Abstract
Hansen et al. found patterns of vertical wind shear, relative humidity (RH), and nonlinear interactions between the Madden–Julian oscillation and El Niño–Southern Oscillation that impact subseasonal Atlantic TC activity. We test whether these patterns can be used to improve subseasonal predictions. To do this we build a statistical–dynamical hybrid model using Navy-ESPC reforecasts as a part of the SUBX project. By adding and removing Navy-ESPC reforecasted values of predictors from a logistic regression model, we assess the contribution of skill from each predictor. We find that Atlantic SSTs and the MJO are the most important factors governing subseasonal Atlantic TC activity. RH contributes little to subseasonal TC predictions; however, shear predictors improve forecast skill at 5–10-day lead times, before forecast shear errors become too large. Nonlinear MJO–ENSO interactions did not improve skill compared to separate linear considerations of these factors but did improve the reliability of predictions for high-probability active TC periods. Both nonlinear MJO–ENSO interactions and the subseasonal shear signal appear linked to PV streamer activity. This study suggests that correcting model shear biases and improving representation of Rossby wave breaking is the most efficient way to improve subseasonal Atlantic TC forecasts.
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