Temporal clusters of extreme events across Europe in a regional multi-scenario/multi-member ensemble
Bibliographic record
Abstract
Climate change drives an increase in the frequency of multiple meteorological extreme event types (e.g., extreme precipitation, storms, droughts, heatwaves) by affecting thermodynamic and dynamic processes in the coupled land-atmosphere system. Extreme events like the extended droughts during 2018-2020 in Europe, flooding triggered by extreme precipitation in Germany in 2021, as well as in Valencia and central France in 2024, or prolonged heatwaves in 2003, 2015, 2018, and 2022 across continental Europe had strong adverse impacts on socio-economic systems and the environment. Given a higher frequency of extreme events, it becomes more likely that regions experience consecutive events of the same type (e.g., multi-annual droughts) or different types of extremes, thereby challenging the regions’ short-term coping and recovery ability and long-term resilience.While extreme events are generally well-studied, holistic analyses of typical extreme event sequences are missing. Compound analyses commonly focus on specific combinations or events, but usually miss typical multi-annual sequences of extreme events with the potential for high impacts.Embedded in the EU horizon 2020 project ARSINOE, our analysis addresses the questions: 1) how often these temporal clusters occur and in which constellations, 2) how robust these constellations are, 3) and what role climate change plays in modulating them. We assess temporal clusters of extreme events on the European scale in a regional multi-model/multi-scenario ensemble of the Canadian Regional Climate Model version 5 (CRCM5) covering the European CORDEX domain at a high spatial resolution (0.11°, 12 km). The CRCM5 was driven by 4 Members of the Max-Planck-Institute Grand Ensemble (MPI-ESM-LR) under SSP1 and SSP3, including corresponding adjustments of land cover. This unique setup allows to sample scenario uncertainty and internal variability. We selected extreme event indicators for extreme heat, droughts, extreme precipitation, wind and fire danger. They cover hazards of regionally varying importance, but each of them poses considerable risks to human and natural systems in Europe.This contribution presents the initial results of climate trends in multiple extreme event indicators under a Paris-Agreement-compliant scenario and a low-mitigation scenario. We also show first findings on consecutive events of these indicators across Europe in reanalysis data and the CRCM5 ensemble. With our research, we aim to map vulnerability hotspots associated with temporally compounding extreme 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".