Bibliographic record
Abstract
Abstract. Near-Term Climate Forcers (NTCFs) play a crucial role in shaping Earth's climate, yet their effects are often overshadowed by long-lived greenhouse gases (GHGs) when addressing climate variability. This study explores the climatic impact of elevated non-methane NTCF concentrations from 1950 to 2014 using CMIP6-AerChemMIP simulations. We analyse data from four Earth System Models with interactive tropospheric chemistry and aerosol schemes, leveraging a twelve-member ensemble to ensure statistical robustness. Unlike single-species or idealised radiative forcing studies, our approach captures the combined effects of co-emitted NTCF species. Our results show that the negative radiative forcing of aerosols dominates the overall NTCF impact, offsetting the warming effects of absorbing aerosols and tropospheric ozone. Multi-model mean analyses reveal three key climate responses: (1) a global cooling, amplified in the Arctic, where autumn temperatures decrease by up to 5 °C, (2) a 38 % increase in Labrador Sea ocean convection, and (3) changes in tropical precipitation, including a 0.6° southward displacement of the Intertropical Convergence Zone (ITCZ). This research addresses the mechanisms driving these climatic changes and underscores the importance of incorporating interactive NTCFs in climate projections. As inferred from their historical impact, future NTCF reductions could amplify regional responses to increasing GHG concentrations, thus requiring more ambitious mitigation strategies.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.187 | 0.146 |
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".