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.

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.

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.

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 extrem

Increasing climate risks introduce new sources of uncertainty to smallholder farmers’ livelihood decisions. While farmers in different development contexts tend to accurately perceive long-term climatic trends, livelihood diversification as a climate resilience strategy has generally lagged behind awareness of climate risks. In this study, we investigate potential mechanisms behind this lagged response through a survey of 500 farming households in Nepal’s Chitwan Valley, a region that is highly dependent on subsistence agriculture and highly exposed to several climate-driven hazards. Specifically, we employ a suite of cross-sectional and time series econometric techniques to analyze how farmers’ information sources, social capital, and previous exposure to climate hazards shape climate risk perceptions and livelihood decisions. We find that climate-driven risks are highly salient to household perceptions of farming risks; however, they also drive higher perceived risks of common livelihood diversification strategies, including rural–urban migration and off-farm employment. Further, while farming households generally maintain diversified income portfolios, exposure to droughts and/or floods leads to persistent increases in the reliance on farming income, which we term a “retrenchment” response. We find evidence for both financial and psychological mechanisms behind this response, which may exacerbate environmentally driven poverty traps. Our results indicate that efforts to build farmers’ resilience to climate risks should especially account for perceived risks of livelihood alternatives, financial constraints, and loss-averse behavior in response to income shocks.

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.

Numerous barriers interfere with achieving effective outcomes of climate adaptation and mitigation governance. In the United States, the politicization of climate change and the long-standing susceptibility of long-term projects to politicians' short-term budgetary incentives both heighten the difficulties for effective climate change governance. U.S. quasi-governmental organizations (QGOs) were traditionally created to address several of these barriers in non-climate change domains. The properties of QGOs that allow these organizations to address governance barriers may also allow them to cope with shocks more easily than traditional government agencies. Green banks are an emerging and growing form of dedicated climate change governance in the United States. We use the organizational ambidexterity (OA) framework to evaluate the case of the quasi-governmental Connecticut Green Bank (CGB)'s adaptation to a 2017 state-instigated budget shock. The OA framework is useful for this case study given its emphasis on managerial response to organizational survival threats. We find that the CGB adapted financially to the shock, as a result of mission and financial drift away from Connecticut state control and policy. The CGB's adaptation to the shock hinged on its quasi-governmental status—which allowed it to create a nonprofit organization, Inclusive Prosperity Capital (IPC), and pursue activities to render both entities financially and operationally self-sufficient. We characterize the CGB's adaptive response as hybrid, structural-cyclical, ambidexterity. Our study provides the first empirical description of ambidexterity in the quasi-governmental space and builds evidence for the utility of applying hybrid ambidexterity theory in the climate change domain. The analysis carries implications for a wide array of public and private organizations that must adapt to survival threats by balancing activities that affect short- and long-term viability within the context of their mission orientation.

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.

Harnessing scientific research to address societal challenges requires careful alignment of expertise, resources, and research questions with real-world needs, timelines, and constraints. In the case of place-based research, studies can avoid misalignment when grounded in the realities of specific locations and conducted in collaboration with knowledgeable local partners. But literature on best practices for such research is underdeveloped on how to identify appropriate locations and partners. In practice, these research-design choices are sometimes made based on convenience or prior experience—a strategy labeled opportunism. Here we examine a deliberative and exploratory approach in contrast to default opportunism. We introduce a general framework for scoping place-based opportunities for research and engagement. We apply the framework to identify climate-adaptation planning decisions, rooted in specific communities, around which to organize research and engagement in a large project addressing coastal climate risks in the Northeast US. The framework asks project personnel to negotiate explicit project goals, identify corresponding evaluation criteria, and assess opportunities against criteria within an iterative cycle of listening to needs, assessing options, prioritizing actions, and refining goals. In the application, we elicit a broad range of objectives from project personnel. We find that a structured process offers opportunities to collaboratively operationalize notions of equity and justice. We find some objectives in tension—including equity objectives—indicating trade-offs that other projects may also need to navigate. We reflect on challenges encountered in the application and on near-term costs and benefits of the exploratory process.

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.

Tipping points have gained substantial traction in climate change discourses. Here we critique the ‘tipping point’ framing for oversimplifying the diverse dynamics of complex natural and human systems and for conveying urgency without fostering a meaningful basis for climate action. Multiple social scientific frameworks suggest that the deep uncertainty and perceived abstractness of climate tipping points render them ineffective for triggering action and setting governance goals. The framing also promotes confusion between temperature-based policy benchmarks and properties of the climate system. In both natural and human systems, we advocate for clearer, more specific language to describe the phenomena labelled as tipping points and for critical evaluation of whether, how and why different framings can support scientific understanding and climate risk management.

Tropical cyclones (TCs) that undergo rapid intensification (RI) before landfall are notoriously difficult to predict and have caused tremendous damage to coastal regions in the United States. Using downscaled synthetic TCs and physics-based models for storm tide and rain, we investigate the hazards posed by TCs that rapidly intensify before landfall under both historical and future mid-emissions climate scenarios. In the downscaled synthetic data, the percentage of TCs experiencing RI is estimated to rise across a significant portion of the North Atlantic basin. Notably, future climate warming causes large increases in the probability of RI within 24 hr of landfall. Also, our analysis shows that RI events induce notably higher rainfall hazard levels than non-RI events with equivalent TC intensities. As a result, RI events dominate increases in 100-year rainfall and storm tide levels under climate change for most of the US coastline.

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.

Risks, such as climate change, disease outbreak, geopolitical tension, may exacerbate food insecurity by negatively impacting crop yield. Additional agricultural nitrogen input may partly offset yield losses, with a corresponding increase in nitrogen pollution. The problems of food insecurity and nitrogen pollution are urgent and global but have not been addressed in an integrated fashion. Current efforts to combat food insecurity occur primarily through the United Nations’ World Food Program at the international level, and, at the local community level, through food banks. The international program to monitor and reduce global nitrogen pollution is in its early stage. Food provision and nitrogen pollution reduction from agriculture presents a dual challenge that requires an integrated solution. Here, we propose a cooperative food bank, where membership is a matter of choice and is not coerced. Membership requires participants to reduce nitrogen pollution in agriculture but creates a risk-buffering system, providing food compensation when participants are affected by risk factors. We delineate the structure of the cooperative food bank, its operation, from the short-term mobilization of resources to long-term capacity building. Lastly, we assess the feasibility of its implementation and highlight the potential major roadblocks to its implementation within the current socio-political context. The cooperative food bank showcases a novel solution that simultaneously tackles food insecurity and nitrogen pollution via governance. We hope this proposal will stimulate a research agenda and policy discussions focused on integrated approaches to effective governance regimes for linked socio-environmental problems.

The politicization of climate change and the difficulty of achieving multi-level or sectoral stakeholder coordination are common institutional barriers to effective climate change adaptation governance outcomes. In the U.S., quasi-government organizations (QGOs) were designed to overcome such barriers, albeit traditionally for non-climatic purposes. This study’s objective is to illustrate how the design characteristics of QGOs may be useful for overcoming the above climate adaptation barriers. Methodologically, this paper analyzes six case studies, selected to illustrate the major characteristics of QGOs, of climate-focused and non climate-focused QGOs at the sub-national level in the U.S. Non climate-focused examples are included for comparison with, and to supplement, the limited number of QGOs currently working on climate efforts. For each case, this study focuses on eight design characteristics: seven that represent measures of political and financial independence, and one focused on board composition, to illustrate the extent to which QGOs enable multi-level and multi-sectoral stakeholder coordination. This study finds that among the assortment of existing QGO designs some are particularly well suited to overcoming either the politicization of climate adaptation policy or obstacles to enhancing policy coordination, while some reduce both, albeit to a lesser extent. Broadly, this paper concludes that QGOs can strengthen effective action by depoliticizing informational sources and fostering cross scale coordination of planning and implementation.

Extreme rainfall found in tropical cyclones (TCs) is a risk for human life and property in many low- to midlatitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computationally expensive, and existing models are largely unable to model key rainfall asymmetries such as rainbands and extratropical transition. Here, a machine learning–based framework is developed to model overwater TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest (QRF) models are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain-rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r² value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain-rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rainbands, wavenumber asymmetries, and possibly extratropical transition.

Several governments have tested formal index-based insurance to build climate resilience among smallholder farmers. Yet, adoption of such programs has generated concerns that insurance may crowd out long-established informal risk transfer arrangements. Understanding this phenomenon requires new analytic approaches that capture dynamics of human social behavior when facing risky events. Here, we develop a modelling framework, based on evolutionary game theory and empirical data from Nepal and Ethiopia, to demonstrate that insurance may introduce a new social dilemma in farmer risk management strategies. We find that while socially optimal risk management is achieved when all farmers pursue a combination of formal and informal risk transfer, a community of self-interested agents is unable to maintain this coexistence at moderate to high covariate risks. We find that a combination of pro-social preferences - namely, moderate altruism and solidarity - helps farmers overcome these concerns and achieve the social optimum. Behavioral interventions that cue such preferences can render financial incentives more efficient in promoting optimal climate risk management, with potential savings worth approximately 5-15 percent of community agricultural income under a range of risk levels.

Sea-level rise amplifies the frequency of extreme sea levels by raising their baseline height. Amplifications are often projected for arbitrary future years and benchmark frequencies. Consequently, such projections do not indicate when flood risk thresholds may be crossed given the current degree of local coastal protection. To better support adaptation planning and comparative vulnerability analyses, we project the timing of the frequency amplification of extreme sea levels relative to estimated local flood protection standards, using sea-level rise projections of IPCC AR6 until 2150. Our central estimates indicate that those degrees of protection will be exceeded ten times as frequently within the next 30 years (the lead time that large adaptation measures may take) at 26% and 32% of the tide gauges considered, and annually at 4% and 8%, for a low- and high-emissions scenario, respectively. Adaptation planners may use our framework to assess the available lead time and useful lifetime of protective infrastructure.

Hurricane storm surge represents a significant threat to coastal communities around the world. Here, we use artificial neural network (ANN) models to predict storm surge levels using hurricane characteristics along the US Gulf and East Coasts. The ANN models are trained with storm surge levels from a hydrodynamic model and physical characteristics of synthetic hurricanes which are downscaled from National Centers for Environmental Prediction (NCEP) reanalysis using a statistical-deterministic hurricane model. The ANN models are able to accurately predict storm surge levels with root-mean-square errors (RMSE) below 0.2 m and correlation coefficients > 0.85. The ANN models trained with the NCEP data also show satisfactory accuracy (RMSE below 0.7 m; correlation > 0.7) in predicting storm surge levels for hurricanes downscaled from future climate projections. Once trained, we use the ANN models to assess the sensitivity of storm surge levels to variations in hurricane characteristics and local geophysical features. Progressively stronger maximum wind speeds and larger outer radius sizes independently increase storm surge levels at all locations along the US East and Gulf Coasts. The response of storm surge levels to changes in hurricane translation speed, however, is found to be sensitive to coastal configuration, with increases in hurricane translation speed amplifying (reducing) storm surge levels in open ocean (semi-enclosed) regions.

Projections of future sea-level change are characterized by both quantifiable uncertainty and by ambiguity. Both types of uncertainty are relevant to users of sea-level projections, particularly those making long-term investment and planning decisions with multigenerational consequences. Communicating information about both types is thus a central challenge faced by scientists who generate sea-level projections to support decision-making. Diverse approaches to communicating uncertainty in future sea-level projections have been taken over the last several decades, but the literature evaluating these approaches is limited and not systematic. Here, we review how the Intergovernmental Panel on Climate Change (IPCC) has approached uncertainty in sealevel projections in past assessment cycles and how this information has been interpreted by national and subnational assessments, as well as alternative approaches used by recent US subnational assessments. The evidence reviewed here generally supports the explicit approach to communicating both types of uncertainty adopted by the IPCC Sixth Assessment Report (AR6).

Sea level rise (SLR) will increase adaptation needs along low-lying coasts worldwide. Despite centuries of experience with coastal risk, knowledge about the effectiveness and feasibility of societal adaptation on the scale required in a warmer world remains limited. This paper contrasts end-century SLR risks under two warming and two adaptation scenarios, for four coastal settlement archetypes (Urban Atoll Islands, Arctic Communities, Large Tropical Agricultural Deltas, Resource-Rich Cities). We show that adaptation will be substantially beneficial to the continued habitability of most low-lying settlements over this century, at least until the RCP8.5 median SLR level is reached. However, diverse locations worldwide will experience adaptation limits over the course of this century, indicating situations where even ambitious adaptation cannot sufficiently offset a failure to effectively mitigate greenhouse-gas emissions.

Climate variability and climate change influence human migration both directly and indirectly through a variety of channels that are controlled by individual and household socioeconomic, cultural, and psychological processes as well as public policies and network effects. Characterizing and predicting migration flows are thus extremely complex and challenging. Among the quantitative methods available for predicting such flows is the widely used gravity model that ignores the network autocorrelation among flows and thus may lead to biased estimation of the climate effects of interest. In this study, we use a network model, the additive and multiplicative effects model for network (AMEN), to investigate the effects of climate variability, migrant networks, and their interactions on South African internal migration. Our results indicate that prior migrant networks have a significant influence on migration and can modify the association between climate variability and migration flows. We also reveal an otherwise obscure difference in responses to these effects between migrants moving to urban and non-urban destinations. With different metrics, we discover diverse drought effects on these migrants; for example, the negative standardized precipitation index (SPI) with a timescale of 12 months affects the non-urban-oriented migrants’ destination choices more than the rainy season rainfall deficit or soil moisture do. Moreover, we find that socioeconomic factors such as the unemployment rate are more significant to urban-oriented migrants, while some unobserved factors, possibly including the abolition of apartheid policies, appear to be more important to non-urban-oriented migrants.

Future coastal flood hazard at many locations will be impacted by both tropical cyclone (TC) change and relative sea-level rise (SLR). Despite sea level and TC activity being influenced by common thermodynamic and dynamic climate variables, their future changes are generally considered independently. Here, we investigate correlations between SLR and TC change derived from simulations of 26 Coupled Model Intercomparison Project Phase 6 models. We first explore correlations between SLR and TC activity by inference from two large-scale factors known to modulate TC activity: potential intensity (PI) and vertical wind shear. Under the high emissions SSP5-8.5, SLR is strongly correlated with PI change (positively) and vertical wind shear change (negatively) over much of the western North Atlantic and North West Pacific, with global mean surface air temperature (GSAT) modulating the co-variability. To explore the impact of the joint changes on flood hazard, we conduct climatological–hydrodynamic modeling at five sites along the US East and Gulf Coasts. Positive correlations between SLR and TC change alter flood hazard projections, particularly at Wilmington, Charleston and New Orleans. For example, if positive correlations between SLR and TC changes are ignored in estimating flood hazard at Wilmington, the average projected change to the historical 100 years storm tide event is under-estimated by 12%. Our results suggest that flood hazard assessments that neglect the joint influence of these factors and that do not reflect the full distribution of GSAT change may not accurately represent future flood hazard.

Estimates of changes in the frequency or height of contemporary extreme sea levels (ESLs) under various climate change scenarios are often used by climate and sea level scientists to help communicate the physical basis for societal concern regarding sea level rise. Changes in ESLs (i.e., the hazard) are often represented using various metrics and indicators that, when anchored to salient impacts on human systems and the natural environment, provide useful information to policy makers, stakeholders, and the general public. While changes in hazards are often anchored to impacts at local scales, aggregate global summary metrics generally lack the context of local exposure and vulnerability that facilitates translating hazards into impacts. Contextualizing changes in hazards is also needed when communicating the timing of when projected ESL frequencies cross critical thresholds, such as the year in which ESLs higher than the design height benchmark of protective infrastructure (e.g., the 100-year water level) are expected to occur within the lifetime of that infrastructure. We present specific examples demonstrating the need for such contextualization using a simple flood exposure model, local sea level rise projections, and population exposure estimates for 414 global cities. We suggest regional and global climate assessment reports integrate global, regional, and local perspectives on coastal risk to address hazard, vulnerability and exposure simultaneously.