Variability in Biomass Burning Emissions Weakens Aerosol Forcing Due To Nonlinear Aerosol‐Cloud Interactions
Bibliographic record
Abstract
Abstract The magnitude of the aerosol forcing remains among the largest unknowns when assessing climate sensitivity over the historical period. Here, we quantify and explain a crucial but often overlooked source of uncertainty in aerosol forcing: the temporal variability of aerosol emissions. We show that time‐variability in biomass burning (BB) emissions weakens the time‐averaged total aerosol forcing, particularly in the Northern Hemisphere mid‐ to high‐latitudes. BB emissions variability produces weaker (less negative) mean effective radiative forcing (ERF) compared to scenarios with no interannual variability in emissions. Satellite‐estimated BB emissions (and associated variability) result in a June–September absolute ERF (relative to zero BB emissions) of −7.7 W m −2 from 50° to 70°N, compared to −10.4 W m −2 when no emissions variability is used in the Community Earth System Model version 2 (CESM2). This difference in forcing is attributable to nonlinear aerosol‐cloud interactions. Aerosol forcing will be overestimated (i.e., more negative) if emissions are temporally‐smoothed.
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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 | 0.000 | 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) | 0.001 | 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".