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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 9 | Copyright
Geosci. Model Dev., 9, 2999-3026, 2016
https://doi.org/10.5194/gmd-9-2999-2016
© Author(s) 2016. This work is distributed under
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. Schürmann1, Thomas Kaminski2,a, Christoph Köstler1, Nuno Carvalhais1, Michael Voßbeck2,a, Jens Kattge1, Ralf Giering3, Christian Rödenbeck1, Martin Heimann1, and Sönke Zaehle1,4 Gregor J. Schürmann et al.
  • 1Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
  • 2The Inversion Lab, Hamburg, Germany
  • 3FastOpt, Hamburg, Germany
  • 4Michel Stifel Centre Jena for Data-driven and Simulation Science, Jena, Germany
  • apreviously at: FastOpt, Hamburg, Germany

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the JSBACH land-surface scheme, which is part of the MPI-Earth System Model v1. The simulated phenology and net land carbon balance were constrained by globally distributed observations of the fraction of absorbed photosynthetically active radiation (FAPAR, using the TIP-FAPAR product) and atmospheric CO2 at a global set of monitoring stations for the years 2005 to 2009. When constrained by FAPAR observations alone, the system successfully, and computationally efficiently, improved simulated growing-season average FAPAR, as well as its seasonality in the northern extra-tropics. When constrained by atmospheric CO2 observations alone, global net and gross carbon fluxes were improved, despite a tendency of the system to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, thereby increasing the overall appropriateness of the simulated biosphere dynamics and underlying parameter values. Our study thus demonstrates the value of multiple-data-stream assimilation for the simulation of terrestrial biosphere dynamics. It further highlights the potential role of remote sensing data, here the TIP-FAPAR product, in stabilising the strongly underdetermined atmospheric inversion problem posed by atmospheric transport and CO2 observations alone. Notwithstanding these advances, the constraint of the observations on regional gross and net CO2 flux patterns on the MPI-CCDAS is limited through the coarse-scale parametrisation of the biosphere model. We expect improvement through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

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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...
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