For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step towards 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 interest (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 pre-industrial forcing. Uncertainties in the atmospheric parameters in the CLUBB (Cloud Layers Unified by Binormals) 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.
Tezaur, Irina, et al. "Global sensitivity analysis using the ultra-low resolution Energy Exascale Earth System Model." Journal of Advances in Modeling Earth Systems, vol. 14, no. 8, Aug. 2022. https://doi.org/10.1029/2021ms002831
Tezaur, Irina, Peterson, Kara, Powell, Amy, Jakeman, John, & Roesler, Erika (2022). Global sensitivity analysis using the ultra-low resolution Energy Exascale Earth System Model. Journal of Advances in Modeling Earth Systems, 14(8). https://doi.org/10.1029/2021ms002831
Tezaur, Irina, Peterson, Kara, Powell, Amy, et al., "Global sensitivity analysis using the ultra-low resolution Energy Exascale Earth System Model," Journal of Advances in Modeling Earth Systems 14, no. 8 (2022), https://doi.org/10.1029/2021ms002831
@article{osti_1882633,
author = {Tezaur, Irina and Peterson, Kara and Powell, Amy and Jakeman, John and Roesler, Erika},
title = {Global sensitivity analysis using the ultra-low resolution Energy Exascale Earth System Model},
annote = {For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step towards 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 interest (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 pre-industrial forcing. Uncertainties in the atmospheric parameters in the CLUBB (Cloud Layers Unified by Binormals) 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.},
doi = {10.1029/2021ms002831},
url = {https://www.osti.gov/biblio/1882633},
journal = {Journal of Advances in Modeling Earth Systems},
issn = {ISSN 1942-2466},
number = {8},
volume = {14},
place = {United States},
publisher = {American Geophysical Union (AGU)},
year = {2022},
month = {08}}
Mahajan, Salil; Evans, Katherine J.; Kennedy, Joseph H.
PASC '19: Platform for Advanced Scientific Computing Conference, Proceedings of the Platform for Advanced Scientific Computing Conferencehttps://doi.org/10.1145/3324989.3325724