Radiative sensitivity quantified by a new set of radiation flux kernels based on the ERA5 reanalysis
Bibliographic record
Abstract
Abstract. Radiative sensitivity, i.e., the response of the radiative flux to climate perturbations, is essential to understanding climate variability. The sensitivity kernels computed by radiative transfer models have been broadly used for assessing the climate forcing and feedbacks in global warming. As these assessments are largely focused on the top of atmosphere (TOA) radiation budget, less attention has been paid to the surface radiation budget or the associated surface radiative sensitivity kernels. Based on the fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis, we produce a new set of radiative kernels for both the TOA and surface radiative fluxes, which is made available at http://doi.org/10.17632/vmg3s67568.1 (H. Huang, 2022). By comparing with other published radiative kernels, we find that the TOA kernels are in agreement in terms of global mean radiative sensitivity and analyzed overall feedback strength. The unexplained residual in the radiation closure tests is found to be generally within 10 %, no matter which kernel dataset is used. The inter-kernel bias-induced uncertainty, as measured by the standard deviation of the global mean feedback parameter value, is typically no more than 10 % in the longwave and 20 % in the shortwave; this uncertainty is much smaller than the inter-climate model spread of the feedbacks. However, there exist more significant regional biases in kernel values, due to the dependence of radiative sensitivity on the atmospheric states, and this contributes to more significant radiation non-closure at the regional scale, such as in the Arctic and Southern Ocean regions. On the other hand, we find relatively larger discrepancies in the surface kernels. Although several kernels can achieve as good radiation closure compared to the TOA kernels, affirming the validity of kernel method for the surface radiation budget analysis, the non-closure residual in certain kernels may amount to over 100 % of the total radiation change. The intercomparison of the surface kernels reveals important biases, such as in the radiative sensitivity to air temperature in the lowermost atmospheric layers adjacent to the surface, which is of critical importance to the overall surface feedback strength.
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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 | 0.002 | 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 | 0.002 |
| 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".