Developing Climate Change Projections using Different Representative Concentration Pathways of Emission Scenario: In the Case Jimma, Ethiopia
Bibliographic record
Abstract
Anthropogenic influence on the climate system is clear, and the recent emissions of greenhouse gases are the highest in history. This particular activity is aimed to project the future climate using different representative concentration pathways of emission scenarios for Jima station. Statistical downscaling approach was used to downscale rainfall and temperature. Predictors were synthesized based on correlation analysis between large scale climate predictors and observed station climate data. Monthly predictors were used to establish a regression model between the predictors and observed climate variables. The regression models were validated against observed station data and were used to generate downscaled future rainfall and temperature. Data for future scenario were collected from the Canadian Centre for Climate Modeling and Analysis (CCCma) of Canada Environment. The minimum temperature will be increased from 0.5 0 C to 0.8 0 C from the lowest to worst emission scenario for neat term period and 1.2 0 C to 2.6 0 C for end of century respectively. Similarly the maximum temperature will be increased 1.2 0 C to 2.0 0 C for near term century and 2.3 0 C to 3.9 0 C for midterm and 3.7 0 C to 4.7 0 C for end of century under RCP2.6, RCP4.5 and RCP8.5 which alarming mitigation measures. There is low % of precipitation change or increasing depends on RCPs the Projection changes slight increase in precipitation for main rain seasons (AMJ) and (JAS) is only under RCP2.6 but there is decrease under RCP4.5 and RCP8.5 for all time horizon of 2020's, 2050's and 2080's. The projected change in annual mean temperature under RCP2.6 (upper), RCP4.5 (middle) and RCP8.5 (bottom) scenarios: approximately 3.7C, 4.2C and 3.7C respectively.
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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 | 0.001 |
| Open science | 0.001 | 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".