The spectrally and vertically decomposed outgoing longwave radiation and its climate trends in the far-infrared
Bibliographic record
Abstract
The outgoing longwave radiation (OLR) at the top of the atmosphere is a critical component of the Earth's radiation energy budget. A substantial portion of the OLR energy lies in the far-infrared (FIR) spectrum, which has not been directly measured for understanding weather and climate variations. Several satellite projects under development, including the Thin Ice Cloud in Far Infrared Experiment (TICFIRE, Blanchet et al. 2011) funded by the Canadian Space Agency, the Polar Radiant Energy in the Far Infrared Experiment (PREFIRE, L’Ecuyer et al., 2021) of U.S. NASA, and the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM, Palchetti et al., 2020) of the European Space Agency, are being developed to fill this observation gap. Given that the FIR observation data is not available yet, we use simulations to acquire prior knowledge of the climatological mean distribution of the OLR in FIR, by using a rapid radiative transfer model, RRTMG, to simulate spectrally decomposed OLR in different spectral bands from global instantaneous atmospheric profiles of the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5). Based on the radiative transfer equation, we dissect the OLR by attributing its distribution and variation to spectrally and vertically decomposed contributions of the atmosphere and Earth surface. Our results disclose that the relatively higher far-infrared fraction of the OLR in polar region is due to stronger surface contribution and identify a minimum atmospheric contribution layer around the tropopause. On the other hand, the variability of the spectrally decomposed OLR field is dissected with the aid of a new set of radiative sensitivity kernels. This analysis discovers that the non-cloud longwave climate feedback, as well as its inter-climate model discrepancy, mainly results from the upper tropospheric thermodynamic fields (temperature and water vapor) and their effects on the FIR radiation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".