Articles | Volume 14, issue 18
https://doi.org/10.5194/bg-14-4295-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-14-4295-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Sep 2017
Research article |
| 27 Sep 2017
Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods
Dan Lu
CORRESPONDING AUTHOR
Computational Sciences and Engineering Division, Climate Change
Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Daniel Ricciuto
Environmental Sciences Division, Climate Change Science Institute, Oak
Ridge National Laboratory, Oak Ridge, TN, USA
Anthony Walker
Environmental Sciences Division, Climate Change Science Institute, Oak
Ridge National Laboratory, Oak Ridge, TN, USA
Cosmin Safta
Sandia National Laboratories, Livermore, CA, USA
William Munger
School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
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32 citations as recorded by crossref.
- Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction D. Lu et al. https://doi.org/10.3389/fenrg.2021.752185
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- Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields H. Benninga et al. https://doi.org/10.1016/j.hydroa.2020.100066
- Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization D. Lu et al. https://doi.org/10.1002/2017MS001134
- Ensemble Numerical Experiments with Earth System Models A. Eliseev https://doi.org/10.1007/s11141-026-10472-0
- An adaptive Kriging surrogate method for efficient joint estimation of hydraulic and biochemical parameters in reactive transport modeling J. Zhou et al. https://doi.org/10.1016/j.jconhyd.2018.08.005
- A filter for tracking non-cooperative low-thrust satellites using surveillance radar data G. Escribano et al. https://doi.org/10.1016/j.actaastro.2023.09.026
- Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model M. Saifuddin et al. https://doi.org/10.1029/2021JG006593
- Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation G. Escribano et al. https://doi.org/10.1016/j.asr.2022.02.034
- Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation I. Fer et al. https://doi.org/10.5194/bg-15-5801-2018
- Bayesian calibration of simple forest models with multiplicative mathematical structure: A case study with two Light Use Efficiency models in an alpine forest M. Bagnara et al. https://doi.org/10.1016/j.ecolmodel.2018.01.014
- Sequential Likelihood-Free Inference with Neural Proposal D. Kim et al. https://doi.org/10.1016/j.patrec.2023.03.021
- Beyond deterministic air quality modeling: a probabilistic screening approach for emission inputs in AERMOD Z. Emerson & T. Khan https://doi.org/10.1093/inteam/vjaf098
- Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling A. Chen et al. https://doi.org/10.1029/2022MS003135
- Simulating the land carbon sink: Progresses and challenges of terrestrial ecosystem models W. Yuan et al. https://doi.org/10.1016/j.agrformet.2024.110264
- A model-independent data assimilation (MIDA) module and its applications in ecology X. Huang et al. https://doi.org/10.5194/gmd-14-5217-2021
- Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo V. Aakula et al. https://doi.org/10.1016/j.eja.2025.127769
- Ecosystem service tradeoff and synergy mechanisms in the Central Asian terminal lake basin based on Bayesian Networks J. Li et al. https://doi.org/10.1016/j.ecoser.2025.101768
- A Bayesian inversion framework to evaluate parameter and predictive inference of a simple soil respiration model in a cool-temperate forest in western Japan M. Toda et al. https://doi.org/10.1016/j.ecolmodel.2019.108918
- An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model R. Zhao et al. https://doi.org/10.1016/j.geoderma.2022.116283
- An Integrative Model for Soil Biogeochemistry and Methane Processes: I. Model Structure and Sensitivity Analysis D. Ricciuto et al. https://doi.org/10.1029/2019JG005468
- A deep learning-based direct forecasting of CO2 plume migration M. Fan et al. https://doi.org/10.1016/j.geoen.2022.211363
- Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. https://doi.org/10.5194/gmd-15-6637-2022
- Calibrating the soil organic carbon model Yasso20 with multiple datasets T. Viskari et al. https://doi.org/10.5194/gmd-15-1735-2022
- Biophysically Informed Imaging Acquisition of Plant Water Status D. Beverly et al. https://doi.org/10.3389/ffgc.2020.589493
- Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations S. Wang & Y. Wang https://doi.org/10.1007/s00382-019-04702-7
- Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques D. Lu & D. Ricciuto https://doi.org/10.5194/gmd-12-1791-2019
- Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model M. Sheng et al. https://doi.org/10.1016/j.agrformet.2019.107659
- A semiempirical model for horizontal distribution of surface wind speed leeward windbreaks F. Yuan et al. https://doi.org/10.1007/s10457-019-00417-0
- Combining Eddy Covariance Towers, Field Measurements, and the MEMS 2 Ecosystem Model Improves Confidence in the Climate Impacts of Bioenergy With Carbon Capture and Storage G. Falvo et al. https://doi.org/10.1111/gcbb.70023
- Hyperspectral surface reflectance improves GPP estimation in terrestrial biosphere modeling using model-data fusion H. Liu et al. https://doi.org/10.1016/j.rse.2025.114989
- Considering coasts: Adapting terrestrial models to characterize coastal wetland ecosystems T. O'Meara et al. https://doi.org/10.1016/j.ecolmodel.2021.109561
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Latest update: 01 Jun 2026
Short summary
Calibration of terrestrial ecosystem models (TEMs) is important but challenging. This study applies an advanced sampling technique for parameter estimation of a TEM. The results improve the model fit and predictive performance.
Calibration of terrestrial ecosystem models (TEMs) is important but challenging. This study...
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