Frontiers in Earth Science (Feb 2022)
Medium-Range Predictability of Boreal Summer Western North Pacific Subtropical High and Its ENSO Modulation
Abstract
In boreal summer, variations of intensity and location of the western North Pacific subtropical high (WNPSH) have significant impacts on weather and climate in East Asia. In this study, the medium-range prediction of WNPSH is comprehensively evaluated with various scores based on reforecast data of the National Centers for Environmental Prediction - Global Ensemble Forecast System, and the predictability source of the WNPSH medium-range forecasting is further analyzed by examining how well the model can reproduce the modulation of El Niño-Southern Oscillation (ENSO) on WNPSH as observed. The results show that this system has a systematic bias in the WNPSH forecasts, mainly manifested by the weak strength and the southeastward shifted position, and such a bias further increases with lead time. Effective prediction skills of WNPSH are 10–11 days for its intensity and area, 7 days for its ridge line, but only 1–3 days for its western boundary ridge point in terms of different scores, respectively, which can be improved through developing a bias correction method of prediction. It is demonstrated that the medium-range predictability of WNPSH is mainly originated from ENSO and its significant lagged effects on WNPSH well revealed in observation can be realistically reproduced by this system within the effective prediction lengths. A strong ENSO modulation of the WNPSH prediction skills has been clearly found in terms of the different indices, which depends on the ENSO’s developing and decaying phases. The intensity and area of WNPSH are usually highly predictable due to the ENSO effect being reproduced by the system well while the location indices of WNPSH have relatively low predictability, which are mainly affected by internal variability and difficultly captured by the model. Predictability analysis of WNPSH as modulated by ENSO shows good potential for medium-range forecasting with high skills.
Keywords
