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 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.

Parametric Controls on Vegetation Responses to Biogeochemical Forcing in the CLM5

“…The majority of land surface model sensitivity tests, uncertainty quantification efforts, and calibration exercises are conducted under present‐day conditions (Dietze et al, ; Fer et al, ; Lu et al, ; Ricciuto et al, ). Predicted global biogeochemical feedbacks, however, are primarily related to the response of the system to forcing, and not necessarily to those system properties that control the mean state.…”

Surrogate‐Based Joint Estimation of Subsurface Geological and Relative Permeability Parameters for High‐Dimensional Inverse Problem by Use of Smooth Local Parameterization

“…Chang et al (2017) implemented a surrogate-based iterative ensemble smoother through developing both the PCE and the interpolation-based surrogate models in subsurface flow data assimilation problems. GPR has been increasing popularity as well in terms of predicting the tracer transport in groundwater models (Asher et al, 2015;Hombal & Mahadevan, 2011;Lu et al, 2018;Roy et al, 2018).…”

Evaluating the E3SM land model version 0 (ELMv0) at a temperate forest site using flux and soil water measurements

“…We attempted to improve the seasonal simulations of SR, GPP, and LAI by modifying several related parameters (Table 2). Using measurements of C and energy fluxes from the MOFLUX site, Lu et al (2018) calibrated a polynomial surrogate model of ELMv0. Based on their results, we modified two parameters, i.e., the SLA at the canopy top from 0.03 to 0.01 and the fraction of leaf nitrogen in the RuBisCO enzyme from 0.1007 to 0.12.…”

“…Globally, soils store over twice as much carbon (C) as the atmosphere (Chapin III et al, 2011). Soil respiration (SR) is the second largest C flux between terrestrial ecosystems and the atmosphere (Luo and Zhou, 2006). An accurate simulation of SR is critical for projecting terrestrial C status, and therefore climate change, in Earth system models (ESMs) (IPCC, 2013).…”