Seasonal Prediction of the Variation of the Winter Cloudy Day Frequency in Eastern China Based on the Tropical and Ex-tropical Influence Routes
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摘要: 本文利用中国东部1078个测站1961～2003年逐日云量数据资料，揭示了中国东部冬季阴天频次主模态的时空分布特征，探讨了其形成的两条独立影响途径，并根据影响机理建立了季节预测模型。结果表明：（1）中国东部冬季阴天频次的经验正交函数分解第一模态独立且显著，解释了其59%的总方差。该模态基本呈现空间一致型的分布，表现出显著的年际变率特征。当该模态为正位相时，北太平洋对流层低层存在显著的大尺度反气旋环流异常，其西侧异常偏南风能够将热带海洋的水汽输送至中国东部地区，导致该地区阴天频次增多。（2）前期8月和9月北太平洋副热带持续性海温异常的纬向偶极型分布（NPD）和副热带北大西洋海平面气压9至11月的短期变化（LPA）是该模态的两个主要驱动因子。当NPD中的西极为冷异常时，在局地低层气旋性异常环流的作用下冷海温异常向南平流，发展至热带西太平洋。而当热带西太平洋冷海温异常形成后，皮耶克里斯反馈作用能够发展和维持太平洋“西冷东暖”海表温度异常分布。“西冷东暖”的海表温度异常导致的热带纬向偶极型对流异常能够进一步激发北太平洋“北正南负”的偶极型高度场异常。北部反气旋异常西侧的偏南风有利于水汽输送至中国东部，从而导致阴天频次增多；LPA主要反映的是中高纬准定常罗斯贝波列由秋季至冬季的迅速转变。当副热带北大西洋海表面气压在前秋由正转负时，罗斯贝波列在东北亚上空冬季形成准正压的异常反气旋，其西侧偏南风异常同样能够导致中国东部阴天频次增多。（3）依据以上热带和热带外的两条独立影响机理途径，我们建立了具有物理意义的季节预测模型，利用2004～2013年间数据进行独立预测获得良好预测效果，可供业务预测部门参考和借鉴。Abstract: The temporal-spatial characteristics of the leading mode of winter cloudy day frequency (CDF) across eastern China are revealed via Empirical Orthogonal Function (EOF) analysis of daily cloud cover obtained from 1078 gauge stations in eastern China from 1961 to 2003. We identified the two influence routes of this leading mode, which we used to conduct a physical-motivated empirical model to the seasonal forecast of the winter CDF in eastern China. The results demonstrate that: (1) The first EOF mode of winter CDF explains 59% of the total variance, which is significant and independent of the other modes. This mode primarily demonstrates a homogenous spatial pattern across eastern China with dominating interannual variability. In the positive phase of this mode, a significant lower-level anticyclonic circulation anomaly occurs across the North Pacific. The anomalous southerly wind across the western flank of the anticyclonic could transport water vapor from the tropical ocean to eastern China, resulting in higher CDF. (2) the preceding persistent North Pacific dipole (NPD) pattern during August and September, and lowering of sea level pressure across midlatitude North Atlantic (LPA) from September–November are the two independent drivers for the formation and variation of this mode. The cold SSTA in the western pole of the NPD is advected southward to the tropical western Pacific using the anomalous northerly of the local low-level anomalous cyclone, forming the Bjerknes feedback, which maintains and accelerates the “cold west warm east” zonal SSTA dipole pattern in the tropical Pacific. This tropical Pacific zonal SSTA pattern stimulates zonal convection dipole, which induces a meridional atmospheric teleconnection in the North Pacific. The anomalous North Pacific anticyclones’ southerly is conducive to more CDF in eastern China. The LPA demonstrates the transition of a quasi-stationary Rossby wave train in mid-high latitudes Eurasia from autumn to winter. In winter, the southerly on the west of the barotropic anticyclonic anomaly across Northeast Asia, the terminal of the Rossby wave train, could result in increased CDF in eastern China. (3) Based on these two independent routes of physical mechanisms from both tropics and ex-tropics, a physics-motivated empirical model is conducted, which demonstrates potential independent prediction skill during the ten years of 2004–2013. The results are essential references for operational departments on seasonal prediction.
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图 1 1961～2003年中国东部冬季阴天频次（a）气候态（阴影，单位：d month−1）和（b）标准差（阴影，单位：d month−1）
Figure 1. (a) Climatology (shading, units: d month−1) and (b) standard deviation (shading, units: d month−1) of cloudy winter days over eastern China from 1961 to 2003
图 2 1961～2003年中国东部冬季阴天频次经验正交函数分解第一模态的（a）空间分布（阴影）及（b）标准化主成分（PC1，黑色实线）。图（a）中的等值线和矢量分别为850 hPa高度场（单位：gpm）和风场（单位：m s−1）回归至PC1；图（b）中的柱状为中国东部冬季阴天频次指数，其中2004年至2013年为独立预测期数据投影至主模态空间分布所得到的PC1（黑色虚线）
Figure 2. (a) Spatial pattern (shading) of the first EOF mode of cloudy winter days across eastern China for the period 1961–2003 and its corresponding (b) standardized principal component (PC1, solid black line). (a) Regressed 850 hPa wind (black vector, units: m s−1) and geopotential height (contour, units: gpm) onto the standardized PC1; (b) the bar represents the winter cloudy day frequency index, and the black dotted line represents the projected PC1 using data in the independent forecast period (2004–2013)
图 3 （a）200 hPa准地转流函数异常（阴影，单位：106 m2 s−1）和波活动通量（矢量，单位：m2 s−2），（b）海表温度和地表2 m温度（阴影，单位：°C）及500 hPa高度场（等值线，单位：gpm）、风场（黑色矢量，单位：m s−1）回归至PC1；（c）同（b），但为降水场（阴影，单位：mm d−1）和850 hPa高度场（等值线，单位：gpm）、风场（黑色矢量，单位：m s−1），灰色阴影区域为青藏高原地区。图中字母“A”/“C”代表反气旋/气旋性环流中心；打点区域为回归系数通过90%显著性检验的区域
Figure 3. Regressed (a) quasi-geostrophic streamfunction anomaly (shading, units: 106 m2 s−1) and wave activity flux at 200 hPa (shading, units: m2 s−2), (b) SST and air temperature at 2 m (shading, units: °C), 500 hPa geopotential height (contours, units: gpm), wind (black vectors, units: m s−1) onto the standardized PC1. (c) is the same as (b), but for precipitation (shading, units: mm d−1), 850 hPa geopotential height (contours, units: gpm), wind (black vectors, units: m s−1), the grey shading indicates Tibet Plateau. The letters “A” and “C” indicate the centers of anticyclonic and cyclonic anomalies, respectively. The regression coefficients of dotted areas are statistically significant at the 90% confidence level
图 4 10和11月平均的（a）海表温度和2 m气温，（b）海平面气压与PC1的相关系数分布，（c），（d）同（a），（b），但时间为8和9月平均。打点区域为相关系数通过90%显著性检验的区域
Figure 4. Correlation between October–November mean (a) SST and air temperature at 2 m and (b) sea level pressure and standardized PC1. (c), (d) as in (a), (b), but for August–September mean. The correlation coefficients of dotted areas are statistically significant at the 90% confidence level
图 5 11月减去9月的（a）海表温度和2 m气温、（b）海平面气压与PC1的相关系数分布，（c）、（d）同（a）、（b），但时间为11月减去8月。打点区域为相关系数通过90%显著性检验的区域
Figure 5. Correlation between November minus September (a) SST and air temperature at 2 m, (b) sea level pressure and standardized PC1. (c), (d) as in (a), (b), but for November minus August. The correlation coefficients of dotted areas are statistically significant at the 90% confidence level
图 6 前期（a）8～9月、（b）9～10月、（c）10～11月、（d）11～12月平均海温（阴影，单位：°C）、500 hPa高度场（等值线，单位：gpm）对预测因子北太平洋纬向偶极型海温异常（NPD）的回归，（e）（f）（g）（h）同（a）（b）（c）（d），但为降水（阴影，单位：mm d−1）、850 hPa位势高度（等值线，单位：gpm）、风场（黑色矢量，单位：m s−1）对预测因子NPD的回归，“A”/“C”代表反气旋/气旋性环流中心，灰色阴影区域为青藏高原地区。打点区域为回归系数通过90%显著性检验的区域
Figure 6. Regressed SST (shading, units: °C), 500 hPa geopotential height (contours, units: gpm) during (a) August–September mean, (b) September–October mean, (e) October–November mean, and (d) November–December mean onto predictor North Pacific SSTA dipole (NPD). (e), (f), (g), and (h) are the same as (a), (b), (c), and (d), but for the precipitation (shading, units: mm d−1) 850 hPa geopotential height (contours, units: gpm) and wind field (black vectors, units: m s−1), the letters “A” and “C” indicate the centers of anticyclonic and cyclonic anomalies, respectively, the grey shading indicates Tibetan Plateau. The regression coefficients of dotted areas are statistically significant at the 90% confidence level
图 7 前期（a）9～10月、（b）10～11月、（c）11～12月200 hPa高度场（等值线，单位：gpm）、罗斯贝波波活动通量（棕色矢量，单位：m2 s−2）对预测因子中纬度北大西洋海平面气压负变压（LPA）的回归；（d）、（e）、（f）同（a）、（b）、（c），但为海平面气压（等值线，单位：hPa）、850 hPa风场对预测因子LPA的回归，灰色阴影区域为青藏高原地区。图中字母“A”/“C”代表反气旋/气旋性环流中心，打点区域为高度场回归系数通过90%显著性检验的区域
Figure 7. Regressed 200 hPa geopotential height (contour, units: gpm), wave activity flux (brown vector, units: m2s−2) during (a) September-October mean, (b) October–November (c) November–December mean onto predictor lowering of sea level pressure across midlatitude North Atlantic (LPA). (d), (e), (f) are same as (a), (b), (c), but for sea level pressure (contour, units: hPa), 850 hPa wind (black vector, units: m s−1) onto LPA, the grey shading indicates Tibetan Plateau. The letters “A” and “C” indicate the centers of anticyclonic and cyclonic anomalies, respectively, the regression coefficients of dotted areas are statistically significant at the 90% confidence level of the geopotential height field
图 8 中国东部冬季阴天频次主模态的PC1。黑色实线（虚线）为历史观测（独立预测期投影重建）的PC1，蓝线（红线）为历史回报（独立预测）的PC1
Figure 8. Time series of standardized PC1 of the winter cloudy day frequency over eastern China. The solid black line (dashed line) is from historical (observation reconstructed) PC1, and the blue line (red line) are the simulated (independent forecasted) PC1
图 9 NPD和LPA预测因子影响后期中国东部冬季阴天频次的机理示意图
Figure 9. Schematic diagram for the impacts of NPD and LPA on cloudy winter days across eastern China via tropical and ex-tropical routes, respectively
图 10 1961～2013年间（a）10～11月平均“持续信号”潜在预测因子X1、X2、X3、X4，（b）8～9月平均“持续信号”潜在预测因子X5、NPD、X7、X8、X9以及（c）11月减9月“趋势信号”潜在预测因子Y1、Y2、Y3、LPA、Y5和（d）11月减8月“趋势信号”潜在预测因子Y6、Y7、Y8、Y9与PC1的11年滑动相关。黑色长线为95%置信度的相关系数阈值
Figure 10. Eleven-year sliding correlation of (a) predictor candidates from the Oct and Nov mean persistent signal X1, X2, X3, X4, and (b) predictor candidates from the Aug. and Sep. mean persistent signal. X5, NPD, X7, X8, X9, and (c) predictor candidates from Nov. minus Sep. tendency signal Y1, Y2, Y3, LPA, Y5, and (d) predictor candidates from Nov. minus Aug. tendency signal Y6, Y7, Y8, Y9 with the standardized PC1. The black dotted line is the threshold for correlation coefficient passing the 95% confidence level
表 1 潜在预测因子与阴天频次主成分（PC1）及因子间相关系数
Table 1. The correlation coefficients between the potential predictors and the principal component (PC1) and among the potential predictors
阴天频次主
成分及因子潜在预测因子 X1 X2 X3 X4 X5 X6 X7 X8 X9 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 PC1 0.45 0.43 0.38 0.44 0.59 0.53 0.35 0.42 0.48 0.43 0.38 0.42 0.53 0.39 0.55 0.56 0.52 0.45 X1 1 0 0.53 0.42 0.33 0.07 0.05 0.14 0.14 0.23 0.56 0.26 0.39 0.44 0.30 0.23 0.26 0.36 X2 1 0.10 0.35 0.34 0.41 0 0.35 0.45 0.18 0.05 0.21 0.32 0 0.13 0.33 0.23 0.22 X3 1 0.31 0.27 0.08 0.08 0.15 0.32 0.41 0.40 0.07 0.35 0.30 0.29 0.46 0.14 0.30 X4 1 0.28 0.22 0.03 0.14 0.27 0.13 0.33 0.27 0.76 0.40 0.34 0.17 0.41 0.76 X5 1 0.31 0.18 0.68 0.37 0.38 0.47 0.30 0.39 0.27 0.24 0.35 0.25 0.32 X6 1 0.43 0.42 0.51 0.41 0 0 0.16 0 0.42 0.38 0.23 0.12 X7 1 0.20 0.30 0.33 0.10 0 0.17 0 0.18 0.31 0.07 0.18 X8 1 0.37 0.38 0.15 0.16 0.18 0 0.21 0.37 0.10 0.17 X9 1 0.53 0 0 0.38 0 0.38 0.46 0.39 0.20 Y1 1 0.25 0.10 0.26 0.13 0.59 0.33 0.30 0.06 Y2 1 0.35 0.42 0.49 0.26 0.25 0.19 0.27 Y3 1 0.28 0.58 0.09 0.19 0.38 0.32 Y4 1 0.45 0.32 0.18 0.42 0.85 Y5 1 0.32 0.17 0.38 0.46 Y6 1 0.35 0.43 0.18 Y7 1 0.11 0.07 Y8 1 0.38 注：加粗代表通过90%显著性检验，斜体加粗代表最优预测因子及其与PC1的相关系数 
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