Sub-seasonal tropical cyclone genesis prediction and MJO in the S2S dataset

Subseasonal probabilistic prediction of tropical cyclone (TC) genesis is investigated here using models from the seasonal to subseasonal (S2S) dataset. Forecasts are produced for basin-wide TC occurrence at weekly temporal resolution. Forecast skill is measured using the Brier skill score relative to two no-skill climatological forecasts: an annual mean climatology that is constant through the year and a seasonal climatology that varies monthly through the TC season. Skill depends on model characteristics, lead time, and ensemble prediction design. Most forecasts show skill for week one (days one to seven), when initialization is important. Among the six S2S models examined here, the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the best performance, with skill in the Atlantic, western North Pacific, eastern North Pacific, and South Pacific at week two. Similarly, the Australian Bureau of Meteorology (BoM) model is skillful in the western North Pacific, South Pacific, and northern Australia at week two. Compared to statistical models, S2S model skill scores are higher at week one, comparable at week two, and lower from weeks three to five. The Madden-Julian Oscillation (MJO) modulates observed TC genesis, and there is a clear relationship, across models and lead times, between models’ skill scores and their ability to represent the MJO–TC relation accurately. Skill scores increase with the ensemble size, as found in previous weather and seasonal prediction studies. 17