Projections of future surface air temperature for Awash River Basin in Ethiopia using statistical downscaling method
Bibliographic record
Abstract
Climate change has become a major environmental and socioeconomic challenge in Ethiopia. Statistically downscaled daily data is used in 30-year intervals from the results of the second generation of the Canadian Earth System Model (CanESM2) under three representative concentration pathways of carbon emission scenarios (RCPs): RCP 2.6, 4.5, and 8.5 to project the future climate change. The method engaged to generate climate change scenarios for each RCPs is Statistical Downscaling Method (SDSM Version 4.2.9), using results of the CanESM2. Besides cited, statistical regression analyses are manipulated to evaluate SDSM model performances. The results showed that regarding SDSM model evaluation, the SDSM model demonstrated good to excellent efficiency with calibration value R2 > 0.95 and validation value R2 > 0.90 in case of maximum and minimum surface air temperature. Regarding the results of climate change scenario projections, on some months and seasons a change in temperature exhibited from a very minor rise (0.3°C) and a very minor decrease (-0.2°C) from the climatic mean, under all RCPs to a significant increase (3.5°C) on some other months and seasons. In addition, for both climate parameters the changes in the periods 2050s and 2080s are greater than in the 2020s, under each RCP. Further, the average change in minimum air temperature (2.5°C) is anticipated to be larger than the change in maximum air temperature (2.0°C), under all RCPs. Moreover, an increasing trend is observed for both maximum and minimum air temperatures starting from 2020s to 2080s in all cases of RCPs. So, in order to keep global warming below 1.5°C, it is recommended to prioritize climate change adaptation and mitigation practices to those low land areas which are going to be more vulnerable and likely affected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".