The emergence of projected scaled patterns of extreme temperatures over Europe
Bibliographic record
Abstract
This work investigates the scalability of extreme temperatures over the European domain with global warming levels. We have used the EURO-CORDEX ensemble of regional model simulations at 0.11° resolution for daily minimum and maximum temperatures to analyze future changes in extreme weather daily events. Scaling with the annual mean global warming modeled by the driving GCM was applied to future extreme temperature indices changes. Regional changes in each index were scaled by corresponding global warming levels obtained from GCMs. This approach asserts that regional patterns of climate change and average global temperature change are linearly related. It can provide information regarding climate change for periods or emission scenarios when no simulations exist. According to the results, the annual minimum of the lowest temperature of the day (TNn) increases more than the annual maximum of the highest temperature of the day (TXx) for Europe. The multi-model mean of the changes in scaled patterns of extreme temperatures emerges early, around 2020, even before it becomes robust. Individual scaled patterns of TNn and TXx emerge from around 2040.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".