Demonstration code and input data for Matrix-based sensitivity assessment of soil organic carbon storage: A case study from the ORCHIDEE-MICT model

  • Yuanyuan Huang (Contributor)
  • Dan Zhu (Contributor)
  • Philippe Ciais (Contributor)
  • Bertrand Guenet (Contributor)
  • Ye Huang (Contributor)
  • Daniel Goll (Contributor)
  • Matthieu Guimberteau (Contributor)
  • Albert Jornet-Puig (Contributor)
  • Xingjie Lu (Contributor)
  • Yiqi Luo (Contributor)



Modeling of global soil organic carbon (SOC) is accompanied by large uncertainties. The heavy computational requirement limits our flexibility in disentangling uncertainty sources especially in high latitudes. We build a structured sensitivity analyzing framework through reorganizing the ORCHIDEE-MICT model with vertically discretized SOC into one matrix equation, which brings flexibility in comprehensive sensitivity assessment. Through Sobol's method enabled by the matrix, we systematically rank 34 relevant parameters according to variance explained by each parameter and find a strong control of carbon input and turnover time on long-term SOC storages. From further analyses for each soil layer and regional assessment, we find that the active layer depth plays a critical role in the vertical distribution of SOC and SOC equilibrium stocks in northern high latitudes (>50˚N). However, the impact of active layer depth on SOC is highly interactive and nonlinear, varying across soil layers and grid cells. SOC from regions with low active layer depth (e.g., the northernmost part of America, Asia and some Greenland regions) is most vulnerable to active layer depth in terms of relative changes. The model is sensitive to the parameter that controls vertical mixing (cryoturbation rate) but only when the vertical carbon input from vegetation is limited since the effect of vertical mixing is relatively small. And the current model structure may still lack mechanisms that effectively bury non-recalcitrant SOC. We envision a future with more comprehensive model inter-comparisons and assessments with an ensemble of land carbon models adopting the matrix-based sensitivity framework.
Date made availableJan 13 2023

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