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. Soil organic carbon (SOC) models are important tools for assessingglobal SOC distributions and how carbon stocks are affected by climatechange. Their performances, however, are affected by data and methods usedto calibrate them. Here we study how a new version of the Yasso SOC model, herenamed Yasso20, performs if calibrated individually or with multiple datasetsand how the chosen calibration method affects the parameter estimation. Wealso compare Yasso20 to the previous version of the Yasso model. We foundthat when calibrated with multiple datasets, the model showed a betterglobal performance compared to a single-dataset calibration. Furthermore,our results show that more advanced calibration algorithms should be usedfor SOC models due to multiple local maxima in the likelihood space. Thecomparison showed that the resulting model performed better with thevalidation data than the previous version of Yasso.

Calibrating the soil organic carbon model Yasso20 with multiple datasets