Climate prediction of summer extreme precipitation frequency in the Yangtze River valley based on sea surface temperature in the southern Indian Ocean and ice concentration in the Beaufort Sea

Abstract

Three statistical prediction models for the summer extreme precipitation frequency (EPF) in the middle and lower reaches of the Yangtze River valley (MLYRV) based on the winter sea surface temperature in the southern Indian Ocean (SIO‐SST; Scheme‐SST), the spring sea‐ice concentration in the Beaufort Sea (Scheme‐SIC), and both predictors (Scheme‐SS), are established by using the year‐to‐year increment method. The winter SIO‐SST may affect the SST anomaly in the east of Australia via a teleconnection pattern. The east of Australia SST signal in winter continues until the following summer and then affects the summer EPF in the MLYRV via modulation of the meridional Hadley circulation in the western North Pacific. The positive Beaufort SIC anomaly may result in a negative summer SST anomaly by the increased surface albedo. In response, the atmospheric circulation presents a dipole anomaly distribution in the Beaufort Sea and the Barents Sea. The Arctic dipole anomaly may bring about frequent extreme precipitation in the MLYRV by adjusting the position of the East Asian westerly jet and Eurasian teleconnection pattern. The prediction skill of the summer EPF for Scheme‐SS is higher than that of Scheme‐SST and Scheme‐SIC in the cross‐validation test during 1962–2017 and the independent hindcast during 1992–2017. Scheme‐SS shows a higher prediction skill of the summer EPF than that of Scheme‐SST and Scheme‐SIC, with a time correlation coefficient of 0.62, accounting for 39% of the total variance, of which 35% is the winter SIO‐SST and 4% the spring Beaufort SIC. Scheme‐SS not only shows a rather high predictive ability for the anomalous summer EPF but can also reproduce the increasing trend of extremely heavy precipitation.

New statistical prediction scheme for monthly precipitation variability in the rainy season over northeastern China

“…In this study, predictors were derived only from SST anomalies in the preceding winter. Besides the SST signals, the signals of snow cover and sea ice (SIC) are also used in the climate prediction (Cohen and Saito, 2003; Cohen and Jones, 2011; Liu and Ren, 2015; Yin and Wang, 2017; Tian and Fan, 2019; Dai and Fan, 2020). Some previous studies indicated that snow cover and SIC anomalies in preceding spring could be relate to summer rainfall over NEC (Liu and Yanai, 2002; Xue et al ., 2003; Wu and Kirtman, 2007; Li et al ., 2009; Wu et al ., 2009; Zuo et al ., 2015; Li et al ., 2018).…”

Comparison of moisture sources of summer precipitation in 1998 and 2020 in the middle and lower reaches of Yangtze River basin

“…The tracking and analysis of moisture contribution to precipitation in the MLYRB from different sources will help to reveal the general characteristics of regional hydrological cycle as well as to improve our understanding of the atmospheric driving mechanisms of the heavy precipitation (Liu et al, 2020a; Wei et al, 2012). Previous studies have demonstrated that precipitation over the MLYRB was influenced by multiple circulation backgrounds, such as the phase shifts of the El Niño–Southern Oscillation (ENSO) (Li et al, 2017), the sea surface temperature (SST) variations between the tropical Western Pacific and Indian Ocean (Liu et al, 2019), the fluctuations of Northwest Pacific High (Zhang et al, 2017b), the changes of soil moisture over the Indo‐China Peninsula (Gao et al, 2020), the midlatitude atmospheric disturbances (Li et al, 2017), the North Atlantic Oscillation (NAO) (Liu et al, 2020b), the changes of polar vortex (Tian & Fan, 2020) and even the abnormal snow coverage over the Tibetan Plateau (Dong et al, 2019). In addition to the teleconnection analyses, studies on moisture tracking further revealed that the oceanic moisture contribution from the Arabian Sea, the Bay of Bengal and the Northwestern Pacific, as well as the terrestrial moisture contribution from the Indo‐China Peninsula and the Southern China, are all critical moisture sources forming precipitation in the MLYRB (Chen et al, 2013; Fremme & Sodemann, 2019; Wang et al, 2018).…”

Improving seasonal prediction of summer rainfall over southern China using the BCC_CSM1.1m model‐circulation increment‐based dynamic statistical technique

“…In particular, Fan et al (2008) proposed a physics‐based prediction model for summer precipitation over the middle‐to‐lower reaches of the Yangtze River valley utilizing the year‐to‐year increment approach, which uses the difference between the anomalies of a certain year and the previous year as the predictand. This approach has been proven to amplify the predictable signals by capturing both the quasi‐biennial and interannual signals, and it has considerable skills for predicting precipitation and temperature over several parts of China (Fan et al, 2009; Huang et al, 2022; Liu & Fan, 2012, 2014; Liu et al, 2013; Tian & Fan, 2020), as well as for climate modes that include the EAM, Asian‐Pacific Oscillation, Arctic Oscillation, and Antarctic Oscillation (Fan et al, 2012; Huang et al, 2014; Zhang et al, 2019a, 2019b) not only in statistical forecasting models but also in dynamic statistical models (Dai & Fan, 2021). Additionally, the year‐to‐year increment approach has been shown to have notable skills in decadal predictions of summer precipitation over northern China, as well as for the Pacific Decadal Oscillation and the EAM (Huang & Wang, 2020a, 2020b; Qian et al, 2022).…”