Tropical cyclones are prone to cause fatalities and damages reaching far into billions of US Dollars. There is evidence that these events could intensify under ongoing global warming, and accordingly disaster prevention and adaptation strategies are necessary. We apply Pseudo-Global Warming PGW as a computational cost-efficient alternative to conventional long-term modeling, enabling the assessment of historical events under future storylines.
Not many studies specifically assess the sensitivity of PGW in the context of short-term extreme events in the United States. In an attempt to close this gap, this study explores the sensitivity of hurricane intensity to different PGW configurations, including a purely thermodynamic, a dynamic, and a more comprehensive modulation of initial and boundary conditions using the Weather and Research and Forecasting Model WRF.
WRF was set up in a two-way nesting framework using domains of 25 and 5 km spatial resolution. Results show that high uncertainties exist between the thermodynamic and dynamic approaches, whereas the deviations between the dynamic approach and the comprehensive variable modulation are low.
Hurricanes modeled under the thermodynamic approach tend toward higher intensities, whereas the perturbation of wind under the dynamic approach may impose unwanted effects on cyclogenesis, for example due to increased vertical wind shear. The highest sensitivity, however, stems from the selected CMIP6 model.
We conclude that PGW studies should thoroughly assess uncertainties imposed by the PGW scheme, similar to those imposed by model parameterizations. All simulation results suggest an increase in maximum wind speeds and precipitation for the high impact model and the ensemble mean. An unfolding of the inspected events in a warmer world could therefore exacerbate the impacts on nature and society.
