Association between three prominent climatic teleconnections and precipitation in Iran using wavelet coherence
Bibliographic record
Abstract
ABSTRACT Large‐scale climatic teleconnections have noticeable effects on meteorological events in different regions of the world. In this study, the linkages between three major climatic indices, Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI), and precipitation in Iran were assessed from 1960 to 2014, at 30 synoptic stations in a time‐frequency space, using wavelet coherence (WCO). The results showed that the SOI is the most effective climatic teleconnection on precipitation in Iran, although the other studied climatic indices have noticeable effects as well. The predominant and effective period of AO on precipitation was equal to or greater than 32 months at most of the stations, while the major effective period of NAO was equal to or greater than 64 months. For the SOI, most parts of the country were affected by a period of less than 64 months, while the predominant period of SOI for the northwestern part of the country was greater than 64 months. A uniform phase difference was not observed between the three studied climatic indices and precipitation in the country; instead the phase differences were usually random. For long‐term periods of SOI, an anti‐phase situation was detected at most of the stations. The study suggested that the WCO is a very powerful and flexible method for studying the relationship between multiple time series in a time–frequency space, and its application in hydrological and meteorological research is expected to increase in the near future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".