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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-141
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-141
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 02 Jul 2020

Submitted as: methods for assessment of models | 02 Jul 2020

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Soil carbon estimates by Yasso15 model improved with state data assimilation

Toni Viskari1, Maisa Laine1, Liisa Kulmala1,2,3, Jarmo Mäkela1, Istem Fer1, and Jari Liski1 Toni Viskari et al.
  • 1Finnish Meteorological Institute, Helsinki, 00101, Finland
  • 2Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland
  • 3Institute for Atmospheric Sciences and Earth System Research, University of Helsinki, Helsinki, Finland

Abstract. Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied if state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurements time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades long projections in by at least 1 t C ha−1. Our results show the benefits of implementing SDA methods for forecasting SOC, but also highlight implementation aspects that need consideration and further research.

Toni Viskari et al.

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Toni Viskari et al.

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Data and model used in 'Soil carbon estimates by Yasso15 model improved with state data assimilation ' T. Viskari, M. Laine, L. Kulmala, J. Mäkelä, I. Fer, and J. Jari https://doi.org/10.5281/zenodo.3891133

Toni Viskari et al.

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Latest update: 28 Oct 2020
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Short summary
The research here established if a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a Fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
The research here established if a Bayesian statistical method called state data assimilation...
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