Circulation Characteristics of Winter Rainfall Interannual Variations over South China and Their Response to Preceding Sea Surface Temperature Anomalies
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摘要: 利用我国160站降水观测资料、NCEP/NCAR再分析资料以及英国哈德莱中心的海表面温度资料,分析了华南冬季(12月至次年2月)降水年际变化的大气环流特征以及对前期海面温度(Sea Surface Temperature, SST)异常的响应。结果表明,东亚高空急流异常偏南(偏北),东亚大槽减弱(增强),天气瞬变扰动和南支槽加强(减弱),来自孟加拉湾和南海的西南风在华南形成异常辐合(辐散),从而有利于该地区降水异常偏多(偏少)。ENSO型SST异常不能完全解释华南降水异常年南支槽和低层环流特征,进一步研究表明,导致华南降水异常的南支槽和低层风场变化与热带印度洋和赤道西太平洋SST异常关系更为密切。由前期热带印度洋和赤道西太平洋构建的SST指数和华南冬季降水相关达到0.44,两者相关系数在SST指数超前1个月时达到最大,对华南冬季降水具有一定潜在预报意义。Abstract: The characteristics of the interannual variability of winter (December–February) precipitation over South China and their association with atmospheric and preceding ocean conditions are analyzed using the observed precipitation data from 160 surface meteorological stations in China, the NCEP/NCAR reanalysis dataset, and the sea surface temperature (SST) dataset from the Met Office Hadley Center. The precipitation tends to be more (less) than the climatology under a southward (northward) shift of the East Asian jet stream, the weakened (strengthened) East Asian trough, and the enhanced (attenuated) transient eddy. The southwesterly winds from the Bay of Bengal and the South China Sea in the front of the southern branch trough favor more precipitation over South China. Further analysis suggests that the preceding SST anomalies (SSTAs) over the tropical Indian Ocean and western tropical Pacific in November have a closer relationship with the southern branch trough and low-level southwesterly winds associated with the variation in precipitation over South China, which could not be well explained by the ENSO-like SSTAs. The correlation coefficient between precipitation over South China and the derived index from the preceding SSTA is 0.44 and reaches a maximum when the SSTA index leads precipitation anomalies by approximately one month, which may act as a potential precursor for wintertime precipitation predictions.
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图 3 合成的降水偏多年(左列)和偏少年(右列)的(a、c)200 hPa纬向风距平(单位:m s−1),(b、d)500 hPa高度距平场(等值线,单位:dagpm)和波作用通量(箭头,单位:m2 s−2)。阴影超过95%置信度水平检验
Figure 3. (a, c) Composites of 200-hPa zonal wind anomalies (units: m s−1), (b, d) 500-hPa geopotential height anomalies (contours, units: dagpm) and the corresponding wave activity flux (vectors, units: m2 s−2) for wet years (left panel) and dry years (right panel). The shadings are significantly above the 95% confidence level
图 4 合成的降水偏多年(左列)和偏少年(右列)的(a、c)200 hPa瞬变波能量距平(单位:m2 s−2)和(b、d)500 hPa垂直速度(单位:10−2 Pa s−1)分布(阴影为超过95%置信度水平检验)
Figure 4. Composites of (a, c) transient eddy kinetic energy anomalies (units: m2 s−2) at 200 hPa and (b, d) 500-hPa vertical velocities (unit: 10−2 Pa s−1) for wet years (left panel) and dry years (right panel).The shadings are the areas significant above the 95% confidence level
图 6 合成的(a)前期11月[矩形区域分别为热带印度洋(20°S~10°N,60°E~100°E)和赤道西太平洋地区(10°S~20°N,110°E~140°E)]和(b)同期冬季降水偏多年海面温度(Sea Surface Temperature,SST,单位:°C)分布(阴影为超过95%置信度水平检验);(c、d)同(a、b),但为降水偏少年
Figure 6. Distributions of the sea surface temperature (SST, unit: °C) in (a) the previous November [rectangles represent the areas of the tropical Indian Ocean (20°S–10°N, 60°–100°E) and the western tropical Pacific (10°S–20°N, 110°E–140°E)] and (b) the winter mean SSTA for wet years (shadings are the areas significantly above the 95% confidence level); (c, d) same as (a, b), but for dry years
图 7 前期11月Niño3.4指数(左列)和INWP指数(右列,INWP定义为前期11月热带印度洋和西太平洋海温距平的差值)回归的冬季(a、c)500 hPa高度场(单位:dagpm,阴影为超过95%置信度水平)和(b、d)850 hPa风场(单位:m s−1,阴影为经向风超过95%置信度水平)
Figure 7. Regression maps of the winter mean (a, c) 500-hPa geopotential height (units: dagpm, shadings are significantly above the 95% confidence level) and (b, d) the 850-hPa wind speed (units: m s−1, shadings are the meridional wind speed significantly above the 95% confidence level) onto the preceding November Niño3.4 index (left panel) and the INWP index (right panel, INWP index is defined as the difference of SSTA over tropical Indian ocean and western tropical Pacific in preceding Nov)
图 8 降水(a)偏多年和(b)偏少年OLR距平分布(单位:W m−2)合成,(c)前期11月INWP指数回归的OLR分布。阴影为经向风超过95%置信度水平检验
Figure 8. Composites of OLR (unit: W m−2) for (a) wet years and (b) dry years and (c) the regression pattern of OLR onto the preceding November INWP index. Shadings are the areas significantly above the 95% confidence level
图 9 (a)构建的INWP海温指数和华南冬季降水的超前滞后相关(虚线分别为95%和99%信度水平),(b)前期11月INWP指数(实线)、Niño3.4指数(虚线)和华南冬季降水的窗口为15 a的滑动相关系数
Figure 9. (a) Lead-lag correlation of the INWP index and winter rainfall over South China (dashed lines represent the 95% and 99% confidence levels) and (b) running correlation between the INWP index (solid line), Niño3.4 index (dashed line) in the preceding November and winter rainfall over South China using a 15-a running window
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