Observed changes in temperature extremes over Asia and their attribution
Bibliographic record
Abstract
This study presents trends in a newly compiled temperature extreme indices dataset for Asia covering the period of 1958–2012. Daily data were homogenized prior to the calculation of the indices. A clear warming trend was observed in all indices, which is consistent with the global warming. In most of the indices, larger warming was observed at high latitudes than at low latitudes. We also compared observations with simulations from the Coupled Model Inter-comparison Project Phase 5 for some indices using an optimal fingerprinting method. These indices include the number of days with daily maximum or minimum temperatures greater than their 90th percentiles or smaller than their 10th percentiles, the annual highest daily maximum and minimum temperatures, and the annual lowest daily maximum and minimum temperatures. We determined that the warming trend was inconsistent with the natural variability of the climate system but agreed with climate responses to external forcing as simulated by the models. The anthropogenic and natural signals could be detected and separated from each other in the region for almost all indices, indicating the robustness of the warming signal as well as the attribution of warming to external causes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".