Submitted as: development and technical paper 24 Sep 2021

Submitted as: development and technical paper | 24 Sep 2021

Review status: this preprint is currently under review for the journal GMD.

The impact of calibrating soil organic carbon model Yasso with multiple datasets

Toni Viskari1, Janne Pusa1, Istem Fer1, Anna Repo2, Julius Vira1, and Jari Liski1 Toni Viskari et al.
  • 1Finnish Meteorological Institute, Helsinki, 00101, Finland
  • 2Natural Resource Center Finland, Helsinki, 00791, Finland

Abstract. Soil Organic Carbon (SOC) models are important tools in determining global SOC distributions and how carbon stocks are affected by climate change. Their performances are, however, affected by data and methods used to calibrate them. Here we study how the Yasso SOC model performs if calibrated individually or with multiple datasets and how the chosen calibration method affected the parameter estimation. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to the multiple local maximas in the likelihood space.

Toni Viskari et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-273', Astrid Kerkweg, 04 Oct 2021
  • RC1: 'Comment on gmd-2021-273', Anonymous Referee #1, 09 Oct 2021
  • RC2: 'Comment on gmd-2021-273', Florian Hartig, 24 Oct 2021
  • AC1: 'Comment on gmd-2021-273', Toni Viskari, 17 Dec 2021

Toni Viskari et al.

Toni Viskari et al.


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Short summary
We wanted to examine how the chosen measurement data and calibration process itself affects soil organic carbon model calibration. In our results we found that there is benefit in using data multiple litterbag decomposition experiments simultaneously, even with the required assumptions. Additionally due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.