Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model.

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.

Mechanisms of tropical cyclone response under climate change in the community earth system model.

Unlabelled: Climate change induces a myriad of effects which influences the global tropical cyclone (TC) genesis frequency. Here we explore how North Atlantic and Western Pacific TCs are affected under climate change using a present-day and a future (1% pCO2 scenario) ensemble of high resolution simulations. We find that the number of TCs decreases () in the North Atlantic but increases () in the Western Pacific.

Intercomparison of regional loss estimates from global synthetic tropical cyclone models.

Tropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity.

A globally consistent local-scale assessment of future tropical cyclone risk.

There is considerable uncertainty surrounding future changes in tropical cyclone (TC) frequency and intensity, particularly at local scales. This uncertainty complicates risk assessments and implementation of risk mitigation strategies. We present a novel approach to overcome this problem, using the statistical model STORM to generate 10,000 years of synthetic TCs under past (1980-2017) and future climate (SSP585; 2015-2050) conditions from an ensemble of four high-resolution climate models.

Estimation of global tropical cyclone wind speed probabilities using the STORM dataset.

Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods.

Generation of a global synthetic tropical cyclone hazard dataset using STORM.

Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model).