The emergence of projected scaled patterns of extreme weather events over Europe
Bibliographic record
Abstract
This work investigates the scalability of wet and dry persisting condition patterns over the European domain with global warming levels. For this aim, we have used the EURO-CORDEX ensemble of regional climate projections at 0.11° grid-mesh for daily minimum and maximum temperature and precipitation to analyze future changes in extreme weather events addressing climate warming levels of 1°C, 2°C, and 3°C, respectively. A simple scaling with the annual mean global mean temperature change modeled by the driving GCM is applied. The annual minimum of daily minimum temperature (TNN) is found to increase more compared to the annual maximum of daily maximum temperature (TXX) at the end of the century. We also identify the emergence of the scaled patterns of minimum and maximum temperatures and wet and dry persisting conditions about certain extreme weather indices. The emergence of the scaled patterns of TNN occurs from around 2040, whereas TXX pattern is emerging around 2050. Individual GCM-RCM pairs tend to have stable spatial patterns since then for both indices. The ensemble mean patterns are emerging earlier than the individual models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".