Previous studies have suggested that the TC activity in the western North Pacific (WNP) and the North Atlantic (NA) has an interannual variability related to the El Niño-Southern Oscillation (ENSO). Additionally, the TC activity in the WNP has also related to the interannual variability of the Indian Ocean (e.g. “the Indian capacitor effect”, which is related to the basin-wide warming/cooling of the Indian Ocean that El Niño/La Niña.). Therefore, it is important to well predict the sea surface temperature (SST) and atmospheric circulation variability responding to it.
We examine the capability of TC seasonal prediction for June-October (i.e. TC season) using a 30-year (1981-2010) re-forecast dataset with JMA/MRI-CPS2. We detect TCs from the re-forecast dataset by an objective algorithm and verify against a besttrack data.
JMA/MRI-CPS2 has a good skill in predicting interannual variability of tropical SSTs and their related atmospheric circulation variability in the WNP: the extension/retreat of the South-east Asian monsoon trough and the decreased/increased vertical wind shear in response to ENSO and the anomalous anticyclonic/cyclonic circulation at lower levels in response to the basin-wide warming/cooling of the Indian Ocean. In the main development region of NA, JMA/MRI-CPS2 is also capable of predicting the anomalous anticyclonic/cyclonic circulation at lower levels and the increased/decreased vertical wind shear during El Niño/La Niña years. This variability induces the interannual variability of the TC activity, and is a source of the predictability for the TC activity such as the TC genesis number and location. The temporal correlation coefficient of the number of TC formation is 0.51 in the WNP and 0.45 in the NA for June-October, predicted from April. Also, the temporal correlation coefficient of accumulated cyclone energy (ACE), which is defined as the sum of the squares of the 6-hourly maximum sustained wind speed in existing TCs, is 0.73 in the WNP and 0.46 in the NA. These results demonstrate the ability of the seasonal prediction for the interannual variability of the TC activity with JMA/MRI-CPS2.