气候学
弹簧(装置)
环境科学
产量(工程)
气候变化
作物产量
作物
中国
地质学
地理
农学
海洋学
生物
机械工程
材料科学
考古
工程类
冶金
作者
Xianke Yang,Yixuan Zhang,Haosu Tang,Ping Huang,Xiaoxia Ling,Shaobing Peng,Dongliang Xiong
摘要
ABSTRACT Climate‐related risks are shaped not only by changes in mean temperatures, but also by temperature variability, which raises the likelihood of extreme weather events with profound impacts on society and ecosystems. Previous studies have documented contrasting seasonal trend differences in summer and winter temperature variability across most land areas. However, spring—a phenologically sensitive season for agricultural systems—has received limited attention for its temperature variability. Focusing on the major rice‐growing regions in southern China, this study employs three indices—daily temperature standard deviation (STD), day‐to‐day temperature variability (DTD) and rapid cooling events (RCE)—to analyse the decadal trends and causes of spring temperature variability and assess its climate effects on rice yield anomalies. Our results reveal decadal trends in the spatial distribution of temperature variability, with increasing frequency and intensity in the Yangtze River Basin and Yunnan Province, and a decreasing trend across much of South China, closely following regional climatological patterns. Overall, the frequency and intensity of RCE trend exhibit a “strong gets weaker, weak gets stronger” pattern, likely linked to increased STD trends caused by spatial non‐uniformity of warming. Through a multiple regression statistical model employing dominance analysis, we find that climate factors, including both mean climate and climate variability, explained 19%–45% of the variance in provincial rice yield anomalies, with up to 13% of the explained variance attributable to spring climate factors related to temperature variability. This study underscores the critical role of spring temperature variability in climate resilience, highlights the urgent need to enhance the adaptability of agricultural systems to extreme climate events.
科研通智能强力驱动
Strongly Powered by AbleSci AI