Statistical dependence between tornado reports and climate indices is difficult to diagnose due to several confounding factors. These factors include but are not limited to low human population density, changes in severe weather reporting procedures, short time series, and the public's severe weather awareness and willingness to report tornadoes.

We used a customized stochastic model to diagnose statistical dependence between tornado reports in the United States and indices of ENSO and PDO. The results suggest increased and decreased tornado frequency in the southeastern and central United States, respectively, when El Nino occurs during cold PDO phase or when La Nina occurs during warm PDO phase.

In order to confirm that this statistical dependence represents physical mechanisms, we use an index of tornado-favorable conditions by combining three important conditions for tornadoes: low-level wind shear, deep tropospheric wind shear, and buoyancy. Synthetic soundings from the NCEP/NCAR reanalysis are used to compute the index. Results from the stochastic model are used to guide physical analysis as the index time series' are generated for Reanalysis grid points that lie within regions of statistical dependence. At the conference, we will present results that relate the index time series to large-scale weather patterns.