On the Current and Future Dry Spell Characteristics over Africa
Bibliographic record
Abstract
Changes in precipitation frequency and intensity distribution over Africa will have a direct impact on dry spells and, therefore, will affect various climate sensitive sectors. In this study, the ability of the fifth generation of the Canadian Regional Climate Model (CRCM5) in simulating annual and seasonal dry spell characteristics is assessed for four precipitation thresholds (0.5 mm, 1 mm, 2 mm and 3 mm) over Africa. The dry spell characteristics considered are the number of dry days, number of dry spells and five-year return levels of maximum dry spell durations. The performance errors are assessed by comparing ERA-Interim driven CRCM5 with the Global Precipitation Climatology Project (GPCP) dataset, for the common 1997–2008 period. Lateral boundary forcing errors, i.e., errors in the CRCM5 simulation created by errors in the driving Canadian Earth System model (CanESM2) data—as well as the added value—of CRCM5 over CanESM2 are also assessed for the current climate. This is followed by an assessment of projected changes to dry spell characteristics for two future climates (2041–2070 and 2071–2100) simulated by both CRCM5 driven by CanESM2 and CanESM2 itself, for Representative Concentration Pathway (RCP) 4.5. Results suggest that CRCM5 driven by ERA-Interim has a tendency to overestimate the annual mean number of dry days and the five-year return level of the maximum dry spell duration in a majority of the regions while it slightly underestimates the number of dry spells. In general, the CRCM5 performance errors associated with the annual and seasonal dry spell characteristics are found to be larger in magnitude compared to the lateral boundary forcing errors. Projected changes to the dry spell characteristics for the 2041–2070 and 2071–2100 periods, with respect to the 1981–2010 period suggests significant changes in the tropics, with the mean number of dry days and the five-year return levels of maximum dry spell duration increasing, while the mean number of dry spell days decreases.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".