Articles | Volume 9, issue 9
https://doi.org/10.5194/gmd-9-2999-2016
© Author(s) 2016. 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-9-2999-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
02 Sep 2016
Development and technical paper |
| 02 Sep 2016
Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0
Gregor J. Schürmann
CORRESPONDING AUTHOR
Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
Thomas Kaminski
The Inversion Lab, Hamburg, Germany
previously at: FastOpt, Hamburg, Germany
Christoph Köstler
Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
Nuno Carvalhais
Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
Michael Voßbeck
The Inversion Lab, Hamburg, Germany
previously at: FastOpt, Hamburg, Germany
Jens Kattge
Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
Ralf Giering
FastOpt, Hamburg, Germany
Christian Rödenbeck
Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
Martin Heimann
Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
Michel Stifel Centre Jena for Data-driven and Simulation Science, Jena, Germany
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32 citations as recorded by crossref.
- Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS) M. Wu et al. 10.1016/j.rse.2020.111719
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- Optimal model complexity for terrestrial carbon cycle prediction C. Famiglietti et al. 10.5194/bg-18-2727-2021
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- ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application P. Bosman & M. Krol 10.5194/gmd-16-47-2023
- Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide T. Kaminski et al. 10.1016/j.rse.2017.08.017
- A New Method for Top-Down Inversion Estimation of Carbon Dioxide Flux Based on Deep Learning H. Wang et al. 10.3390/rs16193694
- 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. 10.1016/j.agrformet.2016.12.004
- Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0) H. Zhu et al. 10.5194/gmd-17-6337-2024
- Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data N. MacBean et al. 10.1038/s41598-018-20024-w
- Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates T. Kaminski et al. 10.5194/bg-14-2527-2017
- No increase is detected and modeled for the seasonal cycle amplitude of δ13C of atmospheric carbon dioxide F. Joos et al. 10.5194/bg-22-19-2025
- A Framework for a Carbon-Based Urban Vegetation Typology M. Ariluoma et al. 10.2139/ssrn.4112904
- Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. 10.5194/bg-14-3401-2017
- Reviews and syntheses: Flying the satellite into your model: on the role of observation operators in constraining models of the Earth system and the carbon cycle T. Kaminski & P. Mathieu 10.5194/bg-14-2343-2017
- A framework for a carbon-based urban vegetation typology - A thematic review M. Ariluoma et al. 10.1016/j.envdev.2023.100899
- Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model M. Peaucelle et al. 10.1111/geb.12937
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- 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. 10.1029/2021GB007177
- 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. 10.1029/2019JG005040
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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. 10.3390/rs11010027
- Simulating the land carbon sink: Progresses and challenges of terrestrial ecosystem models W. Yuan et al. 10.1016/j.agrformet.2024.110264
- 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) V. Bastrikov et al. 10.5194/gmd-11-4739-2018
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- A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1) M. Forkel et al. 10.5194/gmd-10-4443-2017
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Latest update: 21 Apr 2025
Short summary
We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS). The system improves the modelled carbon cycle of the terrestrial biosphere by systematically confronting (or assimilating) the model with observations of atmospheric CO2 and fractions of absorbed photosynthetically active radiation. Jointly assimilating both data streams outperforms the single-data stream experiments, thus showing the value of a multi-data stream assimilation.
We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS). The...
