An Objective Method for Defining Meiyu Onset in Lower Reaches of the Yangtze River Basin

• Published in: Journal of Meteorological Research Vol. 36; no. 6; pp. 841 - 852
• Main Authors: Wang, Wei, Li, Tim, Xin, Fei, Zhu, Zhiwei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environ-mental Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China%Key Laboratory of Cities Mitigation and Adaptation to Climate Change in Shanghai, Shanghai 200030, China; Shanghai Climate Center, Shanghai 200030, China%Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environ-mental Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China; Minhang Meteorological Bureau, Shanghai 201199, China%Department of Atmospheric Sciences, School of Ocean and Earth Science and Technology, University of Hawaii,Honolulu, Hawaii HI96822, USA
• Subjects: Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics and Environmental Physics; Meteorology; Original Paper; Meiyu onset index; a linear regression model; interannual variability; Pacific—Japan pattern; Pacific–Japan pattern
• ISSN: 2095-6037; 2198-0934
• DOI: 10.1007/s13351-022-2069-3

Meiyu is an important climate phenomenon in East Asia, and predicting its onset is critical for local community. Traditionally, the onset of Meiyu is determined by regional operational meteorological centers with some arbitrary criteria. In this study, an objective Meiyu onset index (MOI) is constructed based on large-scale atmospheric conditions such as temperature and relative humidity over the lower reaches of the Yangtze River basin (LYRB). This objectively determined MOI is in good agreement with an integrated area-weighted onset index provided by regional climate centers. A composite analysis is further carried out to reveal large-scale circulation characteristics associated with an early and a late onset group. A La Niña like sea surface temperature (SST) condition in the Pacific and enhanced convection in Philippines are favorable precursory conditions for the early onset. Accompanied with the tropical signals are a Pacific—Japan (PJ) pattern in June and an anomalous anticyclone near Taiwan. Southerly anomalies to the west of the anticyclone transports high mean moisture northward, favoring the onset of Meiyu in LYRB. A linear regression model is constructed for the MOI forecast with three independent predictors. With 1981–2010 as a training period, the reconstructed MOI time series is able to capture the early and late onset years quite well. An independent forecast for the period of 2011–2020 shows a reliable skill. The correlation between the objectively determined MOI and the forecasted date is 0.6, exceeding the 95% confidence level. The newly developed MOI and the regression model can be easily implemented to operational centers for real-time application.