Prediction of accumulated cyclone energy in tropical cyclone over the western North Pacific in autumn

Abstract

Tropical cyclones (TCs) are affected significantly by the climate system and can provide feedbacks. TC activities are important for weather forecasting and climate predictions. Here, we focused on the spatial distribution of accumulated cyclone energy (ACE) and its seasonal prediction. To predict the ACE distribution over the western North Pacific (WNP) in autumn, we established a physical-empirical model. Analyzing 36 years observations (1979–2014) of ACE over the WNP reveals two physically predictable modes. The sea surface temperature (SST) in the southwest Pacific and central Pacific affect the first mode through the low-level circulations. At the same time, the SST in the Gulf of Alaska and the sea-ice concentration in the Beaufort Sea affect the first mode through the circumglobal teleconnection. The development of the eastern Pacific El-Nino and anomalous SST over the North Pacific affect the second mode through the vertical wind shear and low-level circulation. The sea-ice concentration in the Greenland Sea induce an upper-level circulation anomaly over the WNP and affect the second mode. Physically meaningful predictors were selected according to the controlling mechanisms of the two modes. The cross-validated hindcast results demonstrated that the principal components of the two modes are predicted with correlation coefficients of 0.68 and 0.63. Thus, the two modes are predictable. The pattern correlation coefficient skill of the ACE spatial pattern is 0.26, which is significant at the 99% confidence level. The temporal correlation coefficient skill reaches 0.21 over major regions influenced by TCs. To validate the real-time predictability of the model, independent tests were performed on the last three years (2015, 2016 and 2017), and the results show that the pattern correlation coefficients between the observations and the predictions are 0.39, 0.70, and 0.41, respectively.