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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31 citations as recorded by crossref.
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- 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
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- 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
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- 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
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- Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations K. Castro-Morales et al. 10.5194/bg-16-3009-2019
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Saved (preprint)
Latest update: 14 Dec 2024
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...
