Long-Term Persistence in Observed Temperature and Precipitation Series
Bibliographic record
Abstract
The Hurst phenomenon is regarded as an intrinsic characteristic of many natural processes closely related to high uncertainty and long-term persistence. Temperature and precipitation are the two important meteorological factors characterizing the climate conditions of different regions. Analyzing the Hurst phenomenon in precipitation and temperature are crucial for understanding the long-term dynamics of our climate system. This study examines the annual mean temperature (AMT) and annual total precipitation (ATP) series for regions across all the land areas of the world, using both gridded climate data and ground station records. The results demonstrate that, in most regions, the Hurst exponent of AMT is higher than that of ATP, particularly with larger spatial scales of averaging. Like ATP, the Hurst exponents of AMT also increase with the spatial scale of averaging. Unlike AMT, ATP is more controlled by local meteorological conditions which tend to weaken its long-term persistence. Moreover, the cumulative departure from the mean series of ATP is much more variable across different regions, whereas those of AMT for different regions are more similar. What is identified for the first time in this study is the strong similarity in the cumulative departure from the mean patterns of regionally averaged and individual stations’ ATP and AMT series over many regions of the world. At most of these regions and stations where such similarities are identified, more than half have confirmed that AMT is the Granger cause of ATP variations. Moreover, the fluctuation functions obtained in multifractal detrended cross-correlation analysis exhibit approximately linear behavior in the log–log spaces across all regions at both global and continental scales, indicating that ATP and AMT series are long-range cross-correlated.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".