Empirical estimate of intraseasonal and interannual variability of occurrence of tornadoes

Tornadoes are significant meteorological hazards, causing extensive damage to infrastructure and loss of life. Their small spatial scale (approximately 1km or less), short lifespan (order of 1000s) and, highly nonlinear chaotic behaviour makes their prediction problematic using current operational weather predictions and climate models. Developing methods to overcome these limitations is crucial for providing reliable early warnings and forecasts through civil protection services and determining whether human-induced climate change will affect the frequency and intensity of tornadoes. We estimate the expected occurrence of tornadoes using a set of empirical formulas based on meteorological parameters extracted from the ERA5 reanalysis for the period 2000-2024 and compare these estimates to the actual number of observed tornadoes, as recorded by the Strom Prediction Center (SPC) (https://www.spc.noaa.gov/wcm/#dat) for USA  and the European Severe weather database (ESWD) https://www.essl.org/cms/ for Europe. The formulas incorporate W_MAX (updraft maximum parcel vertical velocity, linked to the Convective Available Potential Energy CAPE), WS₇₀₀ (the wind shear in the lower troposphere), LCL (the lifting condensation level), SRH₉₀₀ (low-level storm relative helicity) and provide a probability for the occurrence of tornadoes (see Ingrosso, et al. https://doi.org/10.5194/nhess-23-2443-2023 for details). Results show a good capability of reproducing the seasonal cycle of tornadoes in the USA and some skill to simulate their interannual variability, with a score depending on season and larger in spring. Results are not satisfactory for tornadoes in Europe. Reasons for this partial failure need a further investigation. This study is carried out with the financial support of ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – NextGenerationEU… Project code CN_00000033, CUP C83C22000560007.