Influence of climate oscillations on temperature and precipitation over the United Arab Emirates
Bibliographic record
Abstract
In this study, we investigate the influence of global climate oscillations on the local temperature and precipitation over the United Arab Emirates (UAE), which is one of the driest regions in the world with very high temperatures and low precipitation. The identification and assessment of remote interactions (teleconnections) are carried out by using ground station and gridded data sets. Monthly rainfall data from six ground stations over the UAE for the period of 1982–2010 is used in this study along with the long-term gridded precipitation and temperature data from the Global Precipitation Climatology Center and Global Historic Climatic Network. Linear correlations, wavelet analysis, and cross-wavelet analysis have been applied to identify the relation between climate indices and precipitation (temperature). The analysis reveals that the strong variability in precipitation is closely associated with the Southern Oscillation Index (SOI) and the Indian Ocean Dipole Index (IOD) during the months of August–March, September–January, respectively. In case of temperature, the strong variability is associated with the North Atlantic Oscillation Index (NAO) and the East Atlantic Oscillation Index (EAO) during the months of April–October, July–December. Spatial analysis of cross-wavelet reveals that the winter precipitation is significantly influenced by SOI and temperature during summer by the NAO. This research concludes that the negative phases of SOI (NAO) play a significant role in the increase of precipitation (decrease in summer temperatures) over the UAE region.
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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".