Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization. (30th June 2018)

• Record Type: Journal Article
• Authors: Lu, Dan; Ricciuto, Daniel; Stoyanov, Miroslav; Gu, Lianhong
• Is Part Of: Journal of advances in modeling earth systems. Volume 10:Number 6(2018)
• Page Start: 1337
• Page End: 1356
• Publication Date: 2018-06-30
• Subjects: E3SM land model -- global optimization -- surrogate modeling -- MOFLUX forest site; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1002/2017MS001134
• Languages: English
• ISSNs: 1942-2466

Abstract

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 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. Key Points: Propose a surrogate‐based global optimization method Apply the method to the E3SM land model for parameter optimization The optimization improves the model performance and prediction capability