| 英文摘要： |
A thorough understanding of regional differences in change patterns of surface air temperature (SAT) at various spatial scales can help people cope well with global warming. In this study, a SAT change pattern recognition method was firstly established based on k-mean++ algorithm. By creating a clustering effect evaluation index (DBWk) to select the optimal cluster number k, the pattern of SAT change in 1960-2016 of China was recognized at national and regional scales. Results showed that China's SAT change patterns were grouped into 3 clusters, namely, Clusters I, II and III, at the national scale. These clusters were further divided into 3, 7, and 4 subclusters at the regional scale, respectively. The SAT change in Cluster I was intense, with a relatively cold period (1960-1987) and a relatively warm period (1988-2016). The SAT of Cluster II decreased slightly in the first phase (1960-1983), minimally increased in the third phase (1999-2016), but rose strongly in the second phase (1984-1998). The linear trend (LT) of SAT increase of Cluster III was high and statistically significant, especially in 1983-2016. The analysis of the SAT change pattern of subclusters showed that the SAT fluctuations of the Altai Mountains and Junggar Basin were the strongest. The Northern Qinghai-Tibet Plateau had the highest warming rate, and the LT of warming was statistically the most significant in China. |
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