Skillful Prediction of Monthly Major Hurricane Activity in the North Atlantic with Two‐way Nesting. Issue 15 (7th August 2019) - Gao, Kun; Chen, Jan‐Huey; Harris, Lucas; Sun, Yongqiang; Lin, Shian‐Jiann
Abstract: We investigate the monthly prediction of North Atlantic hurricane and especially major hurricane activity based on the Geophysical Fluid Dynamics Laboratory High‐Resolution Atmospheric Model (HiRAM). We compare the performance of two grid configurations: a globally uniform 25‐km grid and the other with an 8‐km interactive nest over the tropical North Atlantic. Both grid configurations show skills in predicting anomalous monthly hurricane frequency and accumulated cyclone energy. Particularly, the 8‐km nested model shows improved skills in predicting major hurricane frequency and accumulated cyclone energy. The skill in anomalous monthly hurricane occurrence prediction arises from the accurate prediction of zonal wind shear anomalies in the Main Development Region, which in turn arises from the sea surface temperature anomalies persisted from the initialization time. The enhanced resolution on the nested grid permits a better representation of hurricanes and especially intense hurricanes, thereby showing the ability and the potential for prediction of major hurricanes on subseasonal timescales. Plain Language Summary: This study provides the first attempt to predict hurricane and especially major hurricane activity over a month using a global atmospheric model. We highlight the performance of a two‐way‐nested grid configuration, in which the model resolution is locally enhanced in the tropical North Atlantic. Such configuration is a computationally affordable way to permit better simulation of intense hurricanes and demonstrates promising skills in predicting the occurrence of major hurricanes and their wind energy over a month. Key Points: For the first time prediction skill on anomalous monthly hurricane and major hurricane activity is demonstrated in a dynamical model The 8‐km nest better resolves intense hurricanes and hence better predicts monthly major hurricane occurrence and accumulated cyclone energy