Climate change effects on sub-daily extreme precipitation over Europe and the role of natural variability
Bibliographic record
Abstract
Sub-daily precipitation extremes over Europe induce hazards such as mass movements and floods. These hazards are impacting the society in terms of financial losses, which is of great interest for insurance companies. The occurrence probability of heavy rainfall events is often assessed by calculating rainfall return periods. Though, these estimations are governed by uncertainties due to the natural variability of the climate system. Here, we quantify the range of sub-daily extreme precipitation due to natural variability within the single model initial-condition large ensemble featuring 50 members of the Canadian regional climate model, version 5 (CRCM5) under the high-emission scenario Representative Concentration Pathway 8.5. Therefore, we calculate 10-year return levels of sub-daily precipitation for hourly to 24-hourly aggregations in a European domain for each of the 50 ensemble members. The analysis is carried out for four time periods covering 1980 to 2099: the reference period (1980 – 2009) and three future periods (2010 – 2039, 2040 – 2069, 2070 – 2099). We find that the rainfall intensities of the 10-year return levels increase by 5 – 9 % on areal average for every future 30-year period. There, short-duration rainfall intensities increase to a greater extent than longer-duration rainfall intensities. Natural variability as uncertainty source is quantified as the range between the median of the 50 members and the 5th and 95th quantile, respectively. This spread is between -16 % – 20 % for hourly duration and -13 % – 17 % for 24-hourly duration. These findings highlight the large impact of natural variability on the estimation of extreme precipitation return levels. This database also allows us to identify regions in Europe, where future median extreme precipitation exceeds the 95th quantile of the reference period. These regions of significant changes are in northern Europe, central Europe and the eastern part of the Mediterranean.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".