Tropical cyclones (TCs) in their most intense expression (hurricanes or typhoons) are the main natural hazards known to humankind. The impressive socioeconomic consequences for countries dealing with TCs make our ability to model these organized convective structures a key issue to better understanding their nature and their interaction with the climate system. The destructive effects of TCs are mainly caused by three factors: strong wind, storm surge, and extreme precipitation. These TC-induced effects contribute to the annual worldwide damage of the order of billions of dollars and a death toll of thousands of people. Together with the development of tools able to simulate TCs, an accurate estimate of the impact of global warming on TC activity is thus not only of academic interest but also has important implications from a societal and economic point of view. The aim of this article is to provide a description of the TC modeling implementations available to investigate present and future climate scenarios. The two main approaches to dynamically model TCs under a climate perspective are through hurricane models and climate models. Both classes of models evaluate the numerical equations governing the climate system. A hurricane model is an objective tool, designed to simulate the behavior of a tropical cyclone representing the detailed time evolution of the vortex. Considering the global scale, a climate model can be an atmosphere (or ocean)-only general circulation model (GCM) or a fully coupled general circulation model (CGCM). To improve the ability of a climate model in representing small-scale features, instead of a general circulation model, a regional model (RM) can be used: this approach makes it possible to increase the spatial resolution, reducing the extension of the domain considered. In order to be able to represent the tropical cyclone structure, a climate model needs a sufficiently high horizontal resolution (of the order of tens of kilometers) leading to the usage of a great deal of computational power. Both tools can be used to evaluate TC behavior under different climate conditions. The added value of a climate model is its ability to represent the interplay of TCs with the climate system, namely two-way relationships with both atmosphere and ocean dynamics and thermodynamics. In particular, CGCMs are able to take into account the well-known feedback between atmosphere and ocean components induced by TC activity and also the TC–related remote impacts on large-scale atmospheric circulation. The science surrounding TCs has developed in parallel with the increasing complexity of the mentioned tools, both in terms of progress in explaining the physical processes involved and the increased availability of computational power. Many climate research groups around the world, dealing with such numerical models, continuously provide data sets to the scientific community, feeding this branch of climate change science.
Fatalism about natural disasters hinders action to prepare for those disasters, and overcoming this fatalism is one key element to preparing people for these disasters. Research by Bostrom and colleagues shows that failure to act often reflects gaps and misconceptions in citizen’s mental models of disasters. Research by McClure and colleagues shows that fatalistic attitudes reflect people’s attributing damage to uncontrollable natural causes rather than controllable human actions, such as preparation. Research shows which precise features of risk communications lead people to see damage as preventable and to attribute damage to controllable human actions. Messages that enhance the accuracy of mental models of disasters by including human factors recognized by experts lead to increased preparedness. Effective messages also communicate that major damage in disasters is often distinctive and reflects controllable causes. These messages underpin causal judgments that reduce fatalism and enhance preparation. Many of these messages are not only beneficial but also newsworthy. Messages that are logically equivalent but are differently framed have varying effects on risk judgments and preparedness. The causes of harm in disasters are often contested, because they often imply human responsibility for the outcomes and entail significant cost.
Snow- and ice-related hazardous processes threaten society in tropical to high-latitude mountain areas worldwide and at highly variable time scales. On the one hand, small snow avalanches are recorded in high numbers every winter. On the other hand, glacial lake outburst floods (GLOFs) or large-scale volcano–ice interactions occur less frequently but may evolve into destructive process chains resulting in major disasters. These extreme examples document the huge field of types, magnitudes, and frequencies of snow- and ice-related hazardous processes. Mountain societies have learned to cope with natural hazards for centuries, guided by personal experiences and oral and written tradition. Historical records are today still important as a basis to mitigate snow- and ice-related hazards. They are complemented by a broad array of observation and modeling techniques. These techniques differ among themselves with regard to (1) the type of process under investigation and (2) the scale and purpose of investigation. Multi-scale monitoring and warning systems for snow avalanches are in operation in densely populated mid-latitude mountain areas. They build on meteorological and snow profile data in combination with a large pool of expert knowledge. In contrast, ice-related processes such as ice- or rock-ice avalanches, GLOFs, or associated process chains cause damage less frequently in space and time, so that societies are less well adapted. Even though the hazard sources are often far from the society—making field observation challenging—flows travelling for tens of kilometers sometimes impact populated areas. These hazards are strongly influenced by climate change–induced glacier and permafrost dynamics. On the regional or national scale, the evolution of such hazards has to be monitored at short intervals through aerial and satellite imagery and terrain data, employing geographic information systems (GIS). Known hazardous situations have to be monitored in the field. Physical models—applied either in the laboratory or at real-world sites—are employed to explore the mobility of hazardous processes. Since the 1950s, however, computer models have increasingly gained importance in exploring possible travel distances, impact areas, velocities, and impact forces of events. While simple empirical-statistical approaches are used at broad scales in combination with GIS, advanced numeric models are applied to analyze specific case studies. However, the input parameters for these models are uncertain so that (1) the model results have to be validated with observations and (2) appropriate strategies to deal with the uncertainties have to be applied before using the model results for hazard zoning or dimensioning of protective structures. Due to rapid atmospheric warming and related changes in the cryosphere, hazard situations beyond historical experiences are expected to be increasingly relevant in the future. Scenario-based modeling of complex systems and process chains therefore represents an emerging research direction.
Seth Guikema and Roshanak Nateghi
Natural disasters can have significant widespread impacts on society, and they often lead to loss of electric power for a large number of customers in the most heavily impacted areas. In the United States, severe weather and climate events have been the leading cause of major outages (i.e., more than 50,000 customers affected), leading to significant socioeconomic losses. Natural disaster impacts can be modeled and probabilistically predicted prior to the occurrence of the extreme event, although the accuracy of the predictive models will vary across different types of disasters. These predictions can help utilities plan for and respond to extreme weather and climate events, helping them better balance the costs of disaster responses with the need to restore power quickly. This, in turn, helps society recover from natural disasters such as storms, hurricanes, and earthquakes more efficiently. Modern Bayesian methods may provide an avenue to further improve the prediction of extreme event impacts by allowing first-principles structural reliability models to be integrated with field-observed failure data. Climate change and climate nonstationarity pose challenges for natural hazards risk assessment, especially for hydrometeorological hazards such as tropical cyclones and floods, although the link between these types of hazards and climate change remains highly uncertain and the topic of many research efforts. A sensitivity-based approach can be taken to understand the potential impacts of climate change-induced alterations in natural hazards such as hurricanes. This approach gives an estimate of the impacts of different potential changes in hazard characteristics, such as hurricane frequency, intensity, and landfall location, on the power system, should they occur. Further research is needed to better understand and probabilistically characterize the relationship between climate change and hurricane intensity, frequency, and landfall location, and to extend the framework to other types of hydroclimatological events. Underlying the reliability of power systems in the United States is a diverse set of regulations, policies, and rules governing electric power system reliability. An overview of these regulations and the challenges associated with current U.S. regulatory structure is provided. Specifically, high-impact, low-frequency events such as hurricanes are handled differently in the regulatory structure; there is a lack of consistency between bulk power and the distribution system in terms of how their reliability is regulated. Moreover, the definition of reliability used by the North American Reliability Corporation (NERC) is at odds with generally accepted definitions of reliability in the broader reliability engineering community. Improvements in the regulatory structure may have substantial benefit to power system customers, though changes are difficult to realize. Overall, broader implications are raised for modeling other types of natural hazards. Some of the key takeaway messages are the following: (1) the impacts natural hazard on infrastructure can be modeled with reasonable accuracy given sufficient data and modern risk analysis methods; (2) there are substantial data on the impacts of some types of natural hazards on infrastructure; and (3) appropriate regulatory frameworks are needed to help translate modeling advances and insights into decreased impacts of natural hazards on infrastructure systems.