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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23 citations as recorded by crossref.
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- Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates T. Kaminski et al. 10.5194/bg-14-2527-2017
- Terrestrial carbon cycle model-data fusion: Progress and challenges X. Li et al. 10.1007/s11430-020-9800-3
- Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations M. Forkel et al. 10.1038/s41598-019-55187-7
- 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
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- Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance A. Shiklomanov et al. 10.5194/gmd-14-2603-2021
- Consistent assimilation of multiple data streams in a carbon cycle data assimilation system N. MacBean et al. 10.5194/gmd-9-3569-2016
- Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide T. Kaminski et al. 10.1016/j.rse.2017.08.017
- 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
- 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
- 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
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- Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables M. Dantas de Paula et al. 10.1080/17538947.2019.1597187
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- A New Global fAPAR and LAI Dataset Derived from Optimal Albedo Estimates: Comparison with MODIS Products M. Disney et al. 10.3390/rs8040275
- Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers S. Sippel et al. 10.1371/journal.pone.0164960
- A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle P. Peylin et al. 10.5194/gmd-9-3321-2016
20 citations as recorded by crossref.
- Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data N. MacBean et al. 10.1038/s41598-018-20024-w
- 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
- Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates T. Kaminski et al. 10.5194/bg-14-2527-2017
- Terrestrial carbon cycle model-data fusion: Progress and challenges X. Li et al. 10.1007/s11430-020-9800-3
- Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations M. Forkel et al. 10.1038/s41598-019-55187-7
- 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
- Optimal model complexity for terrestrial carbon cycle prediction C. Famiglietti et al. 10.5194/bg-18-2727-2021
- 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
- Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model M. Peaucelle et al. 10.1111/geb.12937
- An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data T. Majasalmi et al. 10.5194/bg-15-399-2018
- Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance A. Shiklomanov et al. 10.5194/gmd-14-2603-2021
- Consistent assimilation of multiple data streams in a carbon cycle data assimilation system N. MacBean et al. 10.5194/gmd-9-3569-2016
- Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide T. Kaminski et al. 10.1016/j.rse.2017.08.017
- 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
- 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
- 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
- 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
- Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables M. Dantas de Paula et al. 10.1080/17538947.2019.1597187
- 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
- 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
3 citations as recorded by crossref.
- A New Global fAPAR and LAI Dataset Derived from Optimal Albedo Estimates: Comparison with MODIS Products M. Disney et al. 10.3390/rs8040275
- Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers S. Sippel et al. 10.1371/journal.pone.0164960
- A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle P. Peylin et al. 10.5194/gmd-9-3321-2016
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Latest update: 01 Jun 2023
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...
