SPIN (v1.0): A Spontaneous Synthetic Tropical Cyclone Model Empowered by NeuralGCM for Hazard Assessment

Abstract. A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. A key advantage of SPIN over previous synthetic TC models is that it avoids the assumption of independence between TCs, while enabling two-way interactions between synthetic TCs and their ambient environment. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamic TC intensity model to estimate their intensity evolutions based on the large-scale environment. SPIN reproduces the observed climatology of TC activity, including interannual variability, seasonal cycle, genesis, tracks, and lifetime maximum intensity distributions. It also faithfully reproduces the observed return periods of landfall intensity across different regions, enabling its future application to TC risk assessment. Beyond individual TC events, SPIN demonstrates improved skills in representing multiple tropical cyclone events (MTCEs), including their interannual variability, peak concurrent TC count per cluster, and the spatial relationship between consecutive TCs. By circumventing the independent TC assumption and allowing for two-way TC-environment interactions, SPIN opens new potential for assessing compound hazards like MTCE and beyond.