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<p>Weather- and climate-related extreme events such as droughts, heatwaves and storms arise from interactions between complex sets of physical processes across multiple spatial and temporal scales, often overwhelming the capacity of natural and/or human systems to cope. In many cases, the greatest impacts arise through the ‘compounding’ effect of weather and climate-related drivers and/or hazards, where the scale of the impacts can be much greater than if any of the drivers or hazards occur in isolation; for instance, when a heavy precipitation falls on an already saturated soil causing a devastating flood. Compounding in this context refers to the amplification of an impact due to the occurrence of multiple drivers and/or hazards either because multiple hazards occur at the same time, previous climate conditions or weather events have increased a system’s vulnerability to a successive event, or spatially concurrent hazards lead to a regionally or globally integrated impact. More generally, compound weather and climate events refer to a combination of multiple climate drivers and/or hazards that contributes to societal or environmental risk.</p><p>Although many climate-related disasters are caused by compound events, our ability to understand, analyse and project these events and interactions between their drivers is still in its infancy. Here we review the current state of knowledge on compound events and propose a typology to synthesize the available literature and guide future research. We organize the highly diverse event types broadly along four main themes, namely preconditioned, multivariate, temporally compounding, and spatially compounding events. We highlight promising analytical approaches tailored to the different event types, which will aid future research and pave the way to a coherent framework for compound event analysis. We further illustrate how human-induced climate change affects different aspects of compound events, such as their frequency and intensity through variations in the mean, variability, and the dependence between their climatic drivers. Finally, we discuss the emergence of new types of events that may become highly relevant in a warmer climate.</p>

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

Current approaches for assessing large‐scale flood risks contravene the fundamental principles of the flood risk system functioning because they largely ignore basic interactions and feedbacks between atmosphere, catchments, river‐floodplain systems, and socioeconomic processes. As a consequence, risk analyses are uncertain and might be biased. However, reliable risk estimates are required for prioritizing national investments in flood risk mitigation or for appraisal and management of insurance portfolios. We review several examples of process interactions and highlight their importance in shaping spatiotemporal risk patterns. We call for a fundamental redesign of the approaches used for large‐scale flood risk assessment. They need to be capable to form a basis for large‐scale flood risk management and insurance policies worldwide facing the challenge of increasing risks due to climate and global change. In particular, implementation of the European Flood Directive needs to be adjusted for the next round of flood risk mapping and development of flood risk management plans focusing on methods accounting for more process interactions in flood risk systems. WIREs Water 2018, 5:e1266. doi: 10.1002/wat2.1266 This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes Engineering Water > Planning Water

While compound weather and climate events (CEs) can lead to significant socioeconomic consequences, their response to climate change is mostly unexplored. We report the first multi-model assessment of future changes in return periods for the co-occurrence of heatwaves and drought, and extreme winds and precipitation based on the Coupled Model Intercomparison Project (CMIP6) and three emission scenarios. Extreme winds and precipitation CEs occur more frequently in many regions, particularly under higher emissions. Heatwaves and drought occur more frequently everywhere under all emission scenarios examined. For each CMIP6 model, we derive a skill score for simulating CEs. Models with higher skill in simulating historical CEs project smaller increases in the number of heatwaves and drought in Eurasia, but larger numbers of strong winds and heavy precipitation CEs everywhere for all emission scenarios. This result is partly masked if the whole CMIP6 ensemble is used, pointing to the considerable value in further improvements in climate models.

Global climate models are used to simulate climate and weather extremes, including extreme rainfall, high and low temperatures, droughts, and winds. Analyses of observations, historical simulations, and projections of extremes (Alexander, 2016; Bindoff et al., 2013; Sillman et al., 2013) have provided major advances in understanding how the statistics of extremes respond to natural variability and global warming. Many analyses of extremes focus on single hazards, such as how hot is the hottest day each year, or how much rain fell during the rainiest 5-day stretch of the year. An evaluation of models included in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) highlights that extremes are generally more difficult to represent realistically than the average (Flato et al., 2013; Sillman et al., 2013b). For instance, Flato et al. (2013) note that CMIP5 models generally capture observed trends in temperature extremes, but rainfall extremes are more challenging, although this might be partly due to higher observational uncertainty. Since this assessment, extensive literature has emerged demonstrating the improved skill of climate models in simulating temperature (e.g., Di Luca et al., 2020) and rainfall extremes (Bador et al., 2020), particularly in hot and cold extremes (Di Luca et al., 2020) and the intensity of heavy rainfall (Kim et al., 2020). The evaluation of wind extremes is more limited, but Kumar et al. (2015) noted that CMIP5 models simulated the multimodel mean (MMM) of spatial patterns of extreme winds with 25-100-year return periods (RPs) well. In the last decade, compound events (CEs) have emerged as a focus for understanding the link between changes in weather and climate and impacts on vulnerable systems (

Abstract. Atmospheric river (AR) systems play a significant role in the simultaneous occurrence of high coastal water levels and heavy precipitation in the Netherlands. Based on observed precipitation values (E-OBS) and the output of a numerical storm surge model (WAQUA/DSCMv5) forced with ERA-Interim sea level pressure and wind fields, we find that the majority of compound events (CEs) between 1979 and 2015 have been accompanied by the presence of an AR over the Netherlands. In detail, we show that CEs have a 3 to 4 times higher chance of occurrence on days with an AR over the Netherlands compared to any random day (i.e. days without knowledge on presence of an AR). In contrast, the occurrence of a CE on a day without AR is 3 times less likely than on any random day. Additionally, by isolating and assessing the prevailing sea level pressure (SLP) and sea surface temperature (SST) conditions with and without AR involvement up to 7 days before the events, we show that the presence of ARs constitutes a specific type of forcing conditions that (i) resemble the SLP anomaly patterns during the positive phase of the North Atlantic Oscillation (NAO+) with a north–south pressure dipole over the North Atlantic and (ii) cause a cooling of the North Atlantic subpolar gyre and eastern boundary upwelling zone while warming the western boundary of the North Atlantic. These conditions are clearly distinguishable from those during compound events without the influence of an AR which occur under SLP conditions resembling the East Atlantic (EA) pattern with a west–east pressure dipole over northern Europe and are accompanied by a cooling of the West Atlantic. Thus, this study shows that ARs are a useful tool for the early identification of possible harmful meteorological conditions over the Netherlands and supports an effort for the establishment of an early warning system.

Many winter deep low-pressure systems passing over Western Europe have the potential to induce significant storm surge levels along the coast of the North Sea. The accompanying frontal systems lead to large rainfall amounts, which can result in river discharges exceeding critical thresholds. The risk of disruptive societal impact increases strongly if river runoff and storm-surge peak occur near-simultaneously. For the Rhine catchment and the Dutch coastal area, existing studies suggest that no such relation is present at time lags shorter than 6 days. Here we re-investigate the possibility of finding near-simultaneous storm surge and extreme river discharge using an extended data set derived from a storm surge model (WAQUA/DCSMv5) and two hydrological riverdischarge models (SPHY and HBV96) forced with conditions from a high-resolution (0.11 • /12 km) regional climate model (RACMO2) in ensemble mode (16 × 50 years). We find that the probability for finding a co-occurrence of extreme river discharge at Lobith and storm surge conditions at Hoek van Holland are up to four times higher (than random chance) for a broad range of time lags (−2 to 10 days, depending on exact threshold). This highlights that the hazard of a co-occurrence of high river discharge and coastal water levels cannot be neglected in a robust risk assessment.