Hurricanes that haven’t happened, yet: Identifying unprecedented tropical cyclone scenarios

Abstract. Tropical cyclones (TCs) can be unprecedented in many dimensions and can result in disasters when they are unforeseen. We conduct an intercomparison of four TC databases to identify plausible synthetic events that would exceed observational records, and argue that these provide robust and evidence-based scenarios for disaster management use. We compare datasets produced by two statistical TC track models and a newly published TC track hindcast archive from numerical weather predictions: STORM (n = 712,800), IRIS (n= 472,162), and WATTCH (n= 36,793). For all six TC basins, we explore how each dataset characterises unprecedented extreme events in terms of lifetime maximum intensity, 24 h changes in wind speed, monthly frequency of Category 4 and 5 storms, and latitude at first landfall. We assess how each dataset represents the basin-level observational record from IBTrACS by conducting a series of fidelity tests (mean, standard deviation, kurtosis, and skewness) to assess whether their most extreme events could be considered plausible in the current climate. Between 50 % and 89 % of dataset-basin combinations pass at least 2/4 fidelity tests if we include where models indicate underestimation. From this, we identify several hundreds of plausible simulated TCs that exceed historical records in different ways. Where datasets show good fidelity, we illustrate the potential use of these datasets by extracting unprecedented scenarios such as a Category 5 TC hitting southern Madagascar or a TC making landfall on the city of Xai-Xai in Mozambique south of the country's most southerly landfall. Based on this work, we underscore an opportunity for disaster management practitioners to access unprecedented TC scenarios relevant to their work and that would be both robust and imaginative, going beyond current practice.