Time of emergence of compound events: contribution of univariate and dependence properties
Bibliographic record
Abstract
Many climate-related disasters often result from a combination of several climate phenomena, also referred to as “compound events’’ (CEs). By interacting with each other, these phenomena can lead to huge environmental and societal impacts, at a scale potentially far greater than any of these climate events could have caused separately. Marginal and dependence properties of the climate phenomena forming the CEs are key statistical properties characterising their probabilities of occurrence. In this study, we propose a new methodology to assess the time of emergence of CE probabilities, which is critical for mitigation strategies and adaptation planning. Using copula theory, we separate and quantify the contribution of marginal and dependence properties to the overall probability changes of multivariate hazards leading to CEs. It provides a better understanding of how the statistical properties of variables leading to CEs evolve and contribute to the change in their occurrences. For illustrative purposes, the methodology is applied over a 13-member multi-model ensemble (CMIP6) to two case studies: compound wind and precipitation extremes over the region of Brittany (France), and frost events occurring during the growing season preconditioned by warm temperatures (growing-period frost) over central France. For compound wind and precipitation extremes, results show that probabilities emerge before the end of the 21st century for six models of the CMIP6 ensemble considered. For growing-period frosts, significant changes of probability are detected for 11 models. Yet, the contribution of marginal and dependence properties to these changes in probabilities can be very different from one climate hazard to another, and from one model to another. Depending on the CE, some models place strong importance on both marginal properties and dependence properties for probability changes. These results highlight the importance of considering changes in both marginal and dependence properties, as well as their inter-model variability, for future risk assessments related to CEs.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".