A Non-Stationary Heat Spell Frequency, Intensity, and Duration Model for France, Integrating Teleconnection Patterns and Climate Change
Bibliographic record
Abstract
The warming observed over the past summers since 2000 is unprecedented in climate records in Europe and especially in France. Extreme temperatures and heat spells were often analyzed in the literature by applying extreme value theory but rarely in a non-stationary (NS) framework and duration modeling is often excluded. For a modern risk-based approach, it is important to have knowledge of the duration, magnitude, and frequency of occurrence of heat spells in a climate variability and change context. Yet, despite their obvious importance, teleconnections and associated climate indices (CIs) have often been excluded from heat spell modelling. The notion of duration is also not easily interpretable in a frequency analysis and can even be subtle, especially in a NS context. In this study, we used time-varying statistical distributions with parameters conditional on covariates representing the time and CIs. The daily maximum temperatures (DMTs) observed at the Orange and Dijon stations in France were used as a case study. This paper highlights a possible relationship between some large-scale climate patterns and the heat spells in France. Overall, the results suggest that considering the combined effect of global warming and these patterns in NS models is useful for a more appropriate characterization of the hazard heat spells in France.
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 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 | 0.000 | 0.000 |
| 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 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".