Frequency Rather Than Intensity Drives Projected Changes of Rainfall Events in Brazil
Bibliographic record
Abstract
Abstract Extreme rainfall events are expected to intensify with global warming, posing significant challenges to both human and natural environments. Despite the importance of such assessments, they are unevenly widespread across the globe. Here, using bias corrected climate simulations of the latest phase of the Coupled Model Intercomparison Project (CMIP6), we provide a comprehensive assessment on how different rainfall events are expected to change across Brazil. Specifically, (a) we explored the projected changes in both intensity and frequency of rainfall events belonging to the right‐tail of the rainfall distribution using a non‐parametric approach, and (b) quantified how rainfall events associate with different return periods are expected to intensify, using a parametric approach. We found that extreme rainfall events will become more frequent and intense by the end of the century, with averaged projected changes for rainfall exceeding the historical rainfall quantile q 0.99 of nearly 100% and 10% on frequency and intensity, respectively. Non‐extreme rainfall events, in contrast, are expected to be less frequent, aligning with the compensation hypothesis. For instance, Brazilian 100‐year rainfall are anticipated to intensify, on average, 17% and 31% under the moderate and the highest CMIP6 emission scenarios, respectively. Finally, our findings suggest that frequency, rather than intensity, dictates the projected changes of rainfall. We believe that the evidence gathered here will certainly contribute to not only an improved understanding of Brazilian rainfall events but also to a better comprehension of the different rainfall properties, their interplay and how the different ways of assessing them may affect climate studies.
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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 | 0.003 |
| 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 | 0.001 | 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".