2025-03-03 中国科学院（CAS）
＜関連情報＞
- https://english.cas.cn/newsroom/research_news/earth/202503/t20250305_903106.shtml
- https://www.sciencedirect.com/science/article/abs/pii/S0022169425002409
中央アジアの天山山脈における異常降水の気候要因駆動型タイムラグ効果 Climatic factor-driven time-lag effects of extreme precipitation in the Tienshan Mountains of Central Asia
Yihan Wang, Yaning Chen, Zhi Li, Gonghuan Fang, Chuan Wang, Xueqi Zhang, Yupeng Li, Yubo Guo
Journal of Hydrology Available online: 22 February 2025
DOI:https://doi.org/10.1016/j.jhydrol.2025.132902
Highlights
- The time-lag effects of climatic factors on extreme precipitation have seasonal differences.
- The interaction between climate factors and extreme precipitation indices is most obvious in the 8–16 month period.
- The interaction of multiple teleconnection factors affects the time-lag effects.
- The model’s explanatory power improves considerably after accounting for all factors affecting the time lag.
Abstract
Global warming is disrupting the natural balance of ecosystems in the Tienshan Mountains of Central Asia (TMCA), leading to an increase in extreme precipitation (EP) events. However, the process of remote correlation between EP and climatic factors in the TMCA is not yet fully elucidated. This study employs partial correlation analysis, wavelet coherence and elastic net regression to explore the seasonal fluctuation characteristics of EP, systematically studies the spatio-temporal differentiation of temperature, water vapor content and teleconnection factors on the time-lag effect of EP, and evaluates the explanatory power of different influencing factors on the time-lag effect. Most EP indices were higher in summer and lower in winter during the study period (1951 to 2015). Furthermore, the time lag associated with temperature was shortest in spring (3-month lag), while that associated with water vapor content was shortest in summer (1-month lag). The Northern Hemisphere Subtropical High Intensity (NSI) and Solar Flux (SF) were the main drivers in both spring and summer, while the NINO B area sea surface temperature anomaly (NINOB) and the Atlantic Multidecadal Oscillation (AMO) were significant in summer and autumn. In winter, the influences of AMO and SF were more widespread. The model’s explanatory power improved significantly after incorporating all factors affecting the time lag. These findings have important implications for analyzing the dynamic response process between EP and climatic factors.
