Compound warm–dry and cold–wet events over the Mediterranean
Bibliographic record
Abstract
Abstract. The Mediterranean (MED) basin is a climate change hot-spot that has seen drying and a pronounced increase in heatwaves over the last century. At the same time, it is experiencing increasing heavy precipitation during wintertime cold spells. Understanding and quantifying the risks from compound events over the MED is paramount for present and future disaster risk reduction measures. Here, we apply a novel method to study compound events based on dynamical systems theory and analyse compound temperature and precipitation anomalies over the MED from 1979 to 2018. The dynamical systems analysis measures the strength of the coupling between different atmospheric variables over the MED. Further, we consider compound hot-dry days in summer and cold-wet days in winter. Our results show that these hot-dry and cold-wet compound days are associated with maxima in the temperature–precipitation coupling parameter of the dynamical systems analysis. This indicates that there is a strong interaction between temperature and precipitation during compound events. In summer, we find a significant upward trend in the coupling between temperature and precipitation over 1979–2018, which is likely driven by a stronger coupling during hot and dry days. Thermodynamic processes associated with long-term MED warming can best explain the trend. No such trend is found for wintertime cold-wet compound events. Our findings suggest that long-term warming strengthens the coupling of temperature and precipitation which intensifies hot-dry compound events.
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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 | 0.000 |
| 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) | 0.001 | 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".