Overlap of Solar and Infrared Spectra and the Shortwave Radiative Effect of Methane
Bibliographic record
Abstract
Abstract This paper focuses on two shortcomings of radiative transfer codes commonly used in climate models. The first aspect concerns the partitioning of solar versus infrared spectral energy. In most climate models, the solar spectrum comprises wavelengths less than 4 μm with all incoming solar energy deposited in that range. In reality, however, the solar spectrum extends into the infrared, with about 12 W m−2 in the 4–1000-μm range. In this paper a simple method is proposed wherein the longwave radiative transfer equation with solar energy input is solved. In comparison with the traditional method, the new solution results in more solar energy absorbed in the atmosphere and less at the surface. As mentioned in a recent intercomparison of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) and line-by-line (LBL) radiation models, most climate model radiation schemes neglect shortwave absorption by methane. However, the shortwave radiative forcing at the surface due to CH4 since the preindustrial period is estimated to exceed that due to CO2. The authors show that the CH4 shortwave effect can be included in a correlated k-distribution model, with the additional flux being accurately simulated in comparison with LBL models. Ten-year GCM simulations are presented, showing the detailed climatic effect of these changes in radiation treatment. It is demonstrated that the inclusion of solar flux in the infrared range produces a significant amount of extra warming in the atmosphere, specifically (i) in the tropical stratosphere where the warming can exceed 1 K day−1, and (ii) near the tropical tropopause layer. Additional GCM simulations show that inclusion of CH4 in the shortwave calculations also produces a warming of the atmosphere and a consequent reduction of the upward flux at the top of the atmosphere.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".