| 秦岭南北降雪异常空间模态识别及其影响因素分析 |
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| 引用本文： | 李双双,何锦屏,段克勤,任涛涛,延军平.秦岭南北降雪异常空间模态识别及其影响因素分析[J].地理科学进展,2023,42(1):161-172. |
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| 作者姓名： | 李双双 何锦屏 段克勤 任涛涛 延军平 |
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| 作者单位： | 陕西师范大学地理科学与旅游学院，西安 710119 |
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| 基金项目： | 国家自然科学基金项目(42171095);国家自然科学基金项目(41877519);国家自然科学基金项目(41771030);国家自然科学基金项目(41701592) |
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| 摘 要： | 识别降雪异常空间模态，明晰降雪异常的影响因素，对理解中国南北过渡带气候变化规律具有重要的实践意义。论文基于1970—2020年逐日气象数据，辅以湿球温度动态阈值法、经验正交分解法等气候诊断方法，对秦岭南北冷季(11月—次年5月)降雪异常空间模态进行识别，探讨了不同主导模态与海气异常的相关关系。结果表明：(1)秦岭南北冷季降雪异常存在2个主导模态。第1模态为“全区一致型”，降雪异常偏强区分布于关中平原、秦岭山地、汉江谷地和大巴山区东段；第2模态为“山地主导下降型”，反映山地降雪异常对气候变化的敏感性；(2)在时间变化上，第1模态以年际波动为主，20世纪90年代中期后，空间模态多处于负相位，即全区一致降雪偏少；第2模态以年代转折为主，近期空间模态多处于正相位，即山地降雪异常偏少；(3)在影响因素上，第1模态降雪异常与1月中高纬度500 hPa欧亚遥相关波列相关，第2模态降雪异常与冬季赤道中东太平洋海温异常密切相关。研究将降雪异常格局与环流异常机制组合研究，可为理解中国南北过渡带降雪异常预警信号提供理论基础。
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| 关 键 词： | 气候变化 降雪 年代际变化 时空分析 秦岭南北 |
| 收稿时间： | 2022-06-17 |
| 修稿时间： | 2022-09-19 |
Identifying the spatiotemporal pattern of snowfall and influencing factors in the south and north of the Qinling Mountains |
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| LI Shuangshuang,HE Jinping,DUAN Keqin,REN Taotao,YAN Junping.Identifying the spatiotemporal pattern of snowfall and influencing factors in the south and north of the Qinling Mountains[J].Progress in Geography,2023,42(1):161-172. |
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| Authors: | LI Shuangshuang HE Jinping DUAN Keqin REN Taotao YAN Junping |
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| Institution: | School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China |
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| Abstract: | Snowfall is an important component of hydrological cycle, which could be used as an indicator of regional climate change. Identifying the variations of snowfall and its influencing factors is essential for snow disaster prevention and water resource management. Based on the daily data of the 72 meteorological stations in the south and north of the Qinling Mountains from 1970 to 2020, wet bulb temperature dynamic threshold method and empirical orthogonal function (EOF) were used to investigate the spatiotemporal variations in dominant modes of cold-season (from November in a given year to the following May) snowfall in the south and north of the Qinling Mountains. This study also explored the possible mechanism for the interdecadal change of dominant modes of snowfall. The results are as follows: 1) The contribution of the first two EOF leading vectors of the snowfall anomalies was 70.8% during the cold season in the south and north of the Qinling Mountains. The positive phase of the first leading mode (EOF1) was mainly characterized by positive snowfall anomalies in the whole study area. Spatially, areas with more anomalous snowfall were distributed in the Guanzhong Basin, as well as the eastern Qinling Mountains, the Daba Mountains, and the Hanjiang River Basin. In correspondence to the positive phase of the second leading mode (EOF2), decreased snowfall occurred in the Qinling and Daba Mountains, whereas increased snowfall occurred in the basins. It indicates that the response of snowfall anomalies to climate change was more sensitive in the mountainous areas. 2) For temporal variations, the EOF1 was primarily characterized by a decreasing tendency with inter-annual variation. After the mid-1990s, more years were in the negative phase, which implies less snowfall over the whole region. The EOF2 mainly exhibited decadal variations and a continuous declining pattern of snowfall in the mountainous areas after the mid-1990s. 3) From the perspective of circulation mechanism, the positive phase of the EOF1 was primarily affected by synoptic-scale wave activates in January over the midlatitudes of Europe-Asian continent. The variability of synoptic-scale wave activity was affected by the anomalous low over Siberia, which led to the consistently less anomalies of snowfall in the whole study area. The positive phase of the EOF2 revealed that snowfall anomalies were closely associated with the negative sea surface temperature anomalies in winter over the central and eastern Pacific. The findings highlight that the combined spatial pattern of snowfall anomalies and its circulation mechanism help explore the precursors of snowfall anomalies. |
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| Keywords: | climate change snowfall interdecadal variability spatiotemporal analysis south and north of the Qinling Mountains |
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