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Building community resilience in the face of climate disasters is critical to achieving a sustainable future. Operational approaches to resilience favor systems’ agile return to the status quo following a disruption. Here, we show that an overemphasis on recovery without accounting for transformation entrenches ‘resilience traps’–risk factors within a community that are predictive of recovery, but inhibit transformation. By quantifying resilience including both recovery and transformation, we identify risk factors which catalyze or inhibit transformation in a case study of community resilience in Florida during Hurricane Michael in 2018. We find that risk factors such as housing tenure, income inequality, and internet access have the capability to trigger transformation. Additionally, we find that 55% of key predictors of recovery are potential resilience traps, including factors related to poverty, ethnicity and mobility. Finally, we discuss maladaptation which could occur as a result of disaster policies which emphasize resilience traps.

 

The higher frequency and intensity of sustained heat events have increased the demand for cooling energy across the globe. Current estimates of summer‐time energy demand are primarily based on Cooling Degree Days (CDD), representing the number of degrees a day's average temperature exceeds a predetermined comfort zone temperature. Through a comprehensive analysis of the historical energy demand data across the USA, we show that the commonly used CDD estimates fall significantly short (±25%) of capturing regional thermal comfort levels. Moreover, given the increasingly compelling evidence that air temperature alone is not sufficient for characterizing human thermal comfort, we extend the widely used CDD calculation to heat index, which accounts for both air temperature and humidity. Our results indicate significant mis‐estimation of regional thermal comfort when humidity is ignored. Our findings have significant implications for the security, sustainability, and resilience of the grid under climate change.

 

Soaring temperatures and increased occurrence of heatwaves have drastically increased air‐conditioning demand, a trend that will likely continue into the future. Yet, the impact of anthropogenic warming on household air conditioning is largely unaccounted for in the operation and planning of energy grids. Here, by leveraging the state‐of‐the‐art in machine learning and climate model projections, we find substantial increases in future residential air conditioning demand across the U.S.—up to 8% with a range of 5%–8.5% (13% with a range of 11%–15%) after anthropogenic warming of 1.5°C (2.0°C) in global mean temperature. To offset this climate‐induced demand, an increase in the efficiency of air conditioners by as much as 8% (±4.5%) compared to current levels is needed; without this daunting technological effort, we estimate that some states will face supply inadequacies of up to 75 million “household‐days” (i.e., nearly half a month per average current household) without air conditioning in a 2.0°C warmer world. In the absence of effective climate mitigation and technological adaptation strategies, the U.S. will face substantial increases in air conditioning demand and, in the event of supply inadequacies, there is increased risk of leaving millions without access to space cooling during extreme temperatures.

 

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

 

Conceptualizing, assessing, and managing disaster risks involve collecting and synthesizing pluralistic information—from natural, built, and human systems—to characterize disaster impacts and guide policy on effective resilience investments. Disaster research and practice, therefore, are highly complex and inherently interdisciplinary endeavors. Characterizing the uncertainties involved in interdisciplinary disaster research is imperative, since misrepresenting uncertainty can lead to myopic decisions and suboptimal societal outcomes. Efficacious disaster mitigation should, therefore, explicitly address the uncertainties associated with all stages of hazard modeling, preparation, and response. However, uncertainty assessment and communication in the context of interdisciplinary disaster research remain understudied. In this “Perspective” article, we argue that in harnessing interdisciplinary methods and diverse data types in disaster research, careful deliberations on assessingType IIIandType IVerrors are imperative. Additionally, we discuss the pathologies in frequentist approaches, calling for an increasing role for Bayesian methods in uncertainty estimations. Moreover, we discuss the potential tradeoffs associated with information and uncertainty, calling for deliberate consideration of the role of diversity of information prior to setting the scope in interdisciplinary modeling. Future research guided by further reflections on the ideas raised in this article could help push the frontiers of uncertainty estimation in interdisciplinary hazard research and practice.

 

Abstract Tropical cyclones cause significant inland hazards, including wind damage and freshwater flooding, which depend strongly on how storm intensity evolves after landfall. Existing theoretical predictions for storm intensification and equilibrium storm intensity have been tested over the open ocean but have not yet been applied to storms after landfall. Recent work examined the transient response of the tropical cyclone low-level wind field to instantaneous surface roughening or drying in idealized axisymmetric f -plane simulations. Here, experiments testing combined surface roughening and drying with varying magnitudes of each are used to test theoretical predictions for the intensity response. The transient response to combined surface forcings can be reproduced by the product of their individual responses, in line with traditional potential intensity theory. Existing intensification theory is generalized to weakening and found capable of reproducing the time-dependent inland intensity decay. The initial (0–10 min) rapid decay of near-surface wind caused by surface roughening is not captured by existing theory but can be reproduced by a simple frictional spindown model, where the decay rate is a function of surface drag coefficient. Finally, the theory is shown to compare well with the prevailing empirical decay model for real-world storms. Overall, results indicate the potential for existing theory to predict how tropical cyclone intensity evolves after landfall.