Efficient surrogate modeling methods for largescale Earth system models based on machinelearning techniques
Abstract
Abstract. Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a largescale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fasttoevaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machinelearningbased surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42 660 carbon fluxmore »
 Authors:

 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC23)
 OSTI Identifier:
 1513382
 Grant/Contract Number:
 AC0500OR22725
 Resource Type:
 Journal Article: Accepted Manuscript
 Journal Name:
 Geoscientific Model Development (Online)
 Additional Journal Information:
 Journal Volume: 12; Journal Issue: 5; Journal ID: ISSN 19919603
 Publisher:
 European Geosciences Union
 Country of Publication:
 United States
 Language:
 English
 Subject:
 58 GEOSCIENCES
Citation Formats
Lu, Dan, and Ricciuto, Daniel M. Efficient surrogate modeling methods for largescale Earth system models based on machinelearning techniques. United States: N. p., 2019.
Web. doi:10.5194/gmd1217912019.
Lu, Dan, & Ricciuto, Daniel M. Efficient surrogate modeling methods for largescale Earth system models based on machinelearning techniques. United States. doi:10.5194/gmd1217912019.
Lu, Dan, and Ricciuto, Daniel M. Mon .
"Efficient surrogate modeling methods for largescale Earth system models based on machinelearning techniques". United States. doi:10.5194/gmd1217912019. https://www.osti.gov/servlets/purl/1513382.
@article{osti_1513382,
title = {Efficient surrogate modeling methods for largescale Earth system models based on machinelearning techniques},
author = {Lu, Dan and Ricciuto, Daniel M.},
abstractNote = {Abstract. Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a largescale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fasttoevaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machinelearningbased surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42 660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42 660 variables, wherein the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly accurate and fasttoevaluate surrogate system will greatly enhance the computational efficiency of data–model integration to improve predictions and advance our understanding of the Earth system.},
doi = {10.5194/gmd1217912019},
journal = {Geoscientific Model Development (Online)},
issn = {19919603},
number = 5,
volume = 12,
place = {United States},
year = {2019},
month = {5}
}
Works referenced in this record:
Dimensionality Reduction for Complex Models via Bayesian Compressive Sensing
journal, January 2014
 Sargsyan, Khachik; Safta, Cosmin; Najm, Habib N.
 International Journal for Uncertainty Quantification, Vol. 4, Issue 1
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data
journal, October 2009
 Fox, Andrew; Williams, Mathew; Richardson, Andrew D.
 Agricultural and Forest Meteorology, Vol. 149, Issue 10
CH _{4} parameter estimation in CLM4.5bgc using surrogate global optimization
journal, January 2015
 Müller, J.; Paudel, R.; Shoemaker, C. A.
 Geoscientific Model Development, Vol. 8, Issue 10
Calibration of the E3SM Land Model Using SurrogateBased Global Optimization
journal, June 2018
 Lu, Dan; Ricciuto, Daniel; Stoyanov, Miroslav
 Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 6
Crop physiology calibration in the CLM
journal, January 2015
 Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.
 Geoscientific Model Development, Vol. 8, Issue 4
Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods
journal, January 2017
 Lu, Dan; Ricciuto, Daniel; Walker, Anthony
 Biogeosciences, Vol. 14, Issue 18
Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures
conference, January 2006
 Archambeau, C.; Valle, M.; Assenza, A.
 2006 IEEE International Symposium on Circuits and Systems
Review of surrogate modeling in water resources: REVIEW
journal, July 2012
 Razavi, Saman; Tolson, Bryan A.; Burn, Donald H.
 Water Resources Research, Vol. 48, Issue 7
An improved analysis of forest carbon dynamics using data assimilation
journal, January 2005
 Williams, Mathew; Schwarz, Paul A.; Law, Beverly E.
 Global Change Biology, Vol. 11, Issue 1
Taking the Human Out of the Loop: A Review of Bayesian Optimization
journal, January 2016
 Shahriari, Bobak; Swersky, Kevin; Wang, Ziyu
 Proceedings of the IEEE, Vol. 104, Issue 1
Multiobjective parameter optimization of common land model using adaptive surrogate modeling
journal, January 2015
 Gong, W.; Duan, Q.; Li, J.
 Hydrology and Earth System Sciences, Vol. 19, Issue 5
On the applicability of surrogatebased Markov chain Monte CarloBayesian inversion to the Community Land Model: Case studies at flux tower sites: SURROGATEBASED MCMC FOR CLM
journal, July 2016
 Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan
 Journal of Geophysical Research: Atmospheres, Vol. 121, Issue 13
The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model
journal, February 2018
 Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter
 Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 2
Comparison of surrogate models with different methods in groundwater remediation process
journal, October 2014
 Luo, Jiannan; Lu, Wenxi
 Journal of Earth System Science, Vol. 123, Issue 7
Special Section on Multidisciplinary Design Optimization: Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come?
journal, April 2014
 Viana, Felipe A. C.; Simpson, Timothy W.; Balabanov, Vladimir
 AIAA Journal, Vol. 52, Issue 4
Bayesian Calibration of the Community Land Model Using Surrogates
journal, January 2015
 Ray, J.; Hou, Z.; Huang, M.
 SIAM/ASA Journal on Uncertainty Quantification, Vol. 3, Issue 1