Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Tao, Feng; Houlton, Benjamin Z.; Huang, Yuanyuan; Wang, Ying‐Ping; Manzoni, Stefano; Ahrens, Bernhard; Mishra, Umakant; Jiang, Lifen; Huang, Xiaomeng; Luo, Yiqi

doi:10.1111/gcb.17297

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Journal Article · Mon May 13 00:00:00 EDT 2024 · Global Change Biology

DOI:https://doi.org/10.1111/gcb.17297· OSTI ID:2349564

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Cornell University, Ithaca, NY (United States); Tsinghua University, Beijing (China)
Cornell University, Ithaca, NY (United States)
Chinese Academy of Sciences (CAS), Beijing (China)
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, VIC (Australia)
Stockholm University (Sweden)
Max Planck Institute for Biogeochemistry, Jena (Germany)
Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States)
Tsinghua University, Beijing (China)

Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

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Research Organization:: Sandia National Lab. (SNL-CA), Livermore, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); US Department of Agriculture (USDA); National Institute of Food and Agriculture (NIFA); National Natural Science Foundation of China (NSFC); National Key Research and Development Program of China; Schmidt Futures; European Research Council (ERC); National Science Foundation (NSF)

Grant/Contract Number:: NA0003525; SC0023514; 42125503; 42075137; 2020YFA0607900; 2020YFA0608000; 2022YFE0195900; 2021YFC3101600; 101001608; 101086179; DEB 1655499; DEB 2242034; 2023-67021-39829

OSTI ID:: 2349564

Alternate ID(s):: OSTI ID: 2349566; OSTI ID: 2351010

Report Number(s):: SAND-2024-06232J

Journal Information:: Global Change Biology, Vol. 30, Issue 5; ISSN 1354-1013

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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