Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)
Authors
Liao, C
Chen, Y
Wang, J
Liang, Y
Huang, Y
Lin, Z
Lu, X
Huang, Y
Tao, F
Lombardozzi, D
Arneth, A
Goll, DS
Jain, A
Sitch, S
Lin, Y
Xue, W
Huang, X
Luo, Y
Publication Date
2022Journal Title
Ecological Processes
ISSN
2192-1709
Publisher
Springer Science and Business Media LLC
Volume
11
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Liao, C., Chen, Y., Wang, J., Liang, Y., Huang, Y., Lin, Z., Lu, X., et al. (2022). Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP). Ecological Processes, 11 (1) https://doi.org/10.1186/s13717-021-00356-8
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>
Keywords
Research, Soil organic carbon, Inter-model comparison, Uncertainty analysis, Carbon–nitrogen coupling, Vertical resolved soil biogeochemistry structure
Sponsorship
National Key Research and Development Program of China (2017YFA0604600)
Identifiers
s13717-021-00356-8, 356
External DOI: https://doi.org/10.1186/s13717-021-00356-8
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333787
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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