The Climatic Analysis of Summer Monsoon Extreme Precipitation Events over West Africa in CMIP6 Simulations
Bibliographic record
Abstract
Abstract We evaluate the capability of 21 models from the new state-of-the-art Coupled Model Intercomparison Project, Phase 6 (CMIP6) in the representation of present-day precipitation characteristics and extremes along with their statistics in simulating daily precipitation during the West African Monsoon (WAM) period (June–September). The study uses a set of standard extreme precipitation indices as defined by the Expert Team on Climate Change Detection and Indices constructed using CMIP6 models and observational datasets for comparison. Three observations; Global Precipitation Climatology Project (GPCP), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Tropical Applications of Meteorology using SATellite and ground-based observation (TAMSAT) datasets are used for the validation of the model simulations. The results show that observed datasets present nearly the same spatial pattern but discrepancies in the magnitude of rainfall characteristics. The models show substantial discrepancies in comparison with the observations and among themselves. A number of the models depict the pattern of rainfall intensity as observed but some models overestimate the pattern over the coastal parts (FGOALS-f3-L and GFDL-ESM4) and western part (FGOALS-f3-L) of West Africa. All model simulations explicitly show the pattern of wet days but with large discrepancies in their frequencies. On extreme rainfall, half of the models express more intense extremes in the 95th percentiles while the other half simulate less intense extremes. All the models overestimate the mean maximum wet spell length except FGOALS-f3-L. The spatial patterns of the mean maximum dry spell length show a good general agreement across the different models, and the observations except for four models that show an overestimation in the Sahara subregion. INM-CM4-8 and INM-CM5-0 display smaller discrepancies from their long-term average rainfall characteristics, in terms of extreme rainfall estimates than the other CMIP6 datasets. For the frequency of heavy rainfall, TaiESM1 and IPSL-CMGA-LR perform better when compared with observational datasets. MIROC6 and GFDL-ESM4 displayed the largest error in representing the frequency of heavy rainfall and 95th percentile extremes, and therefore, cannot be reliable. The study has assessed how rainfall extremes are captured in both observation and the models. Though there are some discrepancies, it gives room for improvement of the models in the next version of CMIP.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".