Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-1899-2015
https://doi.org/10.5194/gmd-8-1899-2015
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, D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams, and P. E. Thornton

Related authors

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods
Dan Lu, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger
Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017,https://doi.org/10.5194/bg-14-4295-2017, 2017
Short summary

Related subject area

Climate and Earth system modeling
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025,https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025,https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025,https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025,https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025,https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary

Cited articles

Barr, A., Hollinger, D., and Richardson, A. D.: CO2 Flux Measurement Uncertainty Estimates for NACP, AGU Fall Meeting, December 2009, abstract number B54A-04B, 2009.
Barr, A., Ricciuto, D. M., Schaefer, K., Richardson, A., Agarwal, D., Thornton, P. E., Davis, K., Jackson, B., Cook, R. B., Hollinger, D. Y., van Ingen, C., Amiro, B., ans M. A. Arain, A. A., Baldocchi, D., Black, T. A., Bolstad, P., Curtis, P., Desai, A., Dragoni, D., Flanagan, L., Gu, L., Katul, G., Law, B. E., Lafleur, P., Margolis, H., Matamala, R., Meyers, T., McCaughey, H., Monson, R., Munger, J. W., Oechel, W., Oren, R., Roulet, N., Torn, M., and Verma, S.: NACP Site: Tower Meteorology, Flux Observations with Uncertainty, and Ancillary Data, available at: http://daac.ornl.gov (last access: 10 June 2015) from Oak Ridge National Laboratory Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/1178, 2013.
Braswell, B. H., Sacks, W. J., Linder, E., and Schimel, D. S.: Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations, Global Change Biol., 11, 335–355, https://doi.org/10.1111/j.1365-2486.2005.00897.x, 2005.
Campolongo, F., Saltelli, A., Sørensen, T., and Tarantola, S.: Hitchhiker's Guide to Sensitivity Analysis, in: Sensitivity Analysis, edited by: Saltelli, A., Chan, K., and Scott, E., Wiley, Chicester, 2000.
Fox, A., Williams, M., Richardson, A. D., Cameron, D., Gove, J. H., Quaife, T., Ricciuto, D. M., Reichstein, M., Tomelleri, E., Trudinger, C. M., and Wijk, M. T. V.: The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data, Agric. For. Meteorol., 149, 1597–1615, https://doi.org/10.1016/j.agrformet.2009.05.002, 2009.
Download
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.
Share