This study focuses on the seasonal forecasting of the June–November frequency of the western North Pacific tropical cyclones (WNP TCF) by combining observations and the North-American multi-model ensemble (NMME) forecasts. We model the interannual variability in WNP TCF using Poisson regression with two sea surface temperature (SST)-based predictors, the first empirical orthogonal function of SST in the Pacific meridional mode (PMM) region (“PSPMM”) after linearly removing the impacts of the El Niño Southern Oscillation, and the SST anomalies averaged over the North Atlantic Ocean (“NASST”). The Poisson regression model trained with the observed WNP TCF and the two predictors exhibits a high skill (correlation coefficient of 0.73 and root mean square error equal to 3.2 TC/year) for the 1965–2016 period. Using SST forecasts by eight models from the NMME as predictors, we can forecast the WNP TCF with promising skill in terms of correlation coefficient (0.63) and root mean square error (3.27 TC/year) for forecasts initialized in June. This study highlights the crucial role played by PMM and NASST in modulating WNP TCF, and suggests the use of PSPMM and NASST as potentially valuable predictors for WNP TCF.
All Science Journal Classification (ASJC) codes
- Atmospheric Science