Climate-Related Extreme Events with High-Resolution Regional Simulations: Assessing the Effects of Climate Change Scenarios in Ouagadougou, Burkina Faso
Bibliographic record
Abstract
We have applied a Bayesian framework for the analysis and testing of possible non-stationarities in extreme meteorological events in an area around Ouagadougou, Burkina Faso. Considering the results obtained for the historical period (1950-2005), it can be seen that, for a given exceedance probability, the intensities of extreme temperature and extreme consecutive dry days ECDD data are positively correlated. The higher values of extreme temperature and ECDD are identified at the Eastern part of the domain. This result suggests that those areas can be more likely exposed to desertification processes. Some of these areas are also coincident with areas in which extreme rainfall events may occur, and this combination can be a factor amplifying the possibility of flood events. Looking at the effects of the two climate change scenarios considered (RCP4.5 and RCP8.5), different patterns were found for the three variables analyzed; whereas, the ECDD data indicate that the stationary model is the one that dominates most of the solutions. The extreme temperature and extreme precipitation show remarkable trends in both scenarios. In this paper, analyses of the spatial distribution of the extreme events and the temporal trends observed when considering scenarios of climate change are performed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".