Climate extremes threaten human health, economic stability, and the well-being of natural and built environments (e.g., 2003 European heat wave). As the world continues to warm, climate hazards are expected to increase in frequency and intensity. The impacts of extreme events will also be more severe due to the increased exposure (growing population and development) and vulnerability (aging infrastructure) of human settlements. Climate models attribute part of the projected increases in the intensity and frequency of natural disasters to anthropogenic emissions and changes in land use and land cover. Here, we review the impacts, historical and projected changes,and theoretical research gaps of key extreme events (heat waves, droughts, wildfires, precipitation, and flooding). We also highlight the need to improve our understanding of the dependence between individual and interrelated climate extremes because anthropogenic-induced warming increases the risk of not only individual climate extremes but also compound (co-occurring) and cascading hazards. ▪ Climate hazards are expected to increase in frequency and intensity in a warming world. ▪ Anthropogenic-induced warming increases the risk of compound and cascading hazards. ▪ We need to improve our understanding of causes and drivers of compound and cascading hazards.
An increase of 0.5°C in summer mean temperatures increases the probability of mass heat-related mortality in India by 146%.
We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties. We show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima. The proposed method, however, addresses this limitation and improves describing the dependence structure. MvCAT also enables evaluation of uncertainties relative to the length of record, which is fundamental to a wide range of applications such as multivariate frequency analysis.
Compound extremes correspond to events with multiple concurrent or consecutive drivers (e.g., ocean and fluvial flooding, drought, and heat waves) leading to substantial impacts such as infrastructure failure. In many risk assessment and design applications, however, multihazard scenarios of extremes and compound events are ignored. In this paper, we review the existing multivariate design and hazard scenario concepts and introduce a novel copula‐based weighted average threshold scenario for an expected event with multiple drivers. The model can be used for obtaining multihazard design and risk assessment scenarios and their corresponding likelihoods. The proposed model offers uncertainty ranges of most likely compound hazards using Bayesian inference. We show that the uncertainty ranges of design quantiles might be large and may differ significantly from one copula model to the other. We also demonstrate that the choice of marginal and copula functions may profoundly impact the multihazard design values. A robust analysis should account for these uncertainties within and between multivariate models that translate into multihazard design quantiles.
Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deeplandslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.Plain Language Summary Many societal and environmental impacts from events such as droughts and storms arise from a combination of weather and climate factors referred to as a compound event. Considering the complex nature of these high-impact events is crucial for an accurate assessment of climate-related risk, for example to develop adaptation and emergency preparedness strategies. However, compound event research has emerged only recently, therefore our ability to analyze these events is still BEVACQUA ET AL.
During the last century, we have observed a warming climate with more intense precipitation extremes in some regions, likely due to increases in the atmosphere's water holding capacity. Traditionally, infrastructure design and rainfall‐triggered landslide models rely on the notion of stationarity, which assumes that the statistics of extremes do not change significantly over time. However, in a warming climate, infrastructures and natural slopes will likely face more severe climatic conditions, with potential human and socioeconomical consequences. Here we outline a framework for quantifying climate change impacts based on the magnitude and frequency of extreme rainfall events using bias corrected historical and multimodel projected precipitation extremes. The approach evaluates changes in rainfall Intensity‐Duration‐Frequency (IDF) curves and their uncertainty bounds using a nonstationary model based on Bayesian inference. We show that highly populated areas across the United States may experience extreme precipitation events up to 20% more intense and twice as frequent, relative to historical records, despite the expectation of unchanged annual mean precipitation. Since IDF curves are widely used for infrastructure design and risk assessment, the proposed framework offers an avenue for assessing resilience of infrastructure and landslide hazard in a warming climate.
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