Anthropogenic Influence on Long Return Period Daily Temperature Extremes at Regional Scales
Bibliographic record
Abstract
Abstract Observed 1961–2000 annual extreme temperatures, namely annual maximum daily maximum (TXx) and minimum (TNx) temperatures and annual minimum daily maximum (TXn) and minimum (TNn) temperatures, are compared with those from climate simulations of multiple model ensembles with historical anthropogenic (ANT) forcing and with combined anthropogenic and natural external forcings (ALL) at both global and regional scales using a technique that allows changes in long return period extreme temperatures to be inferred. Generalized extreme value (GEV) distributions are fitted to the observed extreme temperatures using a time-evolving pattern of location parameters obtained from model-simulated extreme temperatures under ANT or ALL forcing. Evaluation of the parameters of the fitted GEV distributions shows that both ANT and ALL influence can be detected in TNx, TNn, TXn, and TXx at the global scale over the land areas for which there are observations, and also regionally over many large land areas, with detection in more regions in TNx. Therefore, it is concluded that the influence of anthropogenic forcing has had a detectable influence on extreme temperatures that have impacts on human society and natural systems at global and regional scales. External influence is estimated to have resulted in large changes in the likelihood of extreme annual maximum and minimum daily temperatures. Globally, waiting times for extreme annual minimum daily minimum and daily maximum temperature events that were expected to recur once every 20 yr in the 1960s are now estimated to exceed 35 and 30 yr, respectively. In contrast, waiting times for circa 1960s 20-yr extremes of annual maximum daily minimum and daily maximum temperatures are estimated to have decreased to fewer than 10 and 15 yr, respectively.
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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) | 0.000 | 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".