论文标题
20世纪大陆，区域和季节性土地温度的趋势
Trends of continental, zonal and seasonal land temperatures in the 20th century
论文作者
论文摘要
我们使用主成分分析（PCA）和反向排列趋势分析研究了大陆，区域和季节性土地温度异常的演变，尤其是在20世纪初期（ETCW）时期的演变。除大洋洲以外，所有其他大洲都很重要。从ETC开始，南美的变暖意义重大，但是最近在1990年左右才在北美和欧洲开始进行大量的变暖。 区域和季节性PC2均与AMO指数相关，但Zonal PC3与南部振荡指数（SOI）和季节性PC3有关，而季节性PC3与冬季时间El Nino（NINO34 DJF指数）有关。在南半球，最近的变暖开始于1950年代最接近赤道，并在1970年代后期在最南端的区域开始。在两个最低的北部区域（EQ-N24，N24-N44）中，变暖很重要，因为ETCW和1970年代的变暖开始增加，但是在最北端的两个区域（N44-N64，N64-N90）中，冷却后的冷却在ETCW之后的降温延迟了最近的季节，直到1990年左右，所有季节都很大。 这三个PCA几乎具有1910 - 2017年分析的几乎常见的PC1成分，即，直到1940年代，温度的逐渐升高，截至1950年代末，直到1970年代下半年的平坦相位，在那之后急剧上升。但是，大陆PC1仅解释了数据变化的75.2％，而区域和季节性PC1分别解释了81.7％和87.6％的相应数据。
We study the evolution of continental, zonal and seasonal land temperature anomalies especially in the early 20th century warming (ETCW) period, using principal component analysis (PCA) and reverse arrangement trend analysis. ETCW is significant in all other continents except for Oceania. Warming in South America is significant from the ETCW onwards, but significant recent warming started in North America and Europe only around 1990. The zonal and seasonal PC2s are both correlated with AMO index, but zonal PC3 is related to Southern oscillation index (SOI) and seasonal PC3 best correlated with wintertime El Nino (NINO34 DJF index). In the southern hemisphere, the recent warming starts first closest to the equator in the 1950s and latest in the southernmost zone in the late 1970s. In the two lowest northern zones (EQ-N24, N24-N44) the warming is significant since the ETCW, and increased warming starts in 1970s, but in two northernmost zones (N44-N64, N64-N90) the cooling after the ETCW delays the start of recent warming until around 1990. All seasons of the northern hemisphere but no season in the southern hemisphere depict a significant ETCW. All the three PCA have almost common PC1 component for the analyzes 1910-2017, i.e., gradual increase of temperature until 1940s, period of declining towards the end of 1950s, a flat phase until the second half of 1970s and steep rise after that. However, the continental PC1 explains only 75.2 % of the variation of the data, while zonal and seasonal PC1s explain 81.7 % and 87.6 % of the corresponding data, respectively.
