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新疆冬季气温年际异常的主模态及其成因分析 被引量:10

Annual variation of winter temperature and its causes in Xinjiang
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摘要 基于新疆1961—2016年89个观测站冬季平均气温经验正交分解的空间模态,讨论了与各空间模态及其相联系的北半球中高纬度环流特征,结果表明:新疆冬季平均气温的年际异常空间模态分为全区一致类、南北反相类、东西反相类,根据这三类空间模态的正负位相不同分别分为一致偏冷型、一致偏暖型、北冷南暖型、北暖南冷型、东冷西暖型和东暖西冷型等6个空间分布型。新疆冬季平均气温各空间分布型的环流影响因子既表现了极地和中纬度环流相互作用,也有纬圈方向的波列传播的影响。当北半球中纬度西风偏弱,中高纬度环流经向度加大,乌拉尔山地区的高压脊发展和东亚大槽偏深,50°N以南为负高度距平,新疆冬季平均气温一致偏低;反之则一致偏高。北冷南暖型在40°N以北的区域与一致偏冷型的环流特征基本类似,但在中亚至新疆40°N偏南的区域位势高度偏高;北暖南冷型出现时,乌拉尔山负高度距平和东亚大槽偏弱,新疆上空为浅脊控制,新疆南部受脊后的浅槽影响。东冷西暖型和东暖西冷型区别在于中纬度的500 hPa正高度距平中心的位置和700 hPa气流方向。北极涛动(AO)、区域西风指数、乌拉尔山关键区因子、欧亚纬向环流指数、西藏高原-1指数、西藏高原-2指数、斯堪的纳维亚遥相关型指数(SCA)、亚洲区极涡面积指数等8个气候指数都对新疆冬季平均气温产生了重要的影响。 Based on the observed temperature data at 89 meteorological stations and the NCEP-NCAR re-analysis data during the period from 1961 to 2016,this study investigates three major modes of winter temperature over Xinjiang,China through empirical orthogonal function(EOF) method,and the characteristics of associated mid-high latitudes atmospheric circulation in the Northern Hemisphere.It can provide ideas and basis for climate prediction to discuss the possible causes of the abnormal winter temperature in Xinjiang.It reveals that the three major spatial modes of winter temperature in Xinjiang,which include the provincially consistent mode,anticlastic mode between the south and the north and anticlastic mode between the west and the east,could be divided further into 6 spatial distribution patterns as follows the uniformly relative cold(or warm) pattern,North being cold(or warm) and South being warm(or cold) pattern,and East being cold(or warm) and West being warm(or cold) pattern.Further analysis indicates that the atmospheric circulation impact factors on the distribution of the winter average temperature in Xinjiang included the polar and mid-latitude circulation interactions and the wave transmission from the latitude circles.The consistent cold(warm) pattern of winter temperature in Xinjiang was associated with the weakening(strengthening) west wind from the middle latitude in the northern hemisphere,and the strengthening(weakening) longitude circulation in the high latitude,the high pressure ridge development in the Ural Mountains,the deepening East Asia trough the East Asian Trough,the anomaly of negative height in south of 50°N.The difference between the pattern of cold East and warm West and the pattern of warm East and cold West lies in the mid-latitude 500 hPa positive anomaly center and the 700 hPa wind direction.The pattern of cold north and warm south is similar to the circulation feature of the consistent cold pattern in the northern region of 40°N,but higher in the southern region of 40°N from Central Asia to Xinjiang.The pattern of warm North and cold South pattern was associated with the weakening of Ural anomaly and the East Asian Deep Trough,and the control of shallow trough over Xinjiang and the impact of the shallow trough after the ridge in southern Xinjiang.This study revealed the indirect but important influencing factors on winter temperature in Xinjiang from 8 circulation indexes.The difference of the positive and negative phases of the AO index and the area index of the polar vortex in Asia can easily cause the regional variation of the average temperature in winter in Xinjiang,and the greater the absolute value,the more obvious the influence of dominance.When the Tibet Plateau-1 index and the Tibet Plateau-2 index are high,the winter mean temperature in southern Xinjiang tends to be higher.When the SCA index of the key area of Ural Mountains is large,the temperature in the north Xinjiang is easy to be low.When the Eurasian zonal circulation index and the regional westerly index are larger,the winter mean temperature in the western and northern regions of Xinjiang is apt to be higher.
作者 陈颖 贾孜拉.拜山 CHEN Ying;Jiazila BAISHAN(Xinjiang Climate Center, Urumqi 830002, Xinjiang, China)
机构地区 新疆气候中心
出处 《干旱区地理》 CSCD 北大核心 2019年第2期223-231,共9页 Arid Land Geography
基金 中国气象局气候变化专项(CCSF201847) 新疆气象局面上项目(MS201607)资助
关键词 冬季气温 纬向环流 乌拉尔山 winter temperature zonal circulation Ural Mountain
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