Unexpected land-surface warming following a low-to-moderate forcing hypothetical nuclear war
Bibliographic record
Abstract
Abstract. Nuclear conflicts could ignite intense urban fires that inject considerable amounts of black carbon (BC) into the upper atmosphere, with the potential to disrupt global climate. While uncertainties in the total BC injection remain large, relatively few modeling studies and limited model diversity have explored the climatic response to low-to-moderate BC injections, leaving key aspects of their climate impact poorly understood. Here, we investigate the climate response to a set of low-to-moderate forcing scenarios (12 to 24 Tg BC) – roughly one-tenth to one-fifth the strength of the standard high-end cases – using the Canadian Earth System Model version 5. Consistent with previous work, we find prolonged global reductions in surface temperature and precipitation, driven by decreased downwelling shortwave radiation at the surface and increased atmospheric stability. Unexpectedly, however, a transient surface warming develops in the first boreal summer following a boreal-winter injection, linked to reduced net longwave and turbulent fluxes. Precipitation remains suppressed because of enhanced stability. The transient warming is most pronounced for the lowest forcing cases, indicating a nonlinear response across the forcing range. These results underscore the need for broader multi-model assessments and systematic exploration across a wider range of scenarios, given their potential for complex, societally relevant outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".