부산대학교 도서관

For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies. Plain Language Summary: Feedbacks associated with Arctic warming are consequential for both the region and the strongly coupled global climate system. To assess the variability of the impacts of global warming and associated feedbacks in model‐based predictions, we quantified the sensitivity of the Arctic climate state to nine uncertain variables parameterizing the U.S. Department of Energy's global climate model known as the Energy Exascale Earth System Model (E3SM). Because the computational cost of repeatedly running high‐resolution configurations of E3SM was prohibitive, we used an ultra‐low resolution (ULR) configuration as a physics‐based surrogate for sensitivity analysis. Our first ever global sensitivity study of version 1 of the E3SM identified that the atmospheric parameters in E3SM's cloud physics model had the most impact on the atmosphere, sea ice, and ocean quantities of interest. This result demonstrates the importance of fully coupled climate analyses, which are necessary to identify such cross‐component influences. While we constructed confidence intervals that quantify the error in our estimates of parameter sensitivity introduced by using a limited number of ULR E3SM model runs, future investigation is needed to quantify the impact of resolution on error. Key Points: We perform the first global sensitivity analysis using the fully coupled ultra‐low resolution Energy Exascale Earth System ModelUncertainty in cloud physics parameters is found to most greatly impact Arctic climate predictionsOur inferred quantity of interest parameter correlations uncover key physical feedbacks and can guide model tuning [ABSTRACT FROM AUTHOR]