Based on daily observational data(e.g.,temperature,air pressure,and relative humidity)collected from 14 ground meteorological stations in Chengdu,the spatial distributions and temporal variations of summer extreme heat events by calculating five indices of extreme heat events during 1980-2019 were investigated.In addition,the characteristics of extremely dry and humid heat days were analyzed by using the Empirical Orthogonal Function(EOF)and Fourier decomposition methods.The results reveal that the summer extreme heat events have been more frequent in the southeastern area of Chengdu than in its northwestern area.Furthermore,an increasing tendency of heat extremes is observed from northwest to southeast across Chengdu during 1980-2019.The magnitude,frequency,and duration of the extreme heat events exhibit spatial heterogeneity.In particular,hot days occur more than heat waves in the northwest and mid-west of Chengdu,while heat waves are more prevalent in the western and northern parts.The extreme hot days and heat waves are mainly concentrated in the eastern and southern regions of Chengdu.Over the past four decades,Chengdu has witnessed a significant increase in summer extreme heat events,notably in July and August,with the most severe instances occurring in 2006 and 2016.The summer extreme heat events were dominated by the humid heat extremes across Chengdu during 1980-2019.Extreme humid and dry heat days exhibited increasing trends,with the humid heat extremes growing faster than dry heat extremes during this period.EOF analysis indicates two primary modes for extreme dry heat days:The first mode is a homogeneous pattern with obvious variations after the mid-1990s,while the second mode is a zonal tripolar pattern with considerable variations after 2000.The leading EOF mode of extreme humid heat days displays a homogeneous pattern with discernible interannual and interdecadal variations.
关键词
高温热浪/湿热/湿球黑球温度/时空分布特征
Key words
Heat wave/Humid heat/Wet bulb globe temperature/Spatiotemporal distribution characteristic
维普
万方数据