Review of the Influence and Application of SST Anomaly to Flood Season Precipitation in China

Abstract

The spatial distribution of precipitation anomalies during flood season and characteristics of drought and flood disasters in China are directly affected by the speed and stagnation of the East Asian summer monsoon (EASM). EASM is significantly affected by external forcing such as sea surface temperature, land surface processes, ice and snow cover, and internal dynamic anomalies of atmospheric circulation. The sea surface temperature (SST) anomaly and its evolution have always been important factors for predicting precipitation during the flood season, considering lead time and the strength of precipitation prediction in flood season.Based on the scientific understanding and application of the mechanism of El Niño-southern oscillation (ENSO) cycle and other Ocean SST on the key factors of EASM, the prediction skill of flood season precipitation is reviewed. According to a prediction evaluation spanning over 40 years of historical records, the prediction accuracy for different types of rainfall pattern, the prediction accuracy of rain types in 1981-1990, 1991-2000, 2001-2010, and 2011-2020 is 50%/30%, 60%/30%, 50%/40%, and 70%/50%, respectively. In other words, the prediction of the primary rainfall patterns during the flood season in China is closer to the observation, and the accuracy of predicting spatial distribution patterns of drought and flood has significantly improved. This improvement can be attributed to the in-depth understanding of the impact of SST on EASM activities and enhancements made to dynamic climate models. In the history of flood season prediction, there have been both successful and unsuccessful cases. The years with low prediction accuracy and significant flooding events are as follows: 1983, 1991, 1999, 2003, and 2014. The primary basis for prediction is analyzed, revealing that the limited understanding of the mechanism of SST affecting the EASM had a great impact on the skill of precipitation predictions during the flood season. Among these factors, the influence of different phases of the ENSO cycle, the asymmetry of ENSO's influence, the change in ENSO spatial patterns, and the influence of other local seas, such as the Indian Ocean SST anomaly, all play important roles.The importance of multi-factor and multi-scale synergy theory and application, as well as the technical support of the objectification method for prediction, are emphasized in summarizing causes for low prediction skill cases. Finally, some suggestions for improving future flood season precipitation predictions are put forward, and it is emphasized that the development of a multi-factor and multi-time scale synergistic theory, an objective climate prediction method, and an integrated system for monitoring, predictions and impact assessment will significantly enhance predictions and provide services for flood season precipitation.

Keywords

• flood season
• precipitation prediction
• synergistic effect