Assessing the occurrence of compound hot and dry events from pre-industrial conditions to present-day extremes
Bibliographic record
Abstract
Heatwaves and dry spells are major climate hazards that severely impact human health, economy, agriculture, and natural ecosystems. Compound hot and dry summers have become more frequent and intense in recent years in Europe. What remains unclear is, however, to which extent the observed trend can be explained by climate change or as a feature of internal climate variability. In this study, we assess the frequency and intensity of compound hot and dry events in Europe by analyzing recent historical events from reanalysis data 1960-2022 and comparing it to i) a counterfactual reference (corresponding to pre-industrial climate conditions), and ii) model data derived from a Single Model Initial-condition Large Ensemble (SMILE).We use data from the fifth generation of the European Reanalysis (ERA5) to assess the current frequency of the compound hot and dry summers like 2003, 2015, 2018, and 2022 and analyze the intensity of the events. We use the data from the 50-member SMILE Canadian Regional Climate Model Large Ensemble (CRCM5-LE) and calculate the probability of event occurrence for those events in Europe’s current climate. Employing the ensemble allows us to assess the influence of internal climate variability vs. climate change for those events. Additionally, we use pre-industrial conditions (pi-control runs) simulated with CRCM5 to compare the probability of recent hot and dry compound events to a counterfactual world without climate change. Our analysis shows that climate change increases the frequency and intensity of compound hot and dry events. We see a substantial increase in occurrence probabilities compared to a pre-industrial world and draw to emerging hotspots of new compound extremes in several European regions. We illustrate the added value of using pi-control runs in a regional SMILE as a novel approach for impact quantification. It provides the means to understand better the already prominent role of climate change on the occurrence, frequency, and intensity of extreme events in a world of still limited warming.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".