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Climate extremes, such as hurricanes, combined with large-scale integration of environment-sensitive renewables, could exacerbate the risk of widespread power outages. We introduce a coupled climate-energy model for cascading power outages, which comprehensively captures the impacts of climate extremes on renewable generation, and transmission and distribution networks. The model is validated with the 2022 Puerto Rico catastrophic blackout during Hurricane Fiona – a unique system-wide blackout event with complete records of weather-induced outages. The model reveals a resilience pattern that was not captured by the previous models: early failure of certain critical components enhances overall system resilience. Sensitivity analysis on various scenarios of behind-the-meter solar integration demonstrates that lower integration levels (below 45%, including the current level) exhibit minimal impact on system resilience in this event. However, surpassing this critical level without pairing it with energy storage can exacerbate the probability of catastrophic blackouts. 

While tropical cyclone (TC) and heatwave (HW) compound hazard extremes are rare in the historical record, they have been recently emerging and are expected to become more frequent under future climate projections. Joint TC-HW hazards can exacerbate heat stress felt by residents, particularly in densely populated urban communities or areas suffering from storm-related power outages. The Princeton Urban Canopy Model (PUCM) has been used to evaluate heatwave conditions in urban environments, but has yet to be used to model joint TC-HW conditions. In this study, we model joint TC-HW hazards by adjusting the surface energy and water budgets of the PUCM to account for TC flood and extreme wind hazards. We investigate joint hazard interactions during Hurricane Laura (2020) using the Weather Research and Forecasting model (WRF) to simulate both Laura's wind field to drive subsequent hydrodynamic modeling of inundation and post-storm atmospheric conditions. The WRF and hydrodynamic modeling results are then used to drive the PUCM to assess the interaction of joint flooding, wind, and heat and their impacts on the city of Lake Charles in Louisiana. Results show that accounting for TC inundation up to a week after landfall can cause over 3°C reductions in daytime heat stress and 1.5°C increases in nighttime heat stress compared to simulations that ignore the presence of flooding. Accounting for defoliation from extreme TC winds can increase maximum nighttime heat stress by more than 4°C. 

Changes in the tropical cyclone (TC) seasonal cycle can have profound impacts on compound hazards associated with TCs, such as consecutive summer rainfall and TC-heatwave compound events. However, only a few studies have explored future changes in TC seasonality, and they reach discrepant conclusions. In this study, we perform a high-resolution coupled climate simulation to study the future TC seasonal cycle and investigate the mechanisms of possible changes. The model simulation shows that, under the shared socio-economic pathway 5 8.5 scenario, the mean genesis date will shift significantly to later in the season in Northeastern Pacific (ENP) and North Atlantic (NA) but shift to later or earlier depending on the subregions in Northwestern Pacific (WNP). These shifts in TC seasonal cycles are induced by seasonally asymmetric changes in TC-favorable environmental conditions, which arise from seasonally asymmetric changes in large-scale circulation patterns, including the monsoon troughs, jet stream, and tropical zonal circulation. 

Tropical cyclone (TC) hazards coupled with dense urban development along the coastline have resulted in trillions in US damages over the past several decades, with an increasing trend in losses in recent years. So far, this trend has been driven by increasing coastal development. However, as the climate continues to warm, changing TC climatology may also cause large changes in coastal damages in the future. Approaches to quantifying regional TC risk typically focus on total storm damage. However, it is crucial to understand the spatial footprint of TC damage and ultimately the spatial distribution of TC risk. Here, we quantify the magnitude and spatial pattern of TC risk (in expected annual damage (EAD)) across the US from wind, storm surge, and rainfall using synthetic TCs, physics-based hazard models, and a county-level statistical damage model trained on historical TC data. We then combine end-of-century TC hazard simulations with US population growth and wealth increase scenarios (under the SSP2 4.5 emission scenario) to investigate the sensitivity of changes in TC risk across the US Atlantic and Gulf coasts. We find that not directly accounting for the effects of rainfall and storm surge results in much lower risk estimates and smaller future increases in risk. TC climatology change and socioeconomic change drive similar magnitude increases in total EAD across the US (roughly 160%), and that their combined effect (633% increase) is much higher. 

The emerging tropical cyclone (TC)-blackout-heatwave compound risk under climate change is not well understood. In this study, we employ projections of TCs, sea level rise, and heatwaves, in conjunction with power system resilience modeling, to evaluate historical and future TC-blackout-heatwave compound risk in Louisiana, US. We find that the return period for a compound event comparable to Hurricane Ida (2021), with approximately 35 million customer hours of simultaneous power outage and heatwave exposure in Louisiana, is around 278 years in the historical climate of 1980–2005. Under the SSP5-8.5 emissions scenario, this return period is projected to decrease to 16.2 years by 2070–2100, a ~17 times reduction. Under the SSP2-4.5 scenario, it decreases to 23.1 years, representing a ~12 times reduction. Heatwave intensification is the primary driver of this increased risk, reducing the return period by approximately 5 times under SSP5-8.5 and 3 times under SSP2-4.5. Increased TC activity is the second driver, reducing the return period by 40% and 34% under the respective scenarios. These findings enhance our understanding of compound climate hazards and inform climate adaptation strategies. 

Distribution networks, with large-scale integration of distributed renewable resources, particularly rooftop solar photovoltaic systems, represent the most extensive yet vulnerable components of modern electric power systems during climate extremes such as hurricanes. However, existing day-ahead electricity dispatch approaches primarily focus on the transmission network and lack the capability to manage the spatiotemporal risks associated with the vast distribution networks, which can potentially lead to significant power imbalances due to the mismatches between scheduled generation and actual demand. To address this increasingly critical gap under intensifying climate extremes and growing distributed renewable integration, we introduce Risk-aware Electricity Dispatch under Climate Extremes with Renewable integration (REDUCER), a risk-aware day-ahead electricity dispatch model that incorporates high-resolution spatiotemporal risk analysis for distribution networks with large-scale distributed renewable integration into an Entropic Value-at-Risk-constrained mixed-integer convex optimization framework. Applied to the 2022 Puerto Rico power grid under Hurricane Fiona, the proposed REDUCER model is seen to effectively manage these risks with substantially less reliance on additional flexibility resources to cope with power imbalances, reducing overall operational costs by about 30% under extreme cases compared to standard unit commitment strategies already informed by average demand loss. Also, the proposed REDUCER model consistently demonstrates its effectiveness in managing the increasing temporal net demand variability introduced by growing large-scale distributed solar integration while maintaining minimal operational costs. This model offers a practical solution for cost-effective and resilient electricity dispatch of modern power systems with large-scale renewable integration facing intensifying climate risks. 

Rapid global electrification is deepening cross-sector interdependence, fundamentally reshaping the resilience of energy systems in the face of intensifying climate extremes. While increased integration across energy generation, transmission, and consumption sectors can significantly enhance operational flexibility, it can also amplify the risk of cross-sector cascading failures under extreme weather events, giving rise to an emerging resilience paradox that remains insufficiently understood. This study examines evolving cross-sector interactions and their implications for climate resilience by analyzing global electrification trends and regional cases in Texas, integrated with global and downscaled projections of climate extremes. By identifying critical vulnerabilities and flexibility associated with increasing sectoral interdependence, this study highlights the necessity of adopting resilience-oriented, system-level strategies for system operators and policymakers to mitigate cross-sector cascading risks and maximize the benefits of electrification in a changing climate. 

Conventional computational models of climate adaptation frameworks inadequately consider decision-makers’ capacity to learn, update, and improve decisions. Here, we investigate the potential of reinforcement learning (RL), a machine learning technique that efficaciously acquires knowledge from the environment and systematically optimizes dynamic decisions, in modeling and informing adaptive climate decision-making. We consider coastal flood risk mitigations for Manhattan, New York City, USA (NYC), illustrating the benefit of continuously incorporating observations of sea-level rise into systematic designs of adaptive strategies. We find that when designing adaptive seawalls to protect NYC, the RL-derived strategy significantly reduces the expected net cost by 6 to 36% under the moderate emissions scenario SSP2-4.5 (9 to 77% under the high emissions scenario SSP5-8.5), compared to conventional methods. When considering multiple adaptive policies, including accomodation and retreat as well as protection, the RL approach leads to a further 5% (15%) cost reduction, showing RL’s flexibility in coordinatively addressing complex policy design problems. RL also outperforms conventional methods in controlling tail risk (i.e., low probability, high impact outcomes) and in avoiding losses induced by misinformation about the climate state (e.g., deep uncertainty), demonstrating the importance of systematic learning and updating in addressing extremes and uncertainties related to climate adaptation. 

Coastal flooding from tropical cyclone (TC)‐induced storm surges is among the most devastating natural hazards in the US. Accurately quantifying storm surge hazards is crucial for risk mitigation and climate adaptation. In this study, we conduct climatology‐hydrodynamic modeling to estimate TC surge hazards along the US northeast coastline under future climate scenarios. In this methodology, we generate synthetic TCs for the northeastern US to drive a hydrodynamic model (ADCIRC) to simulate storm surges. Observing their significant effect on storm surge, for the first time, we bias‐correct landfall angles of synthetic TCs, in addition to bias‐correcting their frequency and intensity. Our findings show that under the combined effects of sea level rise (SLR) and TC climatology change, historical 100‐year extreme water levels (EWLs) along the US northeast coastline would occur annually at the end of the century in both SSP2‐4.5 and SSP5‐8.5 emissions scenarios. 500‐year EWLs are also projected to occur every 1–60 (1–20) years under SSP2‐4.5 (SSP5‐8.5). SLR is the dominant factor in the dramatic changes in the EWLs. However, while in higher latitudes () TC climatology change modestly affect EWLs ( contribution for 100‐year and for 500‐year EWL changes), in lower latitudes the impact is more significant (up to 40% contribution to 100‐year and 55% for 500‐year EWL changes). Extending previous methods, the physics‐based probabilistic framework presented here can be applied to project future coastal flood hazards under the effects of SLR and storm climatology change for any TC‐prone region. 

Tropical cyclone (TC) winds control design wind speeds for much of the eastern United States. Those winds are likely to intensify with climate change, but climate change was not considered in the ASCE 7-22 design wind speed maps, potentially causing many structures to be designed with unacceptably high levels of risk. In this study, we investigate (1) the increases in design wind speed due to climate change; and (2) the resulting risk to structures if climate change is not considered. We estimated the design wind speeds for US counties affected by TCs along the Gulf and Atlantic coasts using nonstationary methods based on a set of synthetic TCs (1,000–1,500 year simulations) downscaled from the latest global climate projections (CMIP6) for the high-emissions scenario (SSP5-8.5). It was found that over the 21st century, 50-year return period winds would increase by an average of around 10% along the US Gulf and Atlantic coasts. Depending on the risk category, design lifetime, and year of construction, design wind speeds (targeting lifetime exceedance probability) are projected to increase by an average of 3%–6% for all counties studied and 6%–15% for coastal counties. For Risk Category II–IV structures, depending on the design lifetime and year of construction, 8%–36% of all counties studied and 25%–66% of coastal counties would experience projected lifetime exceedance probabilities that were at least two risk categories too low; for example, in up to 26% of all counties studied and 54% of coastal counties, a Risk Category III structure would be effectively designed as Risk Category I or lower.