Articles | Volume 11, issue 24
https://doi.org/10.5194/bg-11-7137-2014
© Author(s) 2014. 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-11-7137-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Dec 2014
Research article |
| 16 Dec 2014
Ecosystem model optimization using in situ flux observations: benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances
D. Santaren
CORRESPONDING AUTHOR
Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
Laboratoire des Sciences du Climat et de l'Environnement, Commissariat à l'Energie Atomique, L'Orme des Merisiers, 91190 Gif sur Yvette, France
P. Peylin
Laboratoire des Sciences du Climat et de l'Environnement, Commissariat à l'Energie Atomique, L'Orme des Merisiers, 91190 Gif sur Yvette, France
C. Bacour
NOVELTIS, 153, rue du Lac, 31670 Labège, France
P. Ciais
Laboratoire des Sciences du Climat et de l'Environnement, Commissariat à l'Energie Atomique, L'Orme des Merisiers, 91190 Gif sur Yvette, France
B. Longdoz
INRA, UMR 1137, Ecologie et Ecophysiologie Forestires, Centre de Nancy, 54280 Champenoux, France
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- Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS M. Wu et al. https://doi.org/10.3390/rs11010027
- Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model C. Bacour et al. https://doi.org/10.1029/2019JG005040
- Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm J. Kallingal et al. https://doi.org/10.5194/gmd-17-2299-2024
- Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0) A. Druel et al. https://doi.org/10.5194/gmd-10-4693-2017
- Assimilating Sentinel-2 data in a modified vegetation photosynthesis and respiration model (VPRM) to improve the simulation of croplands CO2 fluxes in Europe H. Bazzi et al. https://doi.org/10.1016/j.jag.2024.103666
- Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model M. Peaucelle et al. https://doi.org/10.1111/geb.12937
- Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods S. Beylat et al. https://doi.org/10.5194/gmd-18-7501-2025
- Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization D. Lu et al. https://doi.org/10.1002/2017MS001134
- Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles F. Wang et al. https://doi.org/10.5194/hess-22-3863-2018
- Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model N. MacBean et al. https://doi.org/10.1029/2021GB007177
- A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle P. Peylin et al. https://doi.org/10.5194/gmd-9-3321-2016
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- The potential benefit of using forest biomass data in addition to carbon and water flux measurements to constrain ecosystem model parameters: Case studies at two temperate forest sites T. Thum et al. https://doi.org/10.1016/j.agrformet.2016.12.004
- Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle N. Raoult et al. https://doi.org/10.5194/bg-21-1017-2024
- Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter‐Annual Variability K. Mahmud et al. https://doi.org/10.1029/2021JG006400
- Using satellite data to improve the leaf phenology of a global terrestrial biosphere model N. MacBean et al. https://doi.org/10.5194/bg-12-7185-2015
- Assimilating ESA CCI land surface temperature into the ORCHIDEE land surface model: insights from a multi-site study across Europe L. Olivera-Guerra et al. https://doi.org/10.5194/hess-29-261-2025
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