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Abstract Storm surge events are a key driver of widespread flooding, particularly when combined with astronomical tides superimposed on mean sea level (MSL). Coastal storms exhibit seasonal variability which translates into a seasonal cycle in storm surge activity. Understanding changes in the seasonal storm surge cycle is critical as both changes in the amplitude and the phase may alter the flood potential, especially when compounded with changes in the MSL cycle. Here, a comprehensive analysis of the storm surge seasonal cycle and its links to the MSL seasonal cycle is performed using tide gauge observations from a quasi‐global data set. Harmonic analysis is used to assess the mean and changing storm surge seasonal cycles over time. Extreme value analysis is applied to explore the effect of seasonal changes on storm surge return levels. We also quantify the influence of large‐scale climate modes, and we compare how the seasonality of storm surge and MSL have changed relative to each other. The peak of the storm surge cycle typically occurs during winter for tide gauges outside of tropical cyclone regions, where there is also greater variability in the phase of the storm surge cycle. The timing of the peak varied by more than a month at 21% of the tide gauges analyzed. The MSL and storm surge cycles peaked at least once within 30 days over the historic records at 74% of tide gauges. 

Abstract Temporal storm surge clustering refers to a series of events affecting the same region within a short period of time, which can strongly influence coastal flooding impacts and erosion. Here, we analyze global storm surge clustering from tide gauges and a state-of-the-art global model hindcast to identify geographical hotspots of extreme storm surge clusters and assess event frequencies. We study the spatial distribution as well as the contribution of different event intensities to clustering. On average, globally, 92% of coastal locations show significant temporal clustering for 1-year return period events, and 25% for 5-year return level events, although notable spatial differences exist. Our results reveal two distinct clustering regimes: (i) short timescale clustering, where events occur in rapid succession (intra-annual), and (ii) long timescales (inter-annual), providing varying recovery times between events. We also test the validity of assuming a Poisson distribution, commonly used in storm surge frequency analyses. Our results show that >80% of the stations analyzed do not follow a Poisson distribution, at least when including events that are not the most extreme but exceeded, for example, the 1-year return level. These findings offer insights into temporal clustering dynamics of storm surges and their implications for coastal hazard assessments. 

Abstract When different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections. 

With the increase of tropical cyclone activity, coastal communities will experience growing impacts from extreme water levels and associated compound flooding. Multiple drivers contribute to total water level (TWL), including mean sea level, astronomical tides, riverine flow, storm surges, and waves. Therefore, gaining insight into future TWL variability requires a thorough understanding of how those drivers nonlinearly interact at different spatiotemporal scales. In this study, we developed a coupled coastal and wave model at sufficient spatial resolution to analyze: (i) tide–driver interactions and their nonlinear components stemming from surge, river flow, and wind-waves, and (ii) their spatiotemporal evolution across the pre-landfall, landfall, and post-landfall stages of tropical cyclones in the Chesapeake Bay, USA. Results show that tide–surge and tide–wave interactions, along with their nonlinear components, exhibit substantial annual variability, with extreme hurricanes producing abrupt and spatially distinct responses driven by low pressure anomalies in slow-moving storms and wind setup in faster systems. In contrast, tide–river interactions remain negligible except in the upper bay tributaries. A weak or neutral tide–driver interaction does not necessarily indicate a negligible nonlinear response. Rather, nonlinear interactions (NIs) generally act out of phase with their associated drivers, functioning as compensatory mechanisms that amplify or suppress TWL. These nonlinearities are transient and of high-frequency nature near the coast, but evolve into slower, more persistent fluctuations in upstream regions. As climate change reshapes coastal dynamics, a robust understanding of NIs is essential for designing effective flood protection, enhancing risk assessments, and developing informed adaptation strategies for extreme water levels. 

Compound flooding events are a threat to many coastal regions and can have widespread socio-economic implications. However, their frequency of occurrence, underlying flood drivers, and direct link to past socio-economic losses are largely unknown despite being key to supporting risk and adaptation assessments. Here, we present an impact-based analysis of compound flooding for 203 coastal counties along the U.S. Gulf and East coasts by combining data from multiple flood drivers and socio-economic loss information from 1980 to 2018. We find that ~80% of all flood events recorded in our study area were compound rather than univariate. In addition, we show that historical compound flooding events in most counties were driven by more than two flood drivers (hydrological, meteorological, and/or oceanographic) and distinct spatial clusters exist that exhibit variability in the underlying driver of compound flood events. Furthermore, we find that in more than 80% of the counties, over 80% of recorded property and crop losses were linked to compound flooding. Nearly 80% of counties have a higher median loss from compound than univariate events. For these counties, the median property loss is over 26 times greater, and the median crop loss is over 76 times greater for compound events on average. Our analysis overcomes some of the limitations of previous compound-event studies based on pre-defined flood drivers and offers new insights into the complex relationship between hazards and associated socio-economic impacts. 

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments. 

Abstract. Rising seas are a threat to human and natural systems along coastlines. The relation between global warming and sea level rise is established, but the quantification of impacts of historical sea level rise on a global scale is largely absent. To foster such quantification, here we present a reconstruction of historical hourly (1979–2015) and monthly (1900–2015) coastal water levels and a corresponding counterfactual without long-term trends in sea level. The dataset pair allows for impact attribution studies that quantify the contribution of sea level rise to observed changes in coastal systems following the definition of the Intergovernmental Panel on Climate Change (IPCC). Impacts are ultimately caused by water levels that are relative to the local land height, which makes the inclusion of vertical land motion a necessary step. Also, many impacts are driven by sub-daily extreme water levels. To capture these aspects, the factual data combine reconstructed geocentric sea level on a monthly timescale since 1900, vertical land motion since 1900 and hourly storm-tide variations since 1979. The inclusion of observation-based vertical land motion brings the trends of the combined dataset closer to tide gauge records in most cases, but outliers remain. Daily maximum water levels get in closer agreement with tide gauges through the inclusion of intra-annual ocean density variations. The counterfactual data are derived from the factual data through subtraction of the quadratic trend. The dataset is made available openly through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) at https://doi.org/10.48364/ISIMIP.749905 (Treu et al., 2023a).