Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations
Abstract
The atmospheric component of Energy Exascale Earth System Model (E3SM) version 1 (EAMv1) has included many new features in the physics parameterizations compared to its predecessors. Potential complex nonlinear interactions among the new features create a significant challenge for understanding the model behaviors and parameter tuning. Using the one-at-a-time method, the benefit of tuning one parameter may offset the benefit of tuning another parameter, or improvement in one target variable may lead to degradation in another target variable. To better understand the EAMv1 model behaviors and physics, we conducted a large number of short simulations (3 days) in which 18 parameters carefully selected from parameterizations of deep convection, shallow convection and cloud macrophysics and microphysics were perturbed simultaneously using the Latin Hypercube sampling method. From the Perturbed Parameters Ensemble (PPE) simulations and use of different skill score functions, we identified the most sensitive parameters, quantified how the model responds to changes of the parameters for both global mean and spatial distribution, and estimated the maximum likelihood of model parameter space for a number of important fidelity metrics. Comparison of the parametric sensitivity using simulations of two different lengths suggests that PPE using short simulations has some bearing on understanding parametricmore »
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- Pacific Northwest National Laboratory Richland WA USA
- Pacific Northwest National Laboratory Richland WA USA, School of Atmospheric Sciences Nanjing University Nanjing China
- Lawrence Livermore National Laboratory Livermore CA USA
- University of Wisconsin‐Milwaukee Milwaukee WI USA
- Brookhaven National Laboratory Upton NY USA
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Brookhaven National Laboratory (BNL), Upton, NY (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1484410
- Alternate Identifier(s):
- OSTI ID: 1482569; OSTI ID: 1484411; OSTI ID: 1496811; OSTI ID: 1498472
- Report Number(s):
- BNL-209469-2018-JAAM; PNNL-SA-138521; LLNL-JRNL-765390
Journal ID: ISSN 2169-897X
- Grant/Contract Number:
- DEAC02‐05CH11231; DE‐AC05‐00OR22725; DE‐SC0016287; DE‐AC52‐07NA27344; DE‐AC06‐76RLO 1830; SC0012704; AC05-76RL01830; AC52-07NA27344
- Resource Type:
- Published Article
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Additional Journal Information:
- Journal Name: Journal of Geophysical Research: Atmospheres Journal Volume: 123 Journal Issue: 23; Journal ID: ISSN 2169-897X
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Parametric sensitivity; uncertainty quantification; E3SM; PPE; short simulations; sensitivity analysis; Perturbed Parameters Ensemble, Energy Exascale Earth System Model
Citation Formats
Qian, Yun, Wan, Hui, Yang, Ben, Golaz, Jean‐Christophe, Harrop, Bryce, Hou, Zhangshuan, Larson, Vincent E., Leung, L. Ruby, Lin, Guangxing, Lin, Wuyin, Ma, Po‐Lun, Ma, Hsi‐Yen, Rasch, Phil, Singh, Balwinder, Wang, Hailong, Xie, Shaocheng, and Zhang, Kai. Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations. United States: N. p., 2018.
Web. doi:10.1029/2018JD028927.
Qian, Yun, Wan, Hui, Yang, Ben, Golaz, Jean‐Christophe, Harrop, Bryce, Hou, Zhangshuan, Larson, Vincent E., Leung, L. Ruby, Lin, Guangxing, Lin, Wuyin, Ma, Po‐Lun, Ma, Hsi‐Yen, Rasch, Phil, Singh, Balwinder, Wang, Hailong, Xie, Shaocheng, & Zhang, Kai. Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations. United States. https://doi.org/10.1029/2018JD028927
Qian, Yun, Wan, Hui, Yang, Ben, Golaz, Jean‐Christophe, Harrop, Bryce, Hou, Zhangshuan, Larson, Vincent E., Leung, L. Ruby, Lin, Guangxing, Lin, Wuyin, Ma, Po‐Lun, Ma, Hsi‐Yen, Rasch, Phil, Singh, Balwinder, Wang, Hailong, Xie, Shaocheng, and Zhang, Kai. Tue .
"Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations". United States. https://doi.org/10.1029/2018JD028927.
@article{osti_1484410,
title = {Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations},
author = {Qian, Yun and Wan, Hui and Yang, Ben and Golaz, Jean‐Christophe and Harrop, Bryce and Hou, Zhangshuan and Larson, Vincent E. and Leung, L. Ruby and Lin, Guangxing and Lin, Wuyin and Ma, Po‐Lun and Ma, Hsi‐Yen and Rasch, Phil and Singh, Balwinder and Wang, Hailong and Xie, Shaocheng and Zhang, Kai},
abstractNote = {The atmospheric component of Energy Exascale Earth System Model (E3SM) version 1 (EAMv1) has included many new features in the physics parameterizations compared to its predecessors. Potential complex nonlinear interactions among the new features create a significant challenge for understanding the model behaviors and parameter tuning. Using the one-at-a-time method, the benefit of tuning one parameter may offset the benefit of tuning another parameter, or improvement in one target variable may lead to degradation in another target variable. To better understand the EAMv1 model behaviors and physics, we conducted a large number of short simulations (3 days) in which 18 parameters carefully selected from parameterizations of deep convection, shallow convection and cloud macrophysics and microphysics were perturbed simultaneously using the Latin Hypercube sampling method. From the Perturbed Parameters Ensemble (PPE) simulations and use of different skill score functions, we identified the most sensitive parameters, quantified how the model responds to changes of the parameters for both global mean and spatial distribution, and estimated the maximum likelihood of model parameter space for a number of important fidelity metrics. Comparison of the parametric sensitivity using simulations of two different lengths suggests that PPE using short simulations has some bearing on understanding parametric sensitivity of longer simulations. Results from this analysis provide a more comprehensive picture of the EAMv1 behavior. The difficulty in reducing biases in multiple variables simultaneously highlights the need of characterizing model structural uncertainty (so-called embedded errors) to inform future development efforts.},
doi = {10.1029/2018JD028927},
journal = {Journal of Geophysical Research: Atmospheres},
number = 23,
volume = 123,
place = {United States},
year = {Tue Dec 04 00:00:00 EST 2018},
month = {Tue Dec 04 00:00:00 EST 2018}
}
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Table 1: 18 selected uncertain parameters and their ranges
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