The value of initial condition large ensembles to robust adaptation decision-making

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mankin, J. S., Lehner, F., Coats, S., & McKinnon, K. A. The value of initial condition large ensembles to robust adaptation decision-making. Earth's Future, 8(10), (2020): e2012EF001610, doi:10.1029/2020EF001610.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.F. L. has been supported by the Swiss NSF (grant no. PZ00P2_174128), the NSF Division of Atmospheric and Geospace Sciences (grant no. AGS‐0856145, Amendment 87), and the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S.Department of Energy’s Office of Biological & Environmental Research (BER) via NSF IA 1844590. This is SOEST publication no. 11115