Application of wavelet empirical orthogonal function analysis to investigate the nonstationary character of Ethiopian rainfall and its teleconnection to nonstationary global sea surface temperature variations for 1900–1998
Bibliographic record
Abstract
ABSTRACT This study employed the wavelet empirical orthogonal function (WEOF) analysis to analyse the nonstationary variability of rainfall in Ethiopia and global sea surface temperature (SST) for 1900–1998. The study found that the nonstationary variations of both the June to September (JJAS) and February to May (FMAM) Ethiopian rainfall can be delineated into three zones: western half of Ethiopia north of the Great Rift Valley (GRV), southern Ethiopia south of the GRV and the GRV from southwestern Ethiopia to the Afar Triangle. The leading wavelet principal component (WPC) signals showed that Ethiopian rainfall had been in stagnation for most of 1900–1998, with major droughts in the 1940s and 1980s. The dominant frequencies of Ethiopian rainfall ranged between 2 and 8 years. In western Ethiopia, the 2–4‐year rainfall frequencies dominated the rainfall variation, but their trends are modulated by 5–7‐year frequencies, whereas in the Afar Triangle, the 5–7‐year frequencies were dominant. Between 1900 and 1998, the Afar Triangle region experienced decreasing rainfall for 60 years (1900–1960). The seasonal global SST revealed that regardless of what time of the year, the strongest contributions to global SST variations occur in the Antarctic Ocean, the El Niño region of South America and in the southwestern Pacific Ocean, followed by the Atlantic and the Indian Oceans. Further, this study also shows annual migrations of SST variations in the El Niño region, the Antarctic and the Atlantic Oceans. The leading SST signal variations show that SST warming started in the Atlantic and Indian Oceans, from 1950 to 1975, and spread to the Antarctic Ocean between 1960 and 1990, which probably contributed to the melting of sea ice. Teleconnections between WPC1 of Ethiopian rainfall and SST scale‐averaged wavelet power were found for the El Niño region and northern Atlantic, west of the Sahara desert.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".