Kernel-Based Estimation of Stratospheric Aerosol Radiative Effects from Volcanic and Wildfire Events
Bibliographic record
Abstract
To facilitate the quantification of the stratospheric aerosol direct radiative effect (ADRE), this study develops a suite of aerosol kernels based on MERRA-2 reanalysis data. The kernels comprise a five-dimensional dataset that includes latitude, longitude, time, wavelength, and radiative forcing scenarios. They quantify the sensitivity of top-of-atmosphere (TOA) radiative fluxes to changes in stratospheric aerosol optical depth (AOD), and distinguish between scattering and absorbing aerosols. Band-by-band radiative kernels are developed to capture the spectral dependence of ADRE while adjusted kernels account for stratospheric temperature responses. Additionally, we introduce an analytical kernel which enables an estimation of broadband radiative kernel values given such boundary conditions asß TOA insolation, reflectance, and stratospheric AOD. Applying these kernels, we estimate the ADREs of the 2022 Hunga volcanic eruption and the 2020 Australian wildfire. The Hunga eruption resulted in a global mean cooling effect of approximately -0.4 W/m² throughout 2022. In contrast, the Australian wildfire induced a global mean instantaneous ADRE of +0.27 W/m² and a stratosphere-adjusted ADRE of +0.11 W/m². Validation against radiative transfer model calculations confirms the accuracy of our kernel-based estimates. Our findings underscore the significance of spectral dependencies in stratospheric ADRE and highlight the distinct radiative sensitivities of stratospheric aerosols compared to their tropospheric counterparts. The developed radiative kernels offer an efficient and versatile tool for assessing the climatic impacts of stratospheric 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".