PDF《of炉he tropical oceans》

110.5°EC121.5°E (Hong and Liu 2012), which is delineated by the box in Fig.?1. Daily precipita- tion within this domain is averaged in order to capture the regional-scale features of rainfall from the climate perspective. Summer season is defined as JuneCJulyCAugust (JJA), when precipitation over the HRV peaks (Fig.?1). The pre- cipitation during these three months also displays the strongest interannual variation, indicating the importance of JJA precipitation to climate variability in this region (Fig.?1). Furthermore, summer precipitation in the HRV is critical to local agriculture and economy, making warm- season rainfall a central problem of climate prediction for this region. Atmospheric circulation fields, i.e. wind patterns, were adopted from the National Center of Environment Predic- tion/National Center of Atmospheric Research (NCEP/ Fig.?1??Climatology (1961C2012) of monthly mean precipitation (gray bars, mm?day?1 ) over the HRV;

the error bars denote one standard deviation of interannual variation of precipitation in each month. The inner plot shows the 1961C2012 climatology of summer (JJA) precipitation in Eastern China. The black box denotes the HRV region NCAR) reanalysis (Kalnay et?al. 1996) during 1961C2012. In this study, both daily and JJA mean circulation were analyzed. In order to explore the potential predictability of rainfall, both synchronized and preceding sea surface tem- perature anomalies (SSTA) were examined. The SST data is from the National Oceanic and Atmospheric Administra- tion (NOAA) Extended Reconstructed SST [ERSST Ver- sion

3 (Smith et?al. 2008)]. 2.2? Rainfall probability framework: finite Normal mixture model To describe the probability distribution of summer rain- fall over the HRV, a rainfall framework based on a three- cluster Normal mixture model was implemented (Li and Li 2013). The advantages of the Normal mixture model lie in its flexibility of distribution shapes, because any smoothed distribution can be approximated by the combination of a finite number of Normals. Thus, it overcomes the limitation of traditional statistical models that requires the subjective selection of predefined distribution kernels and cannot be easily adapted to different climate zones. For example, the Log-Normal distribution tends to better model the rainfall in the subtropical regions, while the Gamma distribution fits tropical precipitation better (Cho et?al. 2004). Such a subjective selection of distribution models can introduce bias to the statistical inference on the HRV heavy rainfall events. The flexibility of Normal mixture model in distribu- tion shape is especially important for HRV summer rain- fall, because daily rainfall displays multi-modal feature that cannot be captured by traditional models with uni-modal distribution kernels. When constructing finite Normal mixture models, choosing the optimal number of clusters is difficult and sometimes controversial (McLachlan and Peel 2000;

Mel- nykov and Maitra 2010;

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