A new framework for the assessment of potential future disasters caused by typhoons using multi-model ensemble experiments

Abstract. Assessing future changes in typhoon-related hazards is essential for climate adaptation. We develop an event-based storyline framework coupling high-resolution meteorological simulations with river discharge and storm-surge models to quantify future risks under specified warming levels. Using Super Typhoon Hagibis (2019) as a case study, we conduct multi-model, multi-initial-condition ensemble experiments to account for structural model uncertainty and internal variability. Three meteorological models are run with 27-member initial-condition ensembles to drive three river models and two storm-surge models. Experiments cover the present climate and two warming scenarios (+2 K, +4 K). Across ensembles, Hagibis intensifies under warming: precipitation and near-surface winds strengthen, along with lower central pressure relative to present-climate runs. Consistent with these changes, many river basins in eastern Japan exhibit increased discharge, amplifying flood risk, and coastal models indicate larger storm surges. For Typhoon Hagibis, we quantify uncertainties in peak discharge and maximum storm-surge level. The multi-model combination widens uncertainties by sampling structural model differences, thereby spanning a wider range of plausible outcomes. Thus, our multi-model, multi-initial-condition framework provides a more comprehensive assessment of future typhoon-related risks than single-model experiments.