The Value of Initial Condition Large Ensembles to Robust Adaptation Decision‐Making

Earth's Future

Justin S. Mankin¹  Karen A. McKinnon²  Flavio Lehner³  Sloan Coats⁴ 

[1] Department of Geography Dartmouth College Hanover NH USA;Department of Statistics and the Institute of the Environment and Sustainability University of California Los Angeles CA USA;Institute for Atmospheric and Climate Science ETH Zürich Zürich Switzerland;Woods Hole Oceanographic Institution Woods Hole MA USA;

关键词: large ensembles;    robust decision‐making;    internal variability;    initial conditions;    climate adaptation;   

DOI  :  10.1029/2020EF001610

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown