Assessing the performance of the CMIP6 multi model mean in simulating precipitation and temperature across Africa
Bibliographic record
Abstract
Abstract This study examines the capability of the Coupled Model Intercomparison Project Phase 6 to replicate temperature and precipitation across eight African sub-regions, as well as their correlation with three large-scale climate indices. Qualitative estimations indicate that reference datasets (ERA5, CRU, CHIRPS) and the MMM exhibit similar patterns, albeit with slightly different intensities in many sub-regions and seasons. For precipitation and temperature, several models exhibit good performance across various sub-regions and seasons, with PCCs of 0.98. The MMM effectively captures the signs of the observed trend in many African sub-regions but fails to capture its magnitude. The MMM exhibits better performance than individual models across various sub-regions. Models like MMM and GFDL-ESM4 consistently outperform others, particularly in precipitation. However, significant regional disparities are observed, with SWAF, CAF, and WAF (for temperature during DJF) being the most challenging areas, where few models meet the performance thresholds. In MAM, models exhibit strong performance in NAF, WAF, CAF, NEAF, and SWAF but fail to meet the cutoff in SAH and CEAF. The models exhibit robust performance across many regions during JJA and SON. The MMM also captures the increasing (but with relatively lower value) temperature depicted by ERA5 and CRU. The MMM shows a limitation in reproducing the response to some large-scale climate indices.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".