Anthropogenic Greenhouse Gas and Aerosol Contributions to Extreme Temperature Changes during 1951–2015
Bibliographic record
Abstract
Abstract This study conducted a detection and attribution analysis of the observed global and regional changes in extreme temperatures during 1951–2015. HadEX3 observations were compared with multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) using an optimal fingerprinting technique. Annual maximum daily maximum and minimum temperatures (TXx and TNx; warm extremes) and annual minimum daily maximum and minimum temperatures (TXn and TNn; cold extremes) over land were analyzed considering global, continental, and subcontinental scales. Response patterns (fingerprints) of extreme temperatures to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AA), and natural (NAT) forcings were obtained from CMIP6 forced simulations. The internal variability ranges were estimated from preindustrial control simulations. A two-signal detection analysis where the observations are regressed onto ANT and NAT fingerprints simultaneously reveals that ANT signals are robustly detected in separation from NAT over global and all continental domains (North and South America, Europe, Asia, and Oceania) for most of the extreme indices. ANT signals are also detected over many subcontinental regions, particularly for warm extremes (more than 60% of 33 subregions). A three-signal detection analysis that considers GHG, AA, and NAT fingerprints simultaneously demonstrates that GHG signals are detected in isolation from other external forcings over global, continental, and several subcontinental domains especially for warm extremes, explaining most of the observed warming. Moreover, AA influences are detected for warm extremes over Europe and Asia, indicating significant offsetting cooling contributions. Overall, human influences are detected more frequently, compared to previous studies, particularly for cold extremes, due to the extended period and the improved spatial coverage of observations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".