Statistical downscaling of global circulation models to assess future climate changes in the Black Volta basin of Ghana
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Bibliographic record
Abstract
Statistical Downscaling Model (SDSM) is a powerful model for climate change assessment. However, its usage remains very gray with limited studies on climate change (CC) assessment in Ghana. This study explored the applicability and suitability of SDSM for CC assessment in the Black Volta section of Ghana. The hydro-climatic parameters of Hadley center Coupled Model, version 3 (HadCM3) under the A2 and B2 Emissions Scenarios and the second-generation Canadian Earth System Model (CanESM2) under the Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 of the Coupled Model Intercomparison Project Phase 5 were downscaled with SDSM over the Black Volta section in Ghana using 40-year ground station data. The R 2 , NSE, Pbias, RMSE, and KGE of the calibrated and validated results ranged from 64% to 99%, 50–99%, -0.30–21.1, 0.01 °C–1.48 °C and 49%–99%, respectively for both models indicating a good agreement between the historical and the simulated data. The future climate change showed an increase in average minimum temperature of 0.05 °C (2020s), 0.11 °C (2050s), 0.21 °C (2080s) under the A2 scenario, 0.05 °C (2020s), 0.13 °C (2050s), 0.19 °C (2080s) under the B2 scenario, 0.01 °C (2020s), 0.02 °C (2050s), 0.02 °C (2080s) under the RCP 2.6, 0.06 °C (2020s), 0.13 °C (2050s), 0.19 °C (2080s) under the RCP 4.5, and 0.06 °C (2020s), 0.15 °C (2050s), 0.32 °C (2080s) under the RCP 8.5. For Maximum temperature, the average changes showed an increase of 0.17 °C (2020s), 0.36 °C (2050s), 1.14 °C (2080s) under the A2 scenario, 0.18 °C (2020s), 0.39 °C (2050s), 1.01 °C (2080s) under the B2 scenario, 0.03 °C (2020s), 0.16 °C (2050s), 0.17 °C (2080s) under the RCP 2.6, 0.02 °C (2020s), 0.26 °C (2050s), 0.45 °C (2080s) under the RCP 4.5, and 0.03 °C (2020s), 0.29 °C (2050s), 0.61 °C (2080s) under the RCP 8.5. The change in precipitation is not uniform with increase and decrease depending on the months and the scenarios. Overall, A2, B2 scenarios showed higher decrease in precipitation compared to RCPs scenarios. The SDSM is suitable for CC assessment and impact studies. The results from this study are to support the Climate Action, goal 13 of the SDGs.
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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 it