Improving the prediction of East Asia summer monsoon precipitation using a Bayesian dynamic linear model

Seasonal prediction of East Asia summer monsoon rainfall (EASMR) 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 EASMR 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 EASMR. 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 EASMR 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 EASMR during 1979 to 2003 (period 1), while from 2004-2017 (period 2) its connection to EASMR 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 ~2000.

A new prediction model was established using the two predictors with Bayesian DLM. The cross-validation method and a 9-yr 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 EASMR and is a promising method for seasonal prediction.