Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-1899-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-1899-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
01 Jul 2015
Methods for assessment of models |
| 01 Jul 2015
Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model
C. Safta
CORRESPONDING AUTHOR
Sandia National Labs, Livermore, CA 94551, USA
D. M. Ricciuto
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
K. Sargsyan
Sandia National Labs, Livermore, CA 94551, USA
B. Debusschere
Sandia National Labs, Livermore, CA 94551, USA
H. N. Najm
Sandia National Labs, Livermore, CA 94551, USA
M. Williams
School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, EH9 EJN, UK
P. E. Thornton
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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Cited
21 citations as recorded by crossref.
- On the effect of model parameters on forecast objects C. Marzban et al. 10.5194/gmd-11-1577-2018
- What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models R. Sheikholeslami et al. 10.5194/gmd-12-4275-2019
- Evaluation and improvement of the E3SM land model for simulating energy and carbon fluxes in an Amazonian peatland F. Yuan et al. 10.1016/j.agrformet.2023.109364
- Shifts in national land use and food production in Great Britain after a climate tipping point P. Ritchie et al. 10.1038/s43016-019-0011-3
- Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization D. Lu et al. 10.1002/2017MS001134
- Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods D. Lu et al. 10.5194/bg-14-4295-2017
- The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model D. Ricciuto et al. 10.1002/2017MS000962
- Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach L. Hu et al. 10.3390/f11040369
- A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF C. Marzban et al. 10.1175/MWR-D-19-0271.1
- Assessing the ecological vulnerability of the upper reaches of the Minjiang River J. Zhang et al. 10.1371/journal.pone.0181825
- Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion X. Ren et al. 10.1016/j.ecolmodel.2018.03.013
- Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Noncontinuous Model Parameters C. Marzban et al. 10.1175/MWR-D-19-0321.1
- Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling N. Hsieh et al. 10.3389/fphar.2018.00588
- Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM J. Wang et al. 10.1029/2021MS002628
- Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application S. Delahaies et al. 10.5194/gmd-10-2635-2017
- Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation H. He et al. 10.1038/s41597-021-00826-w
- A model-independent data assimilation (MIDA) module and its applications in ecology X. Huang et al. 10.5194/gmd-14-5217-2021
- 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. 10.5194/gmd-15-6637-2022
- A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data R. Sheikholeslami & S. Razavi 10.1029/2020GL089829
- Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates H. Post et al. 10.5194/bg-15-187-2018
- Accounting for foliar gradients in Vcmax and Jmax improves estimates of net CO2 exchange of forests C. Bachofen et al. 10.1016/j.agrformet.2021.108771
21 citations as recorded by crossref.
- On the effect of model parameters on forecast objects C. Marzban et al. 10.5194/gmd-11-1577-2018
- What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models R. Sheikholeslami et al. 10.5194/gmd-12-4275-2019
- Evaluation and improvement of the E3SM land model for simulating energy and carbon fluxes in an Amazonian peatland F. Yuan et al. 10.1016/j.agrformet.2023.109364
- Shifts in national land use and food production in Great Britain after a climate tipping point P. Ritchie et al. 10.1038/s43016-019-0011-3
- Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization D. Lu et al. 10.1002/2017MS001134
- Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods D. Lu et al. 10.5194/bg-14-4295-2017
- The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model D. Ricciuto et al. 10.1002/2017MS000962
- Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach L. Hu et al. 10.3390/f11040369
- A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF C. Marzban et al. 10.1175/MWR-D-19-0271.1
- Assessing the ecological vulnerability of the upper reaches of the Minjiang River J. Zhang et al. 10.1371/journal.pone.0181825
- Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion X. Ren et al. 10.1016/j.ecolmodel.2018.03.013
- Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Noncontinuous Model Parameters C. Marzban et al. 10.1175/MWR-D-19-0321.1
- Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling N. Hsieh et al. 10.3389/fphar.2018.00588
- Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM J. Wang et al. 10.1029/2021MS002628
- Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application S. Delahaies et al. 10.5194/gmd-10-2635-2017
- Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation H. He et al. 10.1038/s41597-021-00826-w
- A model-independent data assimilation (MIDA) module and its applications in ecology X. Huang et al. 10.5194/gmd-14-5217-2021
- 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. 10.5194/gmd-15-6637-2022
- A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data R. Sheikholeslami & S. Razavi 10.1029/2020GL089829
- Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates H. Post et al. 10.5194/bg-15-187-2018
- Accounting for foliar gradients in Vcmax and Jmax improves estimates of net CO2 exchange of forests C. Bachofen et al. 10.1016/j.agrformet.2021.108771
Saved (final revised paper)
Latest update: 02 Nov 2024
Short summary
In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model and focus on the comparison between steady-state and transient
simulation setups. We study model parameters via global sensitivity analysis and employ a Bayesian approach to calibrate these parameters using NEE observations at the Harvard Forest site. The calibration results are then used to assess the predictive skill of the model via posterior predictive checks.
In this paper we propose a probabilistic framework for an uncertainty quantification study of a...
