Michael Oppenheimer

Structured expert judgement (SEJ) is an established method for estimating the likelihood of deeply uncertain outcomes, such as century-to-multicentury ice-sheet changes. We used SEJ to estimate ice-sheet changes through 2300 under scenarios stabilizing in 2100 at 2°C, 3.5°C, and 5°C of global warming. Under a 2°C scenario, the projected median (5th–95th percentile) ice-sheet contribution to GMSL change is 23 (-16 to 76) cm by 2100 and 131 (-45 to 420) cm by 2300; under a 5°C scenario, these values rise to 42 (-34 to 181) cm by 2100 and 365 (10 to 1120) cm by 2300. Relative to a comparable 2018 study, upper-end projections are similar while low-end projections reflect a greater probability of a significant increase in Antarctic accumulation. Combining elicitation results with probabilistic interpolation indicates a GMSL contribution from the Greenland ice sheet under SSP2-4.5 of 16 (-4 to 43) cm by 2100, 33 (-6 to 93) cm by 2150, and 77 (-17 to 234) cm by 2300, exceeding corresponding Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) medium-confidence estimates in uncertainty and, for 2100 and 2150, median values. For Antarctica, projections of 11 (-34 to 85) cm by 2100, 26 (-54 to 191) cm by 2150, and 105 (-84 to 532) cm by 2300 are more uncertain than AR6 medium-confidence projections. Accounting for tail dependence, 95th-percentile total ice-sheet contributions reach 1.1 m by 2100 and 2.5 m by 2150 under SSP2-4.5, highlighting the potential for multi-meter GMSL rise in the 22nd century under intermediate emissions.

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.

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.

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.

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.

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.

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.

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.