摘要：The origins of uncertainty in climate projections have major consequences for the scientific and policy decisions made in response to climate change. Internal climate variability, for example, is an inherent uncertainty in the climate system that is undersampled by the multimodel ensembles used in most climate impacts research. Because of this, decision makers are left with the question of whether the range of climate projections across models is due to structural model choices, thus requiring more scientific investment to constrain, or instead is a set of equally plausible outcomes consistent with the same warming world. Similarly, many questions faced by scientists require a clear separation of model uncertainty and that arising from internal variability. With this as motivation and the renewed attention to large ensembles given planning for Phase 7 of the Coupled Model Intercomparison Project (CMIP7), we illustrate the scientific and policy value of the attribution and quantification of uncertainty from initial condition large ensembles, particularly when analyzed in conjunction with multimodel ensembles. We focus on how large ensembles can support regional‐scale robust adaptation decision‐making in ways multimodel ensembles alone cannot. We also acknowledge several recently identified problems associated with large ensembles, namely, that they are (1) resource intensive, (2) redundant, and (3) biased. Despite these challenges, we show, using examples from hydroclimate, how large ensembles provide unique information for the scientific and policy communities and can be analyzed appropriately for regional‐scale climate impacts research to help inform risk management in a warming world. Plain Language Abstract Estimating uncertainties in projections of climate change poses challenges but is crucial to focusing scientific and policy efforts. Initial condition large ensembles (the same model run many times with the same set of assumptions) has revealed that irreducible uncertainty arising from natural variations in the climate system—called internal variability—can be larger and more persistent than expected when compared to the set of models typically used in climate impacts assessments. Because of this, some argue that the large magnitude of internal variability presents a challenge to effective adaptations in response to climate change. Here we show using examples from water management that characterizing internal variability, even if it is large and irreducible, is the means to more effective decision‐making, pointing to the importance of initial condition large ensembles in this effort. We also discuss the criticisms of large ensembles: that they are costly, redundant, and biased. We show that despite these challenges, large ensembles provide unique information that is consistent with the insights from decision science about how to position effective decisions under conditions of deep uncertainty.
