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Abstract
The uncertainties in scientific studies for climate risk management can be investigated at three levels of complexity: “ABC”. The most sophisticated involves “Analyzing” the full range of uncertainty with large multi-model ensemble experiments. The simplest is about “Bounding” the uncertainty by defining only the upper and lower limits of the likely outcomes. The intermediate approach, “Crystallizing” the uncertainty, distills the full range to improve the computational efficiency of the “Analyze” approach. Modelers typically dictate the study design, with decision-makers then facing difficulties when interpreting the results of ensemble experiments. We assert that to make science more relevant to decision-making, we must begin by considering the applications of scientific outputs in facilitating decision-making pathways, particularly when managing extreme events. This requires working with practitioners from outset, thereby adding “D” for “Decision-centric” to the ABC framework.
Highlights
Introduction — The Cascade of Uncertainty in Scientific ModelingUsed to represent reality, help resource managers explore the outcomes of different trade-offs and decisions
Science informs complex decisions ranging from climate adaptation strategies to appraisals of investment opportunities
Which pathway to take through the choices in a modeling study is inherently subjective, with the range of choices expanding at each stage, creating a “cascade of uncertainty”
Summary
Used to represent reality, help resource managers explore the outcomes of different trade-offs and decisions. Modeling is a complex set of activities, with multiple choices around model selection, identifying which processes to include, and how to benchmark performance. Such choices present contrasting modeling options, each of which leads to potentially different but plausible outcomes. Which pathway to take through the choices in a modeling study is inherently subjective, with the range of choices expanding at each stage, creating a “cascade of uncertainty” This concept was developed by the climate science community to describe how uncertainty ranges expand along the modeling chain. The initial and boundary conditions and values assigned to model parameters likewise affect model output These uncertainties grow as more permutations in the modeling chain are explored. It is crucial to identify modeling pathways that are technically defensible, yet feasible for decision-makers with varying levels of resource and expertise
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