Articles | Volume 7, issue 6
https://doi.org/10.5194/gmd-7-2581-2014
https://doi.org/10.5194/gmd-7-2581-2014
Development and technical paper
 | 
10 Nov 2014
Development and technical paper |  | 10 Nov 2014

Model–data fusion across ecosystems: from multisite optimizations to global simulations

S. Kuppel, P. Peylin, F. Maignan, F. Chevallier, G. Kiely, L. Montagnani, and A. Cescatti

Related authors

Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)
Vladislav Bastrikov, Natasha MacBean, Cédric Bacour, Diego Santaren, Sylvain Kuppel, and Philippe Peylin
Geosci. Model Dev., 11, 4739–4754, https://doi.org/10.5194/gmd-11-4739-2018,https://doi.org/10.5194/gmd-11-4739-2018, 2018
Short summary
EcH2O-iso 1.0: water isotopes and age tracking in a process-based, distributed ecohydrological model
Sylvain Kuppel, Doerthe Tetzlaff, Marco P. Maneta, and Chris Soulsby
Geosci. Model Dev., 11, 3045–3069, https://doi.org/10.5194/gmd-11-3045-2018,https://doi.org/10.5194/gmd-11-3045-2018, 2018
Short summary
A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle
Philippe Peylin, Cédric Bacour, Natasha MacBean, Sébastien Leonard, Peter Rayner, Sylvain Kuppel, Ernest Koffi, Abdou Kane, Fabienne Maignan, Frédéric Chevallier, Philippe Ciais, and Pascal Prunet
Geosci. Model Dev., 9, 3321–3346, https://doi.org/10.5194/gmd-9-3321-2016,https://doi.org/10.5194/gmd-9-3321-2016, 2016
Short summary
Quantifying the model structural error in carbon cycle data assimilation systems
S. Kuppel, F. Chevallier, and P. Peylin
Geosci. Model Dev., 6, 45–55, https://doi.org/10.5194/gmd-6-45-2013,https://doi.org/10.5194/gmd-6-45-2013, 2013

Related subject area

Biogeosciences
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024,https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
In silico calculation of soil pH by SCEPTER v1.0
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024,https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024,https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
A global behavioural model of human fire use and management: WHAM! v1.0
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024,https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024,https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary

Cited articles

Baldocchi, D.: TURNER REVIEW No. 15. "Breathing" of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems, Aust. J. Bot., 56, 1–26, 2008.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., and Evans, R.: FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, 2001.
Bonan, G. B., Oleson, K. W., Fisher, R. A., Lasslop, G., and Reichstein, M.: Reconciling leaf physiological traits and canopy flux data: use of the TRY and FLUXNET databases in the Community Land Model version 4, J. Geophys. Res.-Biogeo., 117, G02026, https://doi.org/10.1029/2011JG001913, 2012.
Botta, A., Viovy, N., Ciais, P., Friedlingstein, P., and Monfray, P.: A global prognostic scheme of leaf onset using satellite data, Glob. Change Biol., 6, 709–725, 2000.
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, Glob. Change Biol., 11, 335–355, https://doi.org/10.1111/j.1365-2486.2005.00897.x, 2005.
Download
Short summary
A consistent calibration of an advanced land surface model was performed by grouping in situ information on land-atmosphere exchanges of carbon and water using broad ecosystem and climate classes. Signatures of improved carbon cycle simulations were found across spatial and temporal scales, along with insights into current model limitations. These results hold promising perspectives within the ongoing efforts towards building robust model-data fusion frameworks for earth system models.