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dc.contributor.authorLiao, C
dc.contributor.authorChen, Y
dc.contributor.authorWang, J
dc.contributor.authorLiang, Y
dc.contributor.authorHuang, Y
dc.contributor.authorLin, Z
dc.contributor.authorLu, X
dc.contributor.authorHuang, Y
dc.contributor.authorTao, F
dc.contributor.authorLombardozzi, D
dc.contributor.authorArneth, A
dc.contributor.authorGoll, DS
dc.contributor.authorJain, A
dc.contributor.authorSitch, S
dc.contributor.authorLin, Y
dc.contributor.authorXue, W
dc.contributor.authorHuang, X
dc.contributor.authorLuo, Y
dc.date.accessioned2022-02-09T13:22:27Z
dc.date.available2022-02-09T13:22:27Z
dc.date.issued2022
dc.date.submitted2021-03-10
dc.identifier.issn2192-1709
dc.identifier.others13717-021-00356-8
dc.identifier.other356
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333787
dc.description.abstract<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Large uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty.</jats:p> </jats:sec>
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectResearch
dc.subjectSoil organic carbon
dc.subjectInter-model comparison
dc.subjectUncertainty analysis
dc.subjectCarbon–nitrogen coupling
dc.subjectVertical resolved soil biogeochemistry structure
dc.titleDisentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)
dc.typeArticle
dc.date.updated2022-02-09T13:22:26Z
prism.issueIdentifier1
prism.publicationNameEcological Processes
prism.volume11
dc.identifier.doi10.17863/CAM.81204
dcterms.dateAccepted2021-12-28
rioxxterms.versionofrecord10.1186/s13717-021-00356-8
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2192-1709
pubs.funder-project-idNational Key Research and Development Program of China (2017YFA0604600)
cam.issuedOnline2022-02-08


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