How dissimilar are the large-scale hydroclimatic precursors and predictability of anomalous monthly rainfall in east and west Japan?

Abstract

<p><strong>Abstract: </strong></p><p>The hydroclimatic teleconnections between global Sea Surface Temperature (SST) fields and monthly rainfall for the summer (June to August) and winter (December to February) seasons over east and west Japan (divided along 138°E longitude) are investigated using the concept of Global Climate Pattern (GCP) (Chanda and Maity, 2015). It is established in a recent study that these teleconnections exhibit contrasting features and have different origins - rainfall anomalies over west Japan are associated with SST anomalies in the tropical Pacific and Indian Ocean, whereas those over east Japan are associated with high-latitude SST anomalies (Maity et al., 2020). Moreover, the teleconnections show inter-seasonal and intra-seasonal variations. For instance, the El Niño Modoki (La Niña Modoki) phenomena are found to influence the early summer (winter) rainfall over west Japan. In east Japan, early summer (June) and winter (December) rainfall is associated with positive SST anomaly differences in eastern sub-tropical Pacific and south Pacific respectively. Further, the study establishes that, beyond the traditional teleconnection patterns such as ENSO, El Niño Modoki, other climatic precursors are also instrumental in triggering below- and above- normal monthly rainfall in east and west Japan. The predictive potential of all such identified teleconnection patterns for monthly rainfall variation is assessed using a machine learning approach, Support Vector Regression (SVR) and a hybrid Graphical Modelling/C-Vine copula (GM-Copula) approach. The later technique helps to construct a conditional independence structure among the correlated variables to prune the redundant information in the predictor pool and develop a month-wise prediction model using the pruned predictor sets only. It is observed that the complex association between the predictors and the predictand is better captured by this GM-Copula approach with slightly better prediction performance in summer (R = 0.66 to 0.70) than in winter (R = 0.45 to 0.75) for both east and west Japan. Thus, it is concluded that, establishing the conditional dependence structure of the predictor pool is an important step to resolve the complexity and dimensionality of the model and the proposed model may be recommended for operational forecast of monthly rainfall over east and west Japan. Further details can be found in Maity et al., (2020).</p><p><strong>Keywords:</strong> Rainfall prediction, Hydroclimatic teleconnection, Global climate pattern, Sea surface temperature, Machine learning, SVR, Graphical Model, Copula, Japan.</p><p><strong>