This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici-Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992-2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, par- ticularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geo- graphical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs' occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI. © 2011 American Meteorological Society.
All Science Journal Classification (ASJC) codes
- Atmospheric Science
Alessandri, A., Borrelli, A., Gualdi, S., Scoccimarro, E., & Masina, S. (2011). Tropical cyclone count forecasting using a dynamical seasonal prediction system: Sensitivity to improved ocean initialization. Journal of Climate, 24(12), 2963 - 2982. https://doi.org/10.1175/2010JCLI3585.1