Search for: All records

Climate extremes increasingly threaten energy infrastructure, yet whether disparities in energy resilience persist within cities under comparable hazard exposure and how distributed energy resources may reshape them remain largely unquantified. By integrating climate and energy projections, socio-demographic data, and an optimization-based power outage metric that captures initial outages, recovery, and distributed energy resource support, this study reveals evident energy resilience disparities shaped by intersectionality across income, race, and ethnicity in New York City. These disparities are projected to be exacerbated under future climates. Middle-income households exhibit the lowest levels of energy resilience, with their outage risk increasing by 1.5-2 times compared to the wealthiest households under severe events. Low- and middle-income Asian and high-income Black households experience up to twice the average outage risk increase compared to others within the same income groups. While distributed energy resources can partially mitigate disparities, their impact remains limited under business-as-usual growth. Our findings identify climate-vulnerable communities and inform efforts to promote energy justice in a changing climate. 

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. 

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. 

Reliable estimates of storm surge and sea level extremes with proper uncertainty quantification are key for cost-effective risk/adaptation planning. However, observational estimates are often unavailable or uncertain along most coastlines owing to data scarcity. Here, we provide a fully observational-driven probabilistic dataset (US-CoastEX) of storm surge and sea level extremes for the U.S. coast (1950–2020). Non-stationary extreme storm surge distributions are generated for gauged and ungauged sites by applying Bayesian methods to the U.S. tide gauge network, complemented with additional storm data unavailable in commonly used tide gauge data. The distributions are combined with tidal peak data to estimate return periods and levels of extreme sea levels and their uncertainty. Ou results show that traditional site-by-site estimates based on existing model data, as well as regionally-aggregated analysis of standard tide gauge data, have underestimated 100-year extreme sea levels by 50% (on average) along much of the U.S. coast, especially in regions exposed to extreme storms. The data supports coastal managers to make decisions, especially in vulnerable areas where in-situ sea-level monitoring is limited. 

Understanding extreme storm surge events that threaten low-lying coastal communities is key to effective flood mitigation/adaptation measures. However, observational estimates are sparse and highly uncertain along most coastal regions with a lack of observational evidence about long-term underlying trends and their contribution to overall extreme sea-level changes. Here, using a spatiotemporal Bayesian hierarchical framework, we analyse US tide gauge record for 1950–2020 and find that observational estimates have underestimated likelihoods of storm surge extremes at 85% of tide gauge sites nationwide. Additionally, and contrary to prevailing beliefs, storm surge extremes show spatially coherent trends along many widespread coastal areas, providing evidence of changing coastal storm intensity in the historical monitoring period. Several hotspots exist with regionally significant storm surge trends that are comparable to trends in mean sea-level rise and its key components. Our findings challenge traditional coastal design/planning practices that rely on estimates from discrete observations and assume stationarity in surge extremes. 

As the global impact of climate change intensifies, there is an urgent need for equitable and efficient climate adaptation policies. Traditional approaches for allocating public resources for climate adaptation that are based on economic benefit-cost analysis often overlook the resulting distributional inequalities. In this study, we apply equity weightings to mitigate the distributional inequalities in two key building and household level adaptation strategies under changing coastal flood hazards: property buyouts and building retrofit in New York City (NYC). Under a mid-range emissions scenario, we find that unweighted benefit cost ratios applied to residential buildings are higher for richer and non-disadvantaged census tracts in NYC. The integration of income-based equity weights alters this correlation effect, which has the potential to shift investment in mitigation towards poorer and disadvantaged census tracts. This alteration is sensitive to the value of elasticity of marginal utility, the key parameter used to calculate the equity weight. Higher values of elasticity of marginal utility increase benefits for disadvantaged communities but reduce the overall economic benefits from investments, highlighting the trade-offs in incorporating equity into adaptation planning. 

North Atlantic tropical cyclone (TC) activity under a high-emission scenario is projected using a statistical synthetic storm model coupled with nine Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The ensemble projection shows that the annual frequency of TCs generated in the basin will decrease from 15.91 (1979-2014) to 12.16 (2075-2100), and TC activity will shift poleward and coast-ward. The mean of lifetime maximum intensity will increase from 66.50 knots to 75.04 knots. Large discrepancies in TC frequency and intensity projections are found among the nine CMIP6 climate models. The uncertainty in the projection of wind shear is the leading cause of the discrepancies in the TC climatology projection, dominating the uncertainties in the projection of thermodynamic parameters such as potential intensity and saturation deficit. The uncertainty in the projection of wind shear may be related to the different projections of horizontal gradient of vertically integrated temperature in the climate models, which can be induced by different parameterizations of physical processes including surface process, sea ice, and cloud feedback. Informed by the uncertainty analysis, a surrogate model is developed to provide the first-order estimation of TC activity in climate models based on large-scale environmental features.