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华南冬季强降水及高、低纬两支波列的协同影响

陈婉玲 李秀珍

陈婉玲, 李秀珍. 2021. 华南冬季强降水及高、低纬两支波列的协同影响[J]. 大气科学, 45(6): 1−15 doi: 10.3878/j.issn.1006-9895.2102.20246
引用本文: 陈婉玲, 李秀珍. 2021. 华南冬季强降水及高、低纬两支波列的协同影响[J]. 大气科学, 45(6): 1−15 doi: 10.3878/j.issn.1006-9895.2102.20246
CHEN Wanling, LI Xiuzhen. 2021. Cooperation of High- and Low-Latitudes Wave Trains in the Occurrence of Extreme Winter Precipitation over South China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(6): 1−15 doi: 10.3878/j.issn.1006-9895.2102.20246
Citation: CHEN Wanling, LI Xiuzhen. 2021. Cooperation of High- and Low-Latitudes Wave Trains in the Occurrence of Extreme Winter Precipitation over South China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(6): 1−15 doi: 10.3878/j.issn.1006-9895.2102.20246

华南冬季强降水及高、低纬两支波列的协同影响

doi: 10.3878/j.issn.1006-9895.2102.20246
基金项目: 国家自然科学基金项目41775043
详细信息
    作者简介:

    陈婉玲,女,1995年出生,硕士研究生,主要从事冬季强降水的研究。E-mail:chenwl26@mail2.sysu.edu.cn

    通讯作者:

    李秀珍,E-mail: lixiuzhen@mail.sysu.edu.cn

  • 中图分类号: P434

Cooperation of High- and Low-Latitudes Wave Trains in the Occurrence of Extreme Winter Precipitation over South China

Funds: National Natural Science Foundation of China (Grant 41775043)
  • 摘要: 采用1979~2015年ERA-Interim再分析资料和中国756个站点的逐日降水观测资料,利用百分位法定义了华南冬季强降水事件,通过K-均值聚类方法分析发现中国冬季强降水的中心主要集中在5个地区:长江中下游地区、华南中西部、华南东南部、淮河流域以及中国西南部。为了揭示华南南部大范围强降水的成因,对比分析了由西南地区自西向东移动至东南沿海的东移型强降水事件与西南地区局地型强降水事件。结果表明,南支槽槽前的暖湿气流与冷空气活动的强弱对峙是触发以及维持这两类强降水的关键。在局地型强降水中,活跃的冷空气活动抑制了南支槽的发展和东扩,强降水局限于西南地区;对于东移型强降水,由于冷空气活动偏弱,南支槽槽前西南暖湿气流东扩,降水落区随之东移。高纬度波列与南亚急流波列的协同作用是影响冷、暖气流相对强弱的关键环流系统。当高纬度波列与南亚急流波列同步发展时,冷、暖空气均较强形成强烈对峙,强降水主要局限在西南地区;当高纬度波列活动超前于南亚急流波列,冷空气活动与南支槽的加深错开,强降水可持续并东移,影响较大范围。
  • 图  1  1979~2015年华南冬季强降水日聚类分布合成(填色,单位:mm d−1)及850 hPa异常风场(箭头,仅画出通过95%信度水平检验部分,单位:m s−1,),图中左上角的YR、CWSC、SESC、HR、SW分别表示长江中下游、华南中西部、华南东南部、淮河流域和中国西南部地区,右上角百分数为该类强降水天数占总强降水天数的百分比

    Figure  1.  Distribution of the composited extreme winter precipitation (shaded, units: mm d−1) and anomalous 850-hPa horizontal wind (vectors, only the result significant at the 95% confidence level is shown, units: m s−1) clustered via K-means method over South China during 1979–2015. YR: Yangtze River valley; CWSC: central and western South China; SESC: southeastern South China; HR: Huaihe River valley; SW: Southwest China. The percentage of total extreme winter precipitation days is shown at the top right of each panel

    图  2  冬季(a–c)局地型和(d–f)东移型强降水事件中降水量水平空间分布随时间演变(填色,单位:mm d−1):(a、d)降水日前一天(−1 d);(b、e)强降水发生当天(0 d);(c、f)降水日后一天(+1 d)

    Figure  2.  Time evolution of the composited precipitation (shaded, units: mm d−1) in (a–c) the local cases and (d–f) the eastward migration cases: (a, d) One day before precipitation day (−1 d); (b, e) heavy precipitation day (0 d); (c, f) one day after precipitation day (+1 d)

    图  3  (a–c)局地型和(d–f)东移型事件中纬向风(箭头;单位:m s−1)和垂直方向上的风(箭头;单位:102 Pa s−1)合成的异常纬向环流和假相当位温(等值线,单位:K)及其异常(填色,单位:K)沿25°N的纬向—垂直剖面演变。黑色箭头表示通过95%信度水平检验

    Figure  3.  Zonal–vertical evolution of the anomalous zonal circulation consisting of zonal wind (vectors; m s−1) and p-vertical velocity (vectors; 102 Pa s−1, , black arrows represent statistically significant above the 95% confidence level) along with the pseudoequivalent potential temperature (black contours, units: K) and its anomalies (shading, units: K) along 25°N in (a–c) the local cases and (d–f) the eastward migration cases

    图  4  (a、c)局地型和(b、d)东移型强降水发生时(a、b)整层积分水汽输送通量异常(箭头,单位:kg m−1 s−1)及其散度(填色,单位:10−5 kg m−2 s−1,只显示事件合成水汽通量大于50 kg m−1 s−1的箭头)分布以及(c、d)700 hPa异常风场(箭头,单位:m s−1)和涡度场(填色,单位:10−6 s−1)分布。黑色箭头及打点区域表示通过95%信度水平检验,AC和C分别代表反气旋和气旋

    Figure  4.  (a, b) Anomalous vertically integrated water vapor fluxes (vectors, units: kg m−1 s−1, only composite water vapor flux larger than 50 kg m−1 s−1 are shown) and their divergence (shading, units: 10−5 kg m−2 s−1), (c, d) anomalous 700-hPa horizontal wind (vectors, units: m s−1) and relative vorticity (shading, units: 10−6 s−1) in the local cases (left column) and eastward migration cases (right column). The locations of anticyclones and cyclones are marked as “AC” and “C,” respectively

    图  5  局地型(左列)和东移型(右列)强降水发生时(a–b)海平面气压(等值线,单位:Pa)及其异常场(填色,单位:Pa),(c–d)850 hPa异常风场(箭头,单位:m s−1)和假相当位温(填色,单位:K),(e–f)500 hPa位势高度(等值线,单位:gpm)及其异常(填色,单位:gpm)分布。黑色箭头及打点区域表示通过95%信度检验

    Figure  5.  Composites of (a–b) the sea level pressure (contours, units: Pa) and its anomaly (shaded, units: Pa), (c–d) anomalous 850-hPa horizontal wind (vectors, units: m s−1) and the pseudoequivalent potential temperature (shading, units: K), and (e–f) 500-hPa geopotential height (contours, units: gpm) and its anomaly (shading, units: gpm) in the local cases (left column) and eastward migration cases (right column). The black arrows and dotted areas represent statistically significant above the 95% confidence level

    图  6  局地型(左列)和东移型(右列)事件中合成的200 hPa波活动通量(箭头,单位:m2 s−2)和准地转流函数(填色,单位:106 m2 s−1)随时间演变:(a、g)−12 d;(b、h)−9 d;(c、i)−6 d;(d、j)−3 d;(e、k)0 d;(f、l)+3 d。黑色箭头及打点区域表示通过95%信度检验

    Figure  6.  Time evolution of anomalous horizontal wave activity fluxes (vectors, units: m2 s−2) and quasigeotrophic streamfunction (shading, unit: 106 m2 s−1) at 200 hPa in (a–f) the local cases and (g–l) the eastward migration cases: (a, g) −12 d, (b, h) −9 d, (c, i) −6 d, (d, j) −3 d, (e, k) 0 d, and (f, l) +3 d. The black arrows and dotted areas represent statistically significant above the 95% confidence level

    图  7  (a)局地型和(b)东移型事件合成的200 hPa波活动通量(箭头,单位:m2 s−2)和准地转流函数(填色,单位:106 m2 s−1)沿20°~40°N平均从−7 d至+3 d的纬向—时间分布。黑色箭头表示通过95%信度水平检验,红色箭头表示波列能量传播的方向,红线表示通过95%信度水平检验

    Figure  7.  Zonal–temporal sections of the 200-hPa anomalous horizontal wave activity fluxes (vectors, units: m2 s−2) and quasigeostrophic streamfunction (shaded units: 106 m2 s−1) averaged over 20°–40°N from −7 d to +3 d in (a) the local cases and (b) the eastward migration cases. The black arrows represent statistically significant above the 95% confidence level, red arrow denotes the direction of propagation, red lines represent statistically significant above the 95% confidence level

    图  8  同图7,但为从−12 d至+3 d沿50°~70°N平均的时间—纬向分布

    Figure  8.  Same as Fig. 7, but for averaged over 50°–70°N from −12 d to +3 d

    图  9  1979~2015年冬季逐日波列指数与200 hPa准地转流函数的相关系数分布,其中填色为局地型对应的高纬度波列,红色等值线为东移型对应的高纬度波列,黑色等值线则是南亚急流波列;红色圆点、红色三角形和黑色正方形分别代表三个波列指数选取的高度中心,仅显示通过95%信度水平检验部分

    Figure  9.  Distribution of the correlation coefficient between the 200-hPa quasigeostrophic streamfunction and the wintertime daily wave train indexes [shaded: IHL (local cases wave train index); red lines: IHM (eastward migration cases wave train index); black lines: ISAJWT (South Asian jet wave train index)] during 1979–2015. Red dots, red triangles, and black squares represent the height center selected for the wave train indexes, only results above the 95% confidence level are shown

    图  10  局地型(左列)、东移型(中间列)高纬度波列和南亚急流波列(右列)指数回归的各气象要素分布:(a、b)海平面气压场(填色,单位:Pa,等值线为重建后的海平面气压)、(c)整层积分水汽输送通量(矢量箭头,单位:kg m−1 s−1)及其散度场(填色,单位:10−5 kg m−2 s−1);(d–f)850 hPa风场(矢量箭头,单位:m s−1,黑色箭头表示通过95%信度水平检验)和假相当位温场(填色,单位:K);(g–i)500 hPa位势高度(填色,单位:gpm,等值线为重建后的位势高度场)。仅显示通过95%信度水平检验部分

    Figure  10.  The regression of (a–b) sea level pressure (shaded, units: Pa, contours: reconstruction of sea level pressure), (c) vertically integrated water vapor fluxes (vectors, units: kg m−1 s−1) and their divergence (shaded, units: 10−5 kg m−2 s−1), (d–f) 850-hPa horizontal wind (vectors, units: m s−1, the black arrows represent statistically significant above the 95% confidence level) and pseudoequivalent potential temperature (shaded, units: K), and (g–i) 500-hPa geopotential height (shaded, units: gpm, contours: reconstruction of geopotential height) on the high-latitude wave train indexes of the local cases (left column) and eastward migration cases (middle column) and South Asian jet (right column) wave train index, respectively. Only results above the 95% confidence level are shown

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出版历程
  • 收稿日期:  2021-05-07
  • 录用日期:  2021-05-07
  • 网络出版日期:  2021-06-03

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