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Title: Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization

Abstract

Calibration of the Energy Exascale Earth System Model (E3SM), land model (ELMv0) is challenging because of its model complexity, strong model nonlinearity, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the ELMv0 parameters to improve model projection of carbon fluxes. We propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the ELMv0, and then calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated a sufficiently large number of times in the optimization, which facilitates the global search. We calibrate eight parameters against five years of net ecosystem exchange, total leaf area index, and latent heat flux data from the U.S. Missouri Ozark flux tower. The calibrated model is then used for predicting the three variables in the following 4 years. The results indicate that an accurate surrogate model can be created for the ELMv0 with a relatively small number of SGmore » points, i.e., a few ELMv0 simulations that can be fully parallel. And, the application of the optimized parameters leads to a better model performance and a higher predictive capability than the default parameter values in the ELMv0.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [2]
  1. Computational Sciences and Engineering Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge TN USA
  2. Environmental Sciences Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge TN USA
  3. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge TN USA
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) (SC-23)
OSTI Identifier:
1458568
Alternate Identifier(s):
OSTI ID: 1458569; OSTI ID: 1460186
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Name: Journal of Advances in Modeling Earth Systems Journal Volume: 10 Journal Issue: 6; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; E3SM land model; global optimization; surrogate modeling; MOFLUX forest site

Citation Formats

Lu, Dan, Ricciuto, Daniel, Stoyanov, Miroslav, and Gu, Lianhong. Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization. United States: N. p., 2018. Web. doi:10.1002/2017MS001134.
Lu, Dan, Ricciuto, Daniel, Stoyanov, Miroslav, & Gu, Lianhong. Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization. United States. doi:10.1002/2017MS001134.
Lu, Dan, Ricciuto, Daniel, Stoyanov, Miroslav, and Gu, Lianhong. Sat . "Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization". United States. doi:10.1002/2017MS001134.
@article{osti_1458568,
title = {Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization},
author = {Lu, Dan and Ricciuto, Daniel and Stoyanov, Miroslav and Gu, Lianhong},
abstractNote = {Calibration of the Energy Exascale Earth System Model (E3SM), land model (ELMv0) is challenging because of its model complexity, strong model nonlinearity, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the ELMv0 parameters to improve model projection of carbon fluxes. We propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the ELMv0, and then calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated a sufficiently large number of times in the optimization, which facilitates the global search. We calibrate eight parameters against five years of net ecosystem exchange, total leaf area index, and latent heat flux data from the U.S. Missouri Ozark flux tower. The calibrated model is then used for predicting the three variables in the following 4 years. The results indicate that an accurate surrogate model can be created for the ELMv0 with a relatively small number of SG points, i.e., a few ELMv0 simulations that can be fully parallel. And, the application of the optimized parameters leads to a better model performance and a higher predictive capability than the default parameter values in the ELMv0.},
doi = {10.1002/2017MS001134},
journal = {Journal of Advances in Modeling Earth Systems},
number = 6,
volume = 10,
place = {United States},
year = {2018},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.1002/2017MS001134

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