Convergent modelling of past soil organic carbon stocks but divergent projections

Abstract

Abstract. Soil carbon (C) models are important tools for understanding soil C balance and projecting C stocks in terrestrial ecosystems, particularly under global change. The initialization and/or parameterization of soil C models can vary among studies even when the same model and data set are used, causing potential uncertainties in projections. Although a few studies have assessed such uncertainties, it is yet unclear what these uncertainties are correlated with and how they change across varying environmental and management conditions. Here, applying a process-based biogeochemical model to 90 individual field experiments (ranging from 5 to 82 years of experimental duration) across the Australian cereal-growing regions, we demonstrated that well-designed optimization procedures enabled the model to accurately simulate changes in measured C stocks, but did not guarantee convergent forward projections (100 years). Major causes of the projection uncertainty were due to insufficient understanding of how microbial processes and soil C pool change to modulate C turnover. For a given site, the uncertainty significantly increased with the magnitude of future C input and years of the projection. Across sites, the uncertainty correlated positively with temperature but negatively with rainfall. On average, a 331 % uncertainty in projected C sequestration ability can be inferred in Australian agricultural soils. This uncertainty would increase further if projections were made for future warming and drying conditions. Future improvement in soil C modelling should focus on how the microbial community and its C use efficiency change in response to environmental changes, and better conceptualization of heterogeneous soil C pools and the C transformation among those pools.

Uncertain future soil carbon dynamics under global change predicted by models constrained by total carbon measurements

“…The 100 years of soil C observations were further divided into 10 successive time lengths: 10, 20, … 100 years to represent data availability, i.e., assuming different time series observations of soil C data. The detailed optimization process was reported in Luo et al (2015). Briefly, the differential evolution performs a quasi-random walk through prior multi-dimensional parameter space (i.e., C f and C s at the start of the simulation experiment [C f,0 and C s,0 , respectively, hereafter], k f , k s , and e in this study) of the model, updates parameter space over the course of successive evolving generations, and finds parameter ensembles reaching the best agreement between observations and model predictions.…”

Predicted Land Carbon Dynamics Are Strongly Dependent on the Numerical Coupling of Nitrogen Mobilizing and Immobilizing Processes: A Demonstration with the E3SM Land Model

“…These uncertain responses were reported in several model intercomparison projects, such as phase 5 of the Coupled Model Intercomparison Project (CMIP5; Hoffman et al 2014;Shao et al 2013;Taylor et al 2012), the North American Carbon Program Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP; Huntzinger et al 2012Huntzinger et al , 2017, and the Trends and Drivers of the Regional-Scale Sources and Sinks of Carbon Dioxide project (TRENDY; http://dgvm.ceh.ac.uk/node/9). These large modeling differences were attributed to several factors, including uncertain input data, uncertain model structures, and uncertain model parameterizations (e.g., Blanke et al 2016;Clein et al 2007;Tang and Zhuang 2008;Luo et al 2015Luo et al , 2017Wieder et al 2015a,b). Further, ESMs have been criticized for ignoring nutrient controls on the terrestrial carbon cycle (e.g., Wieder et al 2015b).…”

Leveraging Environmental Research and Observation Networks to Advance Soil Carbon Science

“…These provide critical insights into how changes in biodiversity (Fornara & Tilman, ), nutrient enrichment (Frey et al, ; Riggs et al, ), agricultural management (Grandy & Robertson, ), and climate change (Melillo et al, ) alter SOM pools and fluxes. Such experiments provide much‐needed opportunity to investigate possible unexpected, dramatic SOM responses in particular ecosystems and improve our ability to model them, given that, even when soil models match observations at steady‐state or in the past, they often diverge under future conditions (Z. Luo et al, ; Sulman et al, ). Synthesizing results from perturbation experiments and integrating them into the formation and testing of SOM models are thus key needs (Knapp et al, ; Luo et al, ), and LTER data can play a central role.…”