Long‐term projections of temperature, precipitation and soil moisture using non‐stationary oscillation processes over the <scp>UAE</scp> region
Bibliographic record
Abstract
ABSTRACT This study discusses the evolution of temperature, precipitation and soil moisture patterns over the United Arab Emirates (UAE) region, which is characterized by hot climate and scarce precipitation. A stochastic model that reproduces non‐stationary oscillation (NSO) processes by utilizing ensemble empirical mode decomposition (EEMD) and non‐parametric techniques is used to predict the evolution of temperature, precipitation and soil moisture. The long‐term gridded temperature, precipitation and soil moisture data from the Global Historic Climatic Network, Global Precipitation Climatology Center and Climate Prediction Center are used in this study. The data consists of 65 years of average monthly temperature and soil moisture measurements and 110 years of average monthly precipitation over the UAE. The last 20 years of observations of temperature, precipitation and soil moisture are reserved for the validation of the methodology and the rest of the data is used for prediction. The results show that future long‐term patterns are well captured by the model and hence confirm the potential of the EEMD technique and the NSO resampling (NSOR) modelling process. The model is also used for forecasting the evolution of temperature, precipitation and soil moisture patterns for the next 30 years. This procedure is finally used to produce the spatial patterns of temperature, precipitation and soil moisture. Significant increase in temperature and decrease in precipitation and soil moisture are observed particularly over Abu Dhabi. The spatial map shows strong increase (decrease) in temperature (precipitation and soil moisture) over most of the UAE. The results are quite different for the south eastern part of the UAE. Western parts of the UAE are projected to see larger temperature increases than other parts. The results are coherent with the previous findings over this region.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".