Tropical cyclones have enormous costs to society through both loss of life and damage to infrastructure. There is good reason to believe that such storms will change in the future as a result of changes in the global climate system and that such changes may have important socioeconomic implications. Here a high-resolution regional climate modeling experiment is presented using the Weather Research and Forecasting (WRF) Model to investigate possible changes in tropical cyclones. These simulations were performed for the period 2001–13 using the ERA-Interim product for the boundary conditions, thus enabling a direct comparison between modeled and observed cyclone characteristics. The WRF simulation reproduced 30 of the 32 named storms that entered the model domain during this period. The model simulates the tropical cyclone tracks, storm radii, and translation speeds well, but the maximum wind speeds simulated were less than observed and the minimum central pressures were too large. This experiment is then repeated after imposing a future climate signal by adding changes in temperature, humidity, pressure, and wind speeds derived from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In the current climate, 22 tracks were well simulated with little changes in future track locations. These simulations produced tropical cyclones with faster maximum winds, slower storm translation speeds, lower central pressures, and higher precipitation rates. Importantly, while these signals were statistically significant averaged across all 22 storms studied, changes varied substantially between individual storms. This illustrates the importance of using a large ensemble of storms to understand mean changes.

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We investigate reliability and component importance in spatially distributed infrastructure networks subject to hazards characterized by large-scale spatial dependencies. In particular, we consider a selected IEEE benchmark power transmission system. A generic hazard model is formulated through a random field with continuously scalable spatial autocorrelation to study extrinsic common-cause-failure events such as storms or earthquakes. Network performance is described by a topological model, which accounts for cascading failures due to load redistribution after initial triggering events. Network reliability is then quantified in terms of the decrease in network efficiency and number of lost lines. Selected importance measures are calculated to rank single components according to their influence on the overall system reliability. This enables the identification of network components that have the strongest effect on system reliability. We thereby propose to distinguish component importance related to initial (triggering) failures and component importance related to cascading failures. Numerical investigations are performed for varying correlation lengths of the random field to represent different hazard characteristics. Results indicate that the spatial correlation has a discernible influence on the system reliability and component importance measures, while the component rankings are only mildly affected by the spatial correlation. We also find that the proposed component importance measures provide an efficient basis for planning network improvements.

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