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谭辉, 朱志伟, 蒋薇, 等. 2023. 基于热带和热带外独立影响途径的中国东部冬季阴天频次的季节预测[J]. 大气科学, 47(3): 683−697. doi: 10.3878/j.issn.1006-9895.2112.21117
引用本文: 谭辉, 朱志伟, 蒋薇, 等. 2023. 基于热带和热带外独立影响途径的中国东部冬季阴天频次的季节预测[J]. 大气科学, 47(3): 683−697. doi: 10.3878/j.issn.1006-9895.2112.21117
TAN Hui, ZHU Zhiwei, JIANG Wei, et al. 2023. Seasonal Prediction of the Variation of the Winter Cloudy Day Frequency in Eastern China Based on the Tropical and Ex-tropical Influence Routes [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(3): 683−697. doi: 10.3878/j.issn.1006-9895.2112.21117
Citation: TAN Hui, ZHU Zhiwei, JIANG Wei, et al. 2023. Seasonal Prediction of the Variation of the Winter Cloudy Day Frequency in Eastern China Based on the Tropical and Ex-tropical Influence Routes [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(3): 683−697. doi: 10.3878/j.issn.1006-9895.2112.21117

基于热带和热带外独立影响途径的中国东部冬季阴天频次的季节预测

Seasonal Prediction of the Variation of the Winter Cloudy Day Frequency in Eastern China Based on the Tropical and Ex-tropical Influence Routes

  • 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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