Matrix MIP to trace uncertainty in predicting land carbon dynamics

Abstract:

Model intercomparison projects (MIPs) usually show a large uncertainty in predicting land carbon (C) dynamics. However, the underlying causes remain not well understood. To trace sources of model uncertainty, we developed a matrix-based MIP (Matrix MIP) by converting six land C cycle models (i.e. DALEC2, TECO, FDBC, CASA, CLM4.5, and ORCHIDEE) to a unified matrix form. The six models differ greatly in complexity, with the number of C pools ranging from 6 in DALEC2 to 100 in ORCHIDEE. All six models were driven by the same gross primary production (GPP) and environmental variables (e.g., temperature and soil moisture) under ambient condition and in an elevated CO₂ (900 ppm) treatment at the SPRUCE experimental site. Despite all models used the same GPP, the variability found among the models become increasingly larger from simulated net primary production (NPP) to net ecosystem production (NEP) to soil organic carbon (SOC) dynamics. Our transient traceability analysis revealed that NPP diverges among the models due to differences in plant C use efficiency; NEP due to combined differences in C residence times and NPP; SOC dynamics predominantly due to differences in baseline soil C residence times. Our study shows that model uncertainty in predicting land C dynamics is understandable analytically after converting land C models into a unified matrix form.