Quantifying the Direct Radiative Effect of Stratospheric Aerosols Using Radiative Kernels
Bibliographic record
Abstract
To facilitate the quantification of the stratospheric aerosol radiative effect, this study generates a set of aerosol direct radiative effect (ADRE) kernels based on MERRA-2 reanalysis data. These radiative kernels measure the sensitivities of ADRE to perturbations in scattering and absorbing aerosol optical depth (AOD), respectively. Both broadband and band-by-band radiative kernels are developed to account for the wavelength dependency of ADRE. The broadband kernels are then emulated by a multivariate regression model, which predicts the kernel values from a handful of predictors, including the top-of-atmosphere (TOA) insolation, TOA reflectance, and stratospheric AOD. These kernels offer an efficient and versatile way to assess the ADRE of stratospheric aerosols. The ADREs of the 2022 Hunga volcano eruption and the 2020 Australia wildfire are estimated from the kernels and validated against radiative transfer model-calculated results. The Hunga eruption induced a global mean cooling forcing of -0.46 W/m² throughout 2022, while the Australia wildfire caused a warming forcing of +0.28 W/m² from January to August. The kernel estimation can capture over 90% of the ADRE variance with relative error within 10%, in these assessments. The results demonstrate the spectral dependencies of stratospheric ADRE and highlight the distinct radiative sensitivity of stratospheric aerosols, which differs significantly from that of tropospheric aerosols.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".