ENSO-Based Predictability of a Regional Severe Thunderstorm Index

Abstract

Here we use coupled climate model forecasts of Niño 3.4 and a regional (Texas, Oklahoma, Arkansas, and Louisiana) tornado environment index (TEI) to examine the modulation of US severe thunderstorm activity by the El Niño-Southern Oscillation (ENSO). The large number of forecast initializations, leads, and ensemble members reduces sampling variability and increases detail in the analysis. The strongest negative relations between TEI and concurrent Niño 3.4 are found in February and March. Both the average of TEI and its spread are larger during cool ENSO conditions, which raises the question of how predictability differs between warm and cool conditions. Predictability is measured using perfect-model skill scores. For a deterministic skill score, which is equivalent to signal-to-noise ratio, larger spread during cool conditions means less predictability. On the other hand, perfect-model probabilistic skill scores are slightly higher (higher predictability) during February and March for cool conditions than for warm conditions due to larger probability shifts.