Characteristics of Precipitation and Temperature Variation and Their Correlation with ENSO in Anhui Province during 1960-2012
Bibliographic record
Abstract
Based on the precipitation and air temperature data in Anhui Province during 1960-2012, this paper takes such methods as linearity trend estimation, least square method, inverse distance weighted, and correlation analysis to study spatial and temporal variations of climate and their correlation with ENSO in Anhui during the past 53 years. The results are indicated as follows: 1) The precipitation and air temperature in Anhui were increasing annually, the growth rates were 0.93 mm/a and 0.02℃/a, respectively, the seasonal precipitation showed various trends, the rainfall both in summer and winter was increasing, while that in spring and fall was decreasing; the change of seasonal mean air temperature showed a statistically significant increasing trend, in which the greatest increase of average temperature occurred in spring; 2) The decadal variability of rainfall characteristics was not obvious in different seasons, the precipitation in summer accounted for about 45.95% of the annual; the decadal variability of annual and seasonal temperature was increasing with fluctuation trend, different from the one of precipitation; 3) The temporal and spatial difference of precipitation was obvious, the decrease amplitude of precipitation in summer and winter, and the increase amplitude of precipitation in spring gradually increased from north to south; it was not regular for spatial variation of temperature, but Suzhou was the area where the rising range of the annual and seasonal temperature was the largest; 4) Monthly as well as annual precipitation and temperature in Anhui Province had a certain relevance with the anomaly of sea surface temperature of Nino 3.4 area and southern oscillation, the relationship between the precipitation of March, September and November and the anomaly of sea surface temperature of Nino 3.4 area and southern oscillation was significant, the temperature in September was significantly influenced by the anomaly of sea surface temperature and southern oscillation, and the anomaly of sea surface temperature had even more impact on the annual precipitation and annual average temperature; 5) In the past 53 years, 16 El Nino and 15 La Nina events occurred, the impact of La Nina events on precipitation was more obvious than that of El Nino, but both La Nina and El Nino events had relatively weak influence on temperature.
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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 | 0.000 |
| 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".