Isolating Tornado Trends: A Physically Informed Time Series Decomposition of the Great Plains and Southeast Tornado Records

Tornado activity in the United States varies across multiple time scales. While several teleconnections are known to modulate atmospheric conditions and influence tornado occurrence, these climate patterns do not fully explain the variability in the tornado record. Long-term trends—specifically, a decline in the Great Plains and an increase in the Southeast—have been reported, but it remains unclear how much of these trends reflect low-frequency variability versus persistent change. This study applies a physically informed, data-driven decomposition framework to isolate long-term tornado trends in the Great Plains and Southeast United States. Dominant periodicities in key teleconnections (ENSO, NAO, PNA, AO, PDO, and AMO) were identified using Fourier analysis and cross-wavelet coherence analysis was used to ensure these teleconnections were coherent with tornado activity before removing re removed from regional tornado counts using multi-seasonal decomposition methods (MSTL and TBATS). Results are consistent with prior findings—showing a decline in Great Plains tornadoes and an increase in the Southeast—but the persistence of these trends after filtering out coherent variability and noise provides robust evidence that the observed trends likely reflect longer-term changes. Additionally, a potential low-frequency cycle in tornado activity is identified, though its full extent remains unresolved due to the limited length of the observational record. The physically informed framework developed here offers a novel approach for isolating trends from variability and noise and may be applied to other climate phenomena.