Distributions and Trends of the Aerosol Direct Radiative Effect in the 21st Century: Aerosol and Environmental Contributions
Bibliographic record
Abstract
Abstract The aerosol direct radiative effect (ADRE) is controlled by both aerosol distributions and environmental factors, making it interesting and important to quantitatively assess their effects on the ADRE inhomogeneity and climate trends. By analyzing the ADRE in the 21st century from a global reanalysis data set, we find that the spatial variability of the ADRE and its trends can be well explained by a linear regression model. In this model, scattering and absorbing aerosol optical depths (AODs) are used, along with critical environmental variables such as surface albedo and cloud radiative effect, as predictors. Based on this model, we find that approximately 70% of the ADRE inhomogeneity is due to the AOD distributions and the remainder is attributable to environmental factors. This study also shows that a stronger cooling effect of the scattering aerosols in the Northern Hemisphere drives northward cross‐equator meridional energy transport, although this transport exhibits a declining trend over the last two decades. The changes in surface albedo and cloud radiative effect strongly influence the trends in the regional ADRE and the meridional energy transport driven by them. In particular, the reduction of surface albedo (sea ice) is primarily responsible for the enhancement of the cooling ADRE, as well as an associated trend in meridional energy transport, in the Arctic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".