Evaluating CMIP6 Model Accuracy in Predicting and Clustering Precipitation and Temperature Trends in the Omo-Gibe River Basin, Ethiopia
Bibliographic record
Abstract
This study evaluated the performance of the latest generation of global climate models (CMIP6) in predicting and clustering climate variables in the Omo-Gibe River Basin (OGRB), Ethiopia—a region highly vulnerable to climate change. Historical data (1984–2014) were used to evaluate 12 CMIP6 models using statistical metrics (R², RMSE, MBE) and categorical metrics (POD, FAR, CSI), identifying the best-performing models for each cluster. The selected models underwent bias correction using distribution mapping to enhance projection accuracy under two socio-economic pathways: SSP2–4.5 (moderate emissions) and SSP5–8.5 (high emissions) for the near-term (2023–2053) and mid-term (2054–2084) periods. The study employed the K-means clustering method to classify spatial variations in precipitation and temperature, resulting in three clusters of precipitation and two clusters for temperature. Sen's slope estimator and the Modified Mann-Kendall (MMK) test were utilized to analyze trends in precipitation and temperature time series. Key findings include model performance: For precipitation, INM-CM5-0, FGOALS-g3, and IPSL-CM6A-LR demonstrated strong performance in Clusters 1 and 3, while MPI-ESM1-2-LR and NorESM2-MM excelled in Cluster 2. For temperature, INM-CM5-0, BCC-CSM2-MR, and MPI-ESM1-2-LR exhibited the highest accuracy in Clusters 1 and 2. Significant precipitation increases were noted, particularly in Cluster 3, which shows a 74.7% rise by mid-century under SSP5–8.5. Temperature projections indicate maximum increases of 11.5% (absolute change: 3.1°C) in Cluster 1 and 8.4% (absolute change: 2.2°C) in Cluster 2, while minimum temperature rises reach as high as 23.5% (absolute change: 3.5°C). Spatial clustering revealed distinct climate patterns, with regions experiencing monomodal and bimodal rainfall cycles, providing a nuanced understanding of precipitation and temperature dynamics across the basin. These results reveal pronounced shifts in climate patterns, underscoring the importance of targeted adaptation strategies. The findings provide critical insights into regional climate dynamics and support the development of climate-resilient water resource management and infrastructure planning. Collaborative efforts between policymakers, researchers, and communities are essential to address the anticipated challenges effectively.
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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 | 0.000 |
| 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".