Radiative forcing pattern and its change controlled by forcing agents and environmental factors
Bibliographic record
Abstract
<!--!introduction!--><b></b> The radiative forcing of greenhouse gases and aerosols has characteristic distribution patterns, which influence global and regional energy budgets and lead to profound climate responses. Because of the multivariable-function nature of radiative transfer, the forcing distribution is controlled by forcing agents, as well as environmental variables. In this work, we aim to 1) determine the pattern of the radiative forcing of greenhouse gases and aerosols based on accurate radiative transfer computations, and 2) to quantify the effects of both forcing agents and environmental factors on the distribution pattern based on analytical equations determined from statistical analysis. We find that the majority of the temporospatial variance of the greenhouse gas and aerosol forcing can be well explained by multivariate regression models. Based on the radiative sensitivities quantified from this approach, we then assess how the changes in the respective controlling factors lead to the changes, as well as the inter-climate model differences, in the forcing magnitude and distribution. A few interesting and important environmental effects are identified. For example, the change in surface albedo is found to strongly influence the trend in the regional aerosol forcing in the Arctic, as well as the meridional energy transport in this region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".