Evaluation of the ECHAM family radiation codes performance in the representation of the solar signal
Bibliographic record
Abstract
Abstract. Solar radiation is the main source of energy for the Earth's atmosphere and in many respects defines its composition, photochemistry, temperature profile and dynamics. The magnitude of the solar irradiance variability strongly depends on the wavelength making difficult its representation in climate models. Due to some deficiencies of the applied radiation codes several models fail to show a clear response in middle stratospheric heating rates to solar spectral irradiance variability, therefore it is important to prove reasonable model performance in this respect before doing multiple model runs. In this work we evaluate the performance of three generations of ECHAM (4, 5 and 6) radiation schemes by comparison with the reference high resolution libRadtran code. We found that both original ECHAM5 and 6 solar radiation codes miss almost all solar signal in the heating rates in the mesosphere. In the stratosphere ECHAM5 code reproduces only about a half of the reference signal, while representation of ECHAM6 code is better – it maximally misses about 17% in the upper stratosphere. On the basis of the comparison results we suggest necessary improvements of the ECHAM family codes by inclusion of available parameterizations of the heating rate due to absorption by oxygen (O2) and ozone (O3). Both codes with the introduced parameterizations represent the heating rate response to the spectral solar irradiance variability simulated with libRadtran much better without substantial increase of computer time. The suggested parameterizations are recommended to apply in the middle atmosphere version of the ECHAM-5 and 6 models for the study of the solar irradiance influence on climate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".