Matrix‐Based Sensitivity Assessment of Soil Organic Carbon Storage: A Case Study from the ORCHIDEE‐MICT Model

Abstract

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 Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE)‐aMeliorated Interactions between Carbon and Temperature (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. The stronger impact of active layer depth on SOC comes from regions with shallow active layer depth (e.g., the northernmost part of America, Asia, and some Greenland regions). 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 nonrecalcitrant SOC. We envision a future with more comprehensive model intercomparisons and assessments with an ensemble of land carbon models adopting the matrix‐based sensitivity framework.

Global soil organic carbon removal by water erosion under climate change and land use change during AD 1850–2005

“…It is also uncertain how sensitive SOC is to these processes. For example, the study of Huang et al (2018), who implemented a matrix-based approach to assess the sensitivity of SOC, showed that equilibrium SOC stocks are more sensitive to input than to mixing for soils in the temperate and high-latitude regions.…”

Accelerated terrestrial ecosystem carbon turnover and its drivers

“…In a previous model evaluation (Guimberteau et al., 2018), it was showed that ORCHIDEE‐MICT could capture the mean value and average vertical profile of soil organic carbon density in high‐latitude regions. Another study by Huang et al using a matrix‐based representation of ORCHIDEE‐MICT soil organic carbon also evaluated systematically the sensitive parameters among 34 parameters that control soil organic carbon profiles and found critical sensitivities to the active layer thickness and the cryoturbation rate (Huang et al., 2018). Here, to further improve the robustness of soil carbon turnover processes, we recommend that future versions should consider the radiocarbon data, which provide an independent constrain for the total soil carbon turnover (He et al., 2016; Lawrence et al., 2019; Mathieu, Hatté, Balesdent, & Parent, 2015; Trumbore, Sierra, & Hicks Pries, 2016).…”

Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

“…The elevated computational efficiency by SASU enables parameter sensitivity analysis (Huang, Zhu, et al, 2018) and pool‐based data assimilation (Hararuk et al, 2015; Hararuk & Luo, 2014; Shi et al, 2018). Sensitivity of the carbon storage from 34 parameters have been evaluated (Huang, Zhu, et al, 2018). The active layer depth among 34 parameters has been identified to play important role in predicting high‐latitude soil organic carbon.…”

“…The matrix approach was initially implemented in the Terrestrial ECOsystem model (TECO) for data assimilation studies (Luo et al, 2003;Xu et al, 2006). The matrix approach has recently been applied to global land models, such as the Community Atmosphere-Biosphere-Land Exchange model (CABLE) (Xia et al, 2012(Xia et al, , 2013, LPJ-GUESS (Ahlstrom et al, 2015), ORCHIDEE (Huang, Zhu, et al, 2018), CLM3.5 (Hararuk et al, 2015;Hararuk & Luo, 2014;Rafique et al, 2016), CLM4 (Rafique et al, 2017), CLM4.5 (Huang, Lu, et al, 2018), and CLM5 in this study. The CABLE matrix model was used to accelerate its spin-up (Xia et al, 2012) and for traceability analysis to trace sources of uncertainty in model simulations of terrestrial carbon storage (Xia et al, 2013).…”