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Volume 11, issue 7 | Copyright
Geosci. Model Dev., 11, 3027-3044, 2018
https://doi.org/10.5194/gmd-11-3027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 27 Jul 2018

Development and technical paper | 27 Jul 2018

Parameter calibration in global soil carbon models using surrogate-based optimization

Haoyu Xu1, Tao Zhang1,2, Yiqi Luo2,3, Xin Huang1,2, and Wei Xue1,2 Haoyu Xu et al.
  • 1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • 2Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084, China
  • 3Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA

Abstract. Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12% for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.

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This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of soil organic carbon. Experiments on three popular global soil carbon cycle models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. This research would help develop and improve the parameterization schemes of Earth climate systems.
This study proposes a new parameter calibration method based on surrogate optimization...
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