Greenhouse gas and aerosol contributions to the observed global and regional changes in extreme temperature changes
Bibliographic record
Abstract
This study carried out an updated detection and attribution analysis of extreme temperature changes for 1951-2015. Four extreme temperature indices (warm extremes: annual maximum daily maximum/minimum temperatures; cold extremes: annual minimum daily maximum/minimum temperatures) were used considering global, continental (6 domains), and subcontinental (33 domains) scales. HadEX3 observations were compared with CMIP6 multi-model simulations using an optimal fingerprinting technique. Response patterns of extreme indices (fingerprints) to anthropogenic (ANT), greenhouse gas (GHG), anthropogenic aerosol (AA), and natural (NAT) forcings were estimated from corresponding CMIP6 forced simulations. Pre-industrial control simulations (CTL) were also used to estimate the internal variability. Results from two-signal detection analysis where the observations are simultaneously regressed onto ANT and NAT fingerprints reveal that ANT signals are robustly detected in separation from NAT in global and most continental regions for all extreme indices. At subcontinental scale, ANT detection occurs especially in warm extremes (more than 60% of regions). Results from three-signal detection analysis where observations are simultaneously regressed onto GHG, AA, and NAT fingerprints show that GHG signals are detected and separated from other external forcings over global, most continental, and several subcontinental (more than 60%) domains in warm extremes. In addition, AA influences are jointly detected in warm extremes over global, Europe and Asia. The detected GHG forcings are found to explain most of the observed warming while AA forcings contribute to the observed cooling for the early decades over globe, Europe, and Asia with a slight warming over Europe during recent decades. Overall, improved detection occurs compared to previous studies, especially in cold extremes, which is due to the use of extended period which increases signal-to-noise ratios.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".