheric water vapor concentration.
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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) | 0.002 | 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 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".