Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity

A hurricane season needs to be quantified using various metrics. Building upon our previous seasonal hurricane prediction model, here we develop two new statistical models to predict the number of major hurricanes (MH) and accumulated cyclone energy (ACE) in the North Atlantic basin using monthly data from March to May of each year for an early June forecast. The input data include zonal pseudo-wind stress to the 3/2 power, sea surface temperature in the North Atlantic, and, depending on the magnitude of the Atlantic Multidecadal Oscillation index, the Multivariate ENSO index. From 1968-2017, these models have a mean absolute error of 0.96 storms for MH and 31 units for ACE. When tested over an independent period from 1958-1967, the models show a 22% improvement for MH and 16% for ACE over the no-skill metric based on a five-year running average. Both MH and ACE results show consistent improvements over those produced by three other centers and the five-year running average climatological prediction over the period 2000-2017 for MH (2003-2017 for ACE) in a simulated real-time prediction. These improvements vary from 25% to 37% for MH and 15% to 37% for ACE. While most forecasting centers called for a slightly above- average hurricane season in May/June 2017, our models predicted in June 2017 a very active season, in much better agreement with observations.