Diagnosing atmospheric heating rate changes using radiative kernels
Bibliographic record
Abstract
Atmospheric radiative heating rate, which manifests radiative energy convergence in the atmosphere, is a fundamental factor shaping the Earth's climate and driving climate change.Compared to the radiative energy budget at the top of atmosphere (TOA) or surface, the atmospheric energy budget and heating rate are less studied and understood due to a lack of observational constraints and of diagnostic tools.Motivated by growing interest in atmospheric energy budget and particularly to facilitate the analysis of atmospheric heating rate, we innovate a set of radiative kernels, which quantitatively measure the sensitivity of atmospheric heating rate to different geophysical variables.When multiplied with the changes in these geophysical variables, these kernels can quantify the contributions of them to the heating rate change.A climate change experiment of Global Climate Models (GCMs) is used to test the application of the heating rate kernels.The results indicate that the radiative heating rate change simulated by the GCMs can be well reproduced by the kernels, which affirms the validity of the kernel method.The decomposition of the heating rate changes reveals rich information of the contributing mechanisms behind the changes.For example, in the tropical upper troposphere, the noticeably enhanced radiative cooling in a warmer climate is found to be dominated by atmospheric temperature and water vapor.Both of them increase the thermal radiation of the atmosphere, and are partially offset by a warming effect of the lifting high-cloud tops in this region.Moreover, we find that compared to the results corrected using the kernels, the cloud effect inferred from the radiative heating difference between clear-and all-skies (using the quantity termed "cloud radiative effect") has a non-negligible bias, which necessitates the use of kernels to quantify the cloud-induced heating rate changes.
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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 | 0.002 |
| 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) | 0.001 | 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".