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

文献类型：期刊论文

作者Tao, Feng9,10; Houlton, Benjamin Z.1,10; Huang, Yuanyuan8; Wang, Ying-Ping7; Manzoni, Stefano6; Ahrens, Bernhard5; Mishra, Umakant3,4; Jiang, Lifen2; Huang, Xiaomeng9; Luo, Yiqi2
刊名GLOBAL CHANGE BIOLOGY
出版日期2024-05-01
卷号30期号:5页码:19
关键词big data assimilation deep learning inter-model uncertainty model parameterization model structure soil organic carbon
ISSN号1354-1013
DOI10.1111/gcb.17297
英文摘要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. Our study demonstrates the critical role of observational data in reducing uncertainties of global soil organic carbon (SOC) simulations by structurally different models. Two process-based models structurally 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. Integrating common observational datasets with process-based models will be critical to inform model development, constrain predictions, and reveal new findings and patterns of key processes in the soil carbon cycle.image
WOS关键词EARTH SYSTEM MODELS ; USE EFFICIENCY ; TERRESTRIAL ECOSYSTEMS ; LITTER DECOMPOSITION ; MICROBIAL CARBON ; UNCERTAINTY ; MATTER ; COMMUNITY ; DYNAMICS ; STORAGE
资助项目National Natural Science Foundation of China ; National Key Research and Development Program of China[2020YFA0607900] ; National Key Research and Development Program of China[2020YFA0608000] ; National Key Research and Development Program of China[2022YFE0195900] ; National Key Research and Development Program of China[2021YFC3101600] ; National Key Scientific and Technological Infrastructure project Earth System Science Numerical Simulator Facility ; Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship ; Schmidt Futures program ; European Research Council (ERC) under the European Union[101001608] ; Horizon Europe project AI4SoilHealth[101086179] ; US Department of Energy ; US Department of Energy's National Nuclear Security Administration[DE-NA-0003525] ; US National Science Foundation (NSF)[DEB 1655499] ; US National Science Foundation (NSF)[DEB 2242034] ; US Department of Energy, Terrestrial Ecosystem Sciences[DE-SC0023514] ; US Department of Energy, Terrestrial Ecosystem Sciences[CW39470] ; Oak Ridge National Laboratory - US Department of Agriculture (USDA), New York State Department of Environmental Conservation ; New York State Department of Agriculture and Markets ; USDA National Institute of Food and Agriculture (NIFA) ; NSF National AI Research Institutes Competitive Award[2023-67021-39829] ; [42125503] ; [42075137]
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001219506700001
出版者WILEY
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Scientific and Technological Infrastructure project Earth System Science Numerical Simulator Facility ; Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship ; Schmidt Futures program ; European Research Council (ERC) under the European Union ; Horizon Europe project AI4SoilHealth ; US Department of Energy ; US Department of Energy's National Nuclear Security Administration ; US National Science Foundation (NSF) ; US Department of Energy, Terrestrial Ecosystem Sciences ; Oak Ridge National Laboratory - US Department of Agriculture (USDA), New York State Department of Environmental Conservation ; New York State Department of Agriculture and Markets ; USDA National Institute of Food and Agriculture (NIFA) ; NSF National AI Research Institutes Competitive Award
源URL[http://ir.igsnrr.ac.cn/handle/311030/205950]  
专题生态系统网络观测与模拟院重点实验室_外文论文
通讯作者Tao, Feng; Huang, Xiaomeng
作者单位1.Cornell Univ, Dept Global Dev, Ithaca, NY USA
2.Cornell Univ, Sch Integrat Plant Sci, Soil & Crop Sci Sect, Ithaca, NY USA
3.Lawrence Berkeley Natl Lab, Joint BioEnergy Inst, Emeryville, CA USA
4.Sandia Natl Labs, Computat Biol & Biophys, Livermore, CA USA
5.Max Planck Inst Biogeochem, Jena, Germany
6.Stockholm Univ, Bolin Ctr Climate Res, Dept Phys Geog, Stockholm, Sweden
7.CSIRO Environm, Clayton, Vic, Australia
8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
9.Tsinghua Univ, Inst Global Change Studies, Dept Earth Syst Sci, Minist Educ,Key Lab Earth Syst Modelling, Beijing 100084, Peoples R China
10.Cornell Univ, Dept Ecol & Evolutionary Biol, Ithaca, NY 14850 USA
推荐引用方式
GB/T 7714		 Tao, Feng,Houlton, Benjamin Z.,Huang, Yuanyuan,et al. Convergence in simulating global soil organic carbon by structurally different models after data assimilation[J]. GLOBAL CHANGE BIOLOGY,2024,30(5):19.
APA Tao, Feng.,Houlton, Benjamin Z..,Huang, Yuanyuan.,Wang, Ying-Ping.,Manzoni, Stefano.,...&Luo, Yiqi.(2024).Convergence in simulating global soil organic carbon by structurally different models after data assimilation.GLOBAL CHANGE BIOLOGY,30(5),19.
MLA Tao, Feng,et al."Convergence in simulating global soil organic carbon by structurally different models after data assimilation".GLOBAL CHANGE BIOLOGY 30.5(2024):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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