Exploring stratospheric aerosol radiative forcing using the SASKTRAN radiative transfer framework
Bibliographic record
Abstract
The radiative forcing associated with stratospheric aerosol is often diagnosed using coupled general circulation models. The radiation codes within such models are state-of-the-art, but contain simplifications in order to optimize computational efficiency and make it feasible to perform simulations on climate-relevant time scales. Calculating radiative forcing using different radiative transfer techniques is useful to validate results from GCMs, and explore sensitivities to parameters that are not easily modified in such models. Here, we report on progress toward quantifying global stratospheric aerosol radiative forcing using the SASKTRAN radiative transfer framework, which has a rich heritage in the context of the retrieval of aerosol and gas species from limb scattered radiation. SASKTRAN is coupled to the Easy Volcanic Aerosol (EVA) forcing generator, allowing for realistic but adjustable stratospheric aerosol properties in global or single column radiative transfer calculations. Simulations are used to assess the impact of multiple scattering on the global radiative forcing, and its dependence on location and aerosol perturbation magnitude. We also assess the impact of using the simplified scattering parameters used as input to most GCMs (extinction, single scattering albedo and asymmetry factor) compared to using the full Mie scattering phase function computed from a given aerosol size distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".