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Abstract:
Soil carbon (C) is a critical component of Earth system models (ESMs) and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the 3rd to 5th assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. Firstly, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by 1st-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic SOC dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Secondly, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based datasets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Thirdly, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable datasets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.
