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The Value of Initial Condition Large Ensembles to Robust Adaptation Decision‐Making
Earth's Future ( IF 8.852 ) Pub Date : 2020-08-10 , DOI: 10.1029/2020ef001610
Justin S. Mankin 1, 2 , Flavio Lehner 3, 4 , Sloan Coats 5, 6 , Karen A. McKinnon 7
Affiliation  

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.

中文翻译:

大集合初始条件对鲁棒适应决策的价值

气候预测的不确定性根源对应对气候变化的科学和政策决定具有重大影响。例如,内部气候变异性是气候系统中的固有不确定性,而大多数气候影响研究中使用的多模型合奏对内部气候不确定性进行了抽样。因此,决策者们面临的问题是,各个模型之间的气候预测范围是否是由于结构模型的选择所致,因此需要更多的科学投资来约束,或者是一组与同一变暖世界相一致的合理的结果。 ... 同样,科学家面临的许多问题要求将模型不确定性和内部可变性所引起的问题明确分开。以此为动机,并针对耦合模型比对项目(CMIP7)的第7阶段计划重新关注大型集成体,我们说明了初始条件大型集成体的不确定性归因和量化的科学和政策价值,尤其是在与多模型合奏相结合。我们关注大型合奏如何以多模型合奏的方式支持区域规模鲁棒的适应性决策。一个人不能。我们也承认最近发现的与大型乐团相关的几个问题,即,它们是(1)资源密集型,(2)冗余和(3)有偏见的。尽管面临这些挑战,我们还是以水文气候为例,说明大型集合体如何为科学界和政策界提供独特的信息,并可以适当地进行分析以进行区域范围的气候影响研究,从而有助于为变暖的世界提供风险管理信息。
更新日期:2020-09-30
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