Compound temperature and precipitation extreme events in southern South America: associated atmospheric circulation, and simulations by a multi-RCM ensemble
Bibliographic record
Abstract
In this paper we analyse the joint distribution of extreme temperature and heavy precipitation events in southern South America during 1961-2000, and the predominant atmospheric circulation associated with the occurrence of compound extreme events. We show that the probability of occurrence of intense precipitation (daily rainfall higher than the 75th percentile) significantly increases during or following a warm night (minimum temperature higher than the 90th percentile), but decreases during a cold night (minimum temperature lower than the 10th percentile) during the warm season. Heavy precipitation events are associated with the simultaneous occurrence of warm days (maximum temperature higher than the 90th percentile) or following such an event in eastern Argentina, but they rarely occur before. In contrast, cold days (maximum temperature lower than the 10th percentile) happen more often after an intense rain. Compound events are usually associated with 1 or 2 typical circulation patterns in each subregion. For example, warm days and heavy precipitation tends to occur more often when a trough over the Pacific Ocean and a cold front over the continent lead to warm and wet air advected to the east of the region of study. We also analysed the skill of 7 regional climate models from the CLARIS LPB project to simulate the statistical relationship between temperature and precipitation extremes in 1990-2000. Overall, models were able to simulate an increase in the probability of occurrence of extreme rainfall during warm nights and cold days, and an inhibition of precipitation during cold nights. However, models tend to fail to capture the spatial distribution of the compound extreme events in southeastern South America.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".