Seasonal prediction of springtime tornado activity in the United States using a hybrid model

Abstract. Tornado activity in the contiguous United States (CONUS) causes fatalities and financial losses every spring, motivating attempts to skillfully predict springtime tornadoes. Such predictions would facilitate decision-making and resource management for both public and private stakeholders. Using ERA5 reanalysis, we analyze five April–May weather regimes (WRs) from 1981–2023, some of which strongly modulate tornado activity. The WR information is incorporated into a hybrid model to predict April–May CONUS tornado activity, including tornado outbreaks (days with > 10 EF-1+ tornadoes). ECMWF seasonal forecasts initialized on 1 April are applied to predict WR frequency, including persistent and non-persistent WRs (lasting ≥ 5 and < 5 consecutive days, respectively). Prediction skill is evaluated using leave-one-year-out cross-validation. Predicted and observed tornado outbreak frequencies are significantly correlated (cc = 0.38). Outbreak predictions are more skillful during the positive phase of the Arctic Oscillation (AO) and Pacific North American pattern (PNA), with a proportion correct of 0.75 and 0.71, respectively. In general, model skill is higher during climate mode phases that favor suppressed tornado activity. This implies that climate modes of low-frequency variability can be used to identify forecasts of opportunity for low tornado activity. SSTs over the North Pacific and North Atlantic may help explain the predictability of tornado activity, specifically a +PNA pattern for years of low tornado activity, but further research is needed to confirm those results. Our study demonstrates the potential for skillful prediction of spring tornado outbreaks using WR forecasts and should be prioritized in future work.