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Convergence in simu...
Convergence in simulating global soil organic carbon by structurally different models after data assimilation
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Tao, Feng (author)
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Houlton, Benjamin Z. (author)
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Huang, Yuanyuan (author)
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Wang, Ying-Ping (author)
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- Manzoni, Stefano, 1979- (author)
- Stockholms universitet,Institutionen för naturgeografi,Bolincentret för klimatforskning (tills m KTH & SMHI)
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Ahrens, Bernhard (author)
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Mishra, Umakant (author)
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Jiang, Lifen (author)
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Huang, Xiaomeng (author)
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Luo, Yiqi (author)
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Global Change Biology. - 1354-1013 .- 1365-2486. ; 30:5
- Related links:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Klimatforskning (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Climate Research (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Multidisciplinär geovetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Geosciences, Multidisciplinary (hsv//eng)
Keyword
- big data assimilation
- deep learning
- inter-model uncertainty
- model parameterization
- model structure
- soil organic carbon
Publication and Content Type
- ref (subject category)
- art (subject category)
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- By the author/editor
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Tao, Feng
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Houlton, Benjami ...
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Huang, Yuanyuan
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Wang, Ying-Ping
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Manzoni, Stefano ...
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Ahrens, Bernhard
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show more...
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Mishra, Umakant
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Jiang, Lifen
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Huang, Xiaomeng
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Luo, Yiqi
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show less...
- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Earth and Relate ...
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and Climate Research
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Earth and Relate ...
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and Geosciences Mult ...
- Articles in the publication
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Global Change Bi ...
- By the university
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Stockholm University