资源环境科技发展态势分析平台

Understanding dynamic changes in climate extremes is important in forecasting extreme climate events and reducing their associated impacts. The objectives of this study were to analyze the spatiotemporal variations in temperature extremes and their association with atmospheric circulation, based on daily maximum (TX) and minimum temperatures (TN) collected from 60 meteorological stations in the Songhua River Basin (SRB) and its surroundings from 1960 to 2014. Following the ETCCDI (Expert Team on Climate Change Detection and Indices), eight extreme temperature indices, including three warm indices, three cold indices and two extreme indices, were chosen to quantify temperature extremes. The Mann-Kendall method and linear trend analysis were used to examine the trends, and Pearson correlation analysis was used to analyze the correlation between the temperature extremes and each atmospheric circulation. The results showed that warm indices, including the number of warm nights, warm days, and summer days, and extreme indices, including minimum TN and maximum TX, showed increasing trends in the SRB from 1960 to 2014. On the other hand, cold indices, including the number of cold nights, cold days and frost days, showed decreasing trends; Warm indices and maximum TX showed significant positive correlations with latitude (P < 0.01). The Arctic Oscillation index (AO) displayed significant negative correlations with the cold indices (P < 0.01) and positive correlations with the warm indices. The warm indices and extreme indices had positive correlations with the Northern Hemisphere Subtropical High area and intensity indices, while the reverse relationship was found between the cold indices and Northern Hemisphere Subtropical High. The Asia polar vortex area and intensity indices showed negative correlations with warm indices and extreme indices, while they were positively correlated to cold indices. The multivariate ENSO index (MEI) showed no linear correlation with any of the temperature extremes. These findings will provide useful information in forecasting extreme climate events and taking measures to reduce their associated impacts. (C) 2017 Elsevier B.V. All rights reserved.