Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (1) stepwise line search, (2) precalibration or screening, and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

Climate mitigation can bring health co-benefits by improving air quality. Yet, whether mitigation will widen or narrow current health disparities remains unclear. Here we use a coupled climate-energy-health model to assess the effects of a global carbon price on the distribution of ambient fine particulate matter (PM2.5) exposure and associated health risks across an ensemble of nearly 30,000 future scenarios. We find that pricing carbon consistently lowers the PM2.5-attributable death rates in lower-income countries by reducing fossil fuel burning (e.g., China and India). Since these countries are projected to have large ageing populations, the greatest reduction in global average PM2.5-attributable death rate is found in elderly populations, which are more vulnerable to air pollution than the other age groups. In contrast, the health effects in higher-income countries are more complex, because pricing carbon can increase the emissions from bioenergy use and land-use changes, counteracting the mortality decrease from reduced fossil fuel burning. Mitigation technology choices and complex interactions between age structures, energy use, and land use all influence the distribution of health effects. Our results highlight the importance of an improved understanding of regional characteristics and cross-sector dynamics for addressing the interconnected challenges of climate, health, and social inequalities.

Projections of nonstationary climate risks can vary considerably from one source to another, posing considerable communication and decision-analytical challenges. One such challenge is how to present trade-offs under deep uncertainty in a salient and interpretable manner. Some common approaches include analyzing a small subset of projections or treating all considered projections as equally likely. These approaches can underestimate risks, hide deep uncertainties, and are mostly silent on which assumptions drive decision-relevant outcomes. Here we introduce and demonstrate a transparent Bayesian framework for synthesizing deep uncertainties to inform climate risk management. The first step of this workflow is to generate an ensemble of simulations representing possible futures and analyze them through standard exploratory modeling techniques. Next, a small set of probability distributions representing subjective beliefs about the likelihood of possible futures is used to weight the scenarios. Finally, these weights are used to compute and characterize trade-offs, conduct robustness checks, and reveal implicit assumptions. We demonstrate the framework through a didactic case study analyzing how high to elevate a house to manage coastal flood risks.