Climate Risk Management

Abstract

Accelerating global climate change drives new climate risks. People around the world are researching, designing, and implementing strategies to manage these risks. Identifying and implementing sound climate risk management strategies poses nontrivial challenges including ( a) linking the required disciplines, ( b) identifying relevant values and objectives, ( c) identifying and quantifying important uncertainties, ( d) resolving interactions between decision levers and the system dynamics, ( e) quantifying the trade-offs between diverse values under deep and dynamic uncertainties, ( f) communicating to inform decisions, and ( g) learning from the decision-making needs to inform research design. Here we review these challenges and avenues to overcome them. ▪ People and institutions are confronted with emerging and dynamic climate risks. ▪ Stakeholder values are central to defining the decision problem. ▪ Mission-oriented basic research helps to improve the design of climate risk management strategies.

A Typology for Characterizing Human Action in MultiSector Dynamics Models

“…Humans generally interface with models from three distinct vantages, (a) humans as users of the models, (b) humans as creators of models, and (c) humans as actors represented in the models. In each of these modes of interface, human values strongly shape the modeling effort (see, e.g., Keller et al, 2021;Mayer et al, 2017;Tuana, 2017Tuana, , 2020Vezér et al, 2017).…”

Toward More Actionable Flood‐Risk Information

“…Finally, Bayesian model averaging offers a framework to integrate information based on the credibility of each model output (Keller et al., 2020; Z. Liu & Merwade, 2018). Uncertainty characterization and model diagnostics can help to demonstrate pathways for simplifying analytical frameworks by identifying decision‐relevant uncertainties and quantifying the contribution of individual uncertainty sources (see discussions, e.g., in Beevers et al., 2020; Hall & Solomatine, 2008; Keller et al., 2020; Reed et al., 2022; Savage et al., 2016). Because flood dynamics vary regionally, uncertainty characterization may reveal the need for different regional modeling choices.…”

“…In traditional approaches to estimating river flow and flood inundation, information producers manually adjust a subset of model parameters to calibrate models (Judi et al., 2018; Pianosi et al., 2016). This approximation may fail to identify the decision‐relevant parameters and can under‐sample parametric uncertainty (Keller et al., 2020).…”

A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management

“…This provides a formal basis for choices such as protecting hospitals and critical infrastructure to a higher degree than ordinary buildings (ASCE, 2013). However, these methods are silent on how standards should balance trade‐offs, not only between cost and performance but also between other stakeholder values such as sense of place, distributive justice, and safety (Bessette et al., 2017; Helgeson et al., 2022; Keller et al., 2021; Quinn et al., 2017; Vezér et al., 2018).…”

“…Human‐natural systems are never closed and model results are never unique, and thus validation and verification of models representing these systems is necessarily qualitative and subjective (Oreskes et al., 1994). In other words, no model exists that could represent the full truth, and the future is therefore deeply uncertain (Haasnoot et al., 2021; Keller et al., 2021; Lempert, 2002; Walker et al., 2013). To address these challenges, a growing literature on decision making under deep uncertainty (DMDU) emphasizes the value of identifying decisions that are robust, in some sense, to deep uncertainties (Borgomeo et al., 2018; Herman et al., 2015; McPhail et al., 2019; Moody & Brown, 2013).…”