Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)

Ecological Processes

Yuanyuan Huang¹  Yiqi Luo²  Stephen Sitch³  Atul Jain⁴  Wei Xue⁵  Cuijuan Liao⁶  Feng Tao⁶  Yishuang Liang⁶  Yanluan Lin⁶  Xiaomeng Huang⁶  Jingmeng Wang⁶  Yizhao Chen⁷  Zhongyi Lin⁷  Almut Arneth⁸  Yansong Huang⁹  Xingjie Lu^1,10  Danica Lombardozzi^1,11  Daniel S. Goll^1,12 

[1]CSIRO Oceans and Atmosphere

[2]Center for Ecosystem Science and Society, Department of Biological Sciences, Northern Arizona University

[3]College of Life and Environmental Sciences, University of Exeter

[4]Department of Atmospheric Sciences, University of Illinois

[5]Department of Computer Science and Technology, Tsinghua University

[6]Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University

[7]Joint Innovation Center for Modern Forestry Studies, College of Biology and the Environment, Nanjing Forestry University

[8]Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research

[9]School of Life Science, Nanjing University

[10]Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University

[11]Terrestrial Sciences Section, Climate and Global Dynamics, National Center for Atmospheric Research

[12]Université Paris Saclay, CEA-CNRS-UVSQ, LSCE/IPSL

关键词: Soil organic carbon;    Inter-model comparison;    Uncertainty analysis;    Carbon–nitrogen coupling;    Vertical resolved soil biogeochemistry structure;   

DOI  :  10.1186/s13717-021-00356-8

来源: DOAJ

【 摘 要 】

Abstract Background 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. Results 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. Conclusions 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.

【 授权许可】

Unknown