Improving the prediction of western North Pacific summer precipitation using a Bayesian dynamic linear model

Seasonal prediction of western North Pacific summer monsoon rainfall (WNPSMR) is in great demand but remains challenging, because the relationships between the Asian monsoon system and precursors are nonstationary and exhibit significant decadal changes. The present study aims to (1) examine decadal variations of the relationships between the WNPSMR and predictors used in previous studies and (2) establish a new prediction model using a Bayesian dynamical linear model (DLM), which is capable of capturing the time-evolving relationships between the predictand and predictors whereas the conventional static linear model cannot. Two predictors were selected previously to predict the WNPSMR. One is the sea level pressure tendency anomalies over the tropical eastern Pacific from late spring to early summer, which represents remote forcing related to ENSO and has a stable effect on WNPSMR throughout the analysis period. The other is the sea surface temperature anomaly difference between the northern Indian Ocean (IO) and the WNP during spring through early summer (called IOWPSST), which denotes local air-sea interaction that affects the WNP subtropical high. Results show that the IOWPSST has strong influence on WNPSMR during 1979-2003 (period 1), while from 2004 to 2017 (period 2) its connection to WNPSMR evidently weakens. This nonstationary relationship is due to the non-persistence of the enhanced WNP subtropical high during period 2, which is associated with the positive-to-negative phase transition of the Interdecadal Pacific Oscillation since similar to 2000. A new prediction model was established using the two predictors with Bayesian DLM. The cross-validation method and a 9-years independent forward-rolling forecast is applied to test the hindcast and actual forecast ability. Results show that the Bayesian DLM has higher hindcast/forecast skill and lower mean square error compared with static linear model, suggesting that the DLM has advantage in predicting WNPSMR and is a promising method for seasonal prediction.