A New Global Tropical Cyclone Model

Tropical cyclones, such as hurricanes and typhoons, are a serious threat to nearly a billion people around the world (1). However, key parts of how these storms form, get stronger, and eventually weaken are yet not fully understood. Uncertainties arise from natural variability, climate change, limited historical data, and inconsistent model projections (2). Moreover, global climate models struggle to simulate the most intense storms, leading to uncertainty in risk assessments.
To address these limitations, stochastic modelling approaches, such as the Imperial College Storm model (IRIS) (3), provide probabilistic cyclone risk assessments. IRIS uses simulations to create many possible storm scenarios, based on both past data and physical principles. This helps estimate the potential risks from cyclones over the long term and each year. However, IRIS still needs improvements, especially for regions like the Western Pacific, where biases such as the high-intensity bias in the South China Sea need to be corrected.
To improve IRIS, one key factor that needs to be included is the El Nino-Southern Oscillation (ENSO), which is a natural climate pattern that affects cyclone formation, strength, and direction. This will be achieved by analysing four ENSO scenarios-El Nino, La Nina, neutral conditions, and a baseline. Representative years for each phase will be selected to train datasets, enabling a more accurate assessment of ENSO's impact on cyclone activity. By comparing IRIS outputs to observational data, the model can be benchmarked.
1. Emanuel, Kerry. (2003). 'Tropical Cyclones'. Annual Review of Earth and Planetary Sciences 31 (Volume 31, 2003)
2. Knutson et al. (2010). 'Tropical Cyclones and Climate Change'. Nature Geoscience 3 (3)
3. Sparks, Nathan, and Ralf Toumi. (2024). 'The Imperial College Storm Model (IRIS) Dataset'. Scientific Data 11 (1)