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洪洁莉, 陈丽娟. 2024. 海河流域盛夏降水预测模型的研发和适用性分析[J]. 大气科学, 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2210.22142
引用本文: 洪洁莉, 陈丽娟. 2024. 海河流域盛夏降水预测模型的研发和适用性分析[J]. 大气科学, 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2210.22142
HONG Jieli, CHEN Lijuan. 2024. Development and Application of the Mid-Summer Precipitation Prediction Model over the Haihe River Basin [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2210.22142
Citation: HONG Jieli, CHEN Lijuan. 2024. Development and Application of the Mid-Summer Precipitation Prediction Model over the Haihe River Basin [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2210.22142

海河流域盛夏降水预测模型的研发和适用性分析

Development and Application of the Mid-Summer Precipitation Prediction Model over the Haihe River Basin

  • 摘要: 海河流域初夏(6月)和盛夏(7~8月)的降水有显著的年代际变化差异,尤其在2002年之后,海河流域初夏和盛夏的年代际变化特征相反,因此有必要分别针对夏季不同阶段建立预测模型。本文基于年际增量的思想,寻找影响海河流域盛夏降水异常的预测因子,以突出年际变化异常的影响信号。前冬欧亚中高纬度关键区域海平面气压指数SLPI(Sea Level Pressure Index)、6月热带中东太平洋海温Niño3指数以及表征厄尔尼诺—南方涛动现象(El Niño–Southern Oscillation,简称ENSO)演变速度的Niño3指数在6月与1月之差的年际增量作为三个关键预测因子,建立回归方程。进一步利用多模式的2022年6月Niño3指数的预报结果代入预测模型,对海河流域2022年盛夏降水进行预报试验。相对各动力气候模式3月起报的盛夏降水异常预测,基于年际增量的回归模型对海河流域盛夏降水异常拟合和回报的准确率更高,尤其是降水显著偏多年份,预测技巧更突出。进一步对预报偏差较大的年份复盘归因发现,前冬海平面气压指数对冬季风和夏季风转换关系的描述可能受到后期春夏热带太平洋和印度洋海温异常演变的干扰。当前冬海平面指数预示的后期海温演变与实际海温演变信号差异较大时,需关注动力模式对临近热带海温尤其是热带印度洋海表温度距平的预报以及海温变化对海河流域盛夏降水的可能影响。

     

    Abstract: There is a significant difference in the decadal variation between early summer (June) and mid-summer (July–August, JA) precipitation over the Haihe River basin; particularly after 2002, the decadal variation characteristics of June and JA precipitation over the Haihe River basin are opposite; thus, it is necessary to establish independent prediction models for June or JA precipitation. Based on advantage of annual increment approach, some important predictors (e.g., the previous winter monsoon, ENSO phase and its developing speed) associated with the annual increment of mid-summer rainfall anomaly over Haihe are chosen using correlation analysis. The difference between the current year and previous years (DY) of the sea level pressure index (SLPI) of key areas in middle and high latitudes of Europe and Asia in the previous winter, the Niño3 index in June, and the tendency between June and January of the Niño3 index as ENSO evolution speed are used as key factors to establish the multivariate linear regression equation. Then, the forecast experiment of mid-summer precipitation over the Haihe River basin in 2022 is performed according to the predicted Niño3 index in June by models. The comparison between the annual increment and climate models results started in March revealed that the former has a high prediction ability, particularly in flooding years. Furthermore, the failure hindcast case is carefully examined through the contribution of each predictor. The main factor is the SLPI, which discloses the relationship between East Asian winter and summer monsoon, which strongly relies on the following tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTa) evolution. Nevertheless, the tropical SSTa, particularly in the western Indian Ocean in June, shows unique features and may disturb the contribution of the SLPI. This implies that the near prediction of SSTa in crucial areas highly related to the Haihe mid-summer rainfall should be paid attention to.

     

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