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Effect of preceding soil moisture-snow cover anomalies around Turan Plain on June precipitation over the southern Yangtze River valley
Atmospheric Research ( IF 4.5 ) Pub Date : 2021-09-10 , DOI: 10.1016/j.atmosres.2021.105853
Kejun He , Ge Liu , Renguang Wu , Sulan Nan , Jingxin Li , Xiaoyuan Yue , Huimei Wang , Xinchen Wei , Rongrong Li

This study investigates precursory signals of the June precipitation over the southern Yangtze River valley (SYRV). It is found that the synergistic anomalies of the Turan Plain soil moisture and northern Iranian Plain snow cover (TPSM-IPSC) during April can modulate the June SYRV precipitation. Through the persistence/memory effect of soil moisture anomalies, lower soil moisture around the Turan Plain-Iranian Plain region can maintain from April to June. Because of drier soil (i.e., lower soil moisture), higher surface air temperature (SAT) appears over the Turan Plain during June. The higher SAT anomaly stimulates anomalous upward motion and associated overlying and downstream atmospheric circulation anomalies through modulating the downstream dispersion of Rossby wave energy. As a part of these atmospheric circulation anomalies, the blocking-like anomaly to the west of the Okhotsk Sea facilitates more June precipitation over the SYRV. Additionally, June SYRV precipitation is significantly correlated with sea surface temperature (SST) anomalies in the tropical eastern Pacific (TEP) during the preceding winter. The TPSM-IPSC can compensate for the defect of prediction using the TEP SST (i.e., ENSO) signal in recent years since the former (latter) shows a strengthened (weakened) relationship with SYRV precipitation recently. Considering jointly the traditional pacific SST and new TPSM-IPSC precursors, we establish a physics-based statistical prediction model, which shows a good skill in predicting June SYRV precipitation.



中文翻译:

图兰平原前期土壤水分-雪盖异常对长江以南地区6月降水的影响

本研究调查了长江南部流域 (SYRV) 六月降水的前兆信号。发现4月图兰平原土壤湿度和伊朗北部平原积雪(TPSM-IPSC)的协同异常可以调节6月的SYRV降水。通过土壤水分异常的持久性/记忆效应,图兰平原-伊朗平原地区周围较低的土壤水分可以在 4 月至 6 月间保持。由于土壤较干燥(即土壤湿度较低),6 月份图兰平原上空出现较高的地表气温 (SAT)。较高的 SAT 异常通过调节罗斯比波能量的下游扩散刺激了异常向上运动和相关的上覆和下游大气环流异常。作为这些大气环流异常的一部分,鄂霍次克海以西的阻塞状异常促进了 SYRV 上空的更多 6 月降水。此外,6 月 SYRV 降水与前一个冬季热带东太平洋 (TEP) 的海面温度 (SST) 异常显着相关。TPSM-IPSC可以弥补近年来利用TEP SST(即ENSO)信号进行预测的缺陷,因为前者(后者)与近期SYRV降水的关系增强(减弱)。结合传统的太平洋海温和新的TPSM-IPSC前兆,我们建立了一个基于物理的统计预测模型,该模型在预测6月SYRV降水方面具有良好的能力。6 月 SYRV 降水与前一个冬季热带东太平洋 (TEP) 的海面温度 (SST) 异常显着相关。TPSM-IPSC可以弥补近年来利用TEP SST(即ENSO)信号进行预测的缺陷,因为前者(后者)与近期SYRV降水的关系增强(减弱)。结合传统的太平洋海温和新的TPSM-IPSC前兆,我们建立了一个基于物理的统计预测模型,该模型在预测6月SYRV降水方面具有良好的能力。6 月 SYRV 降水与前一个冬季热带东太平洋 (TEP) 的海面温度 (SST) 异常显着相关。TPSM-IPSC可以弥补近年来利用TEP SST(即ENSO)信号进行预测的缺陷,因为前者(后者)与近期SYRV降水的关系增强(减弱)。结合传统的太平洋海温和新的TPSM-IPSC前兆,我们建立了一个基于物理的统计预测模型,该模型在预测6月SYRV降水方面具有良好的能力。ENSO) 信号,因为前者(后者)与近期 SYRV 降水的关系呈现增强(减弱)的关系。结合传统的太平洋海温和新的TPSM-IPSC前兆,我们建立了一个基于物理的统计预测模型,该模型在预测6月SYRV降水方面具有良好的能力。ENSO) 信号,因为前者(后者)与近期 SYRV 降水的关系呈现增强(减弱)的关系。结合传统的太平洋海温和新的TPSM-IPSC前兆,我们建立了一个基于物理的统计预测模型,该模型在预测6月SYRV降水方面具有良好的能力。

更新日期:2021-09-20
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