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

作　　者：Cuijuan Liao Yizhao Chen Jingmeng Wang Yishuang Liang Yansong Huang Zhongyi Lin Xingjie Lu Yuanyuan Huang Feng Tao Danica Lombardozzi Almut Arneth Daniel SGoll Atul Jain Stephen Sitch Yanluan Lin Wei Xue Xiaomeng Huang Yiqi Luo 

机构地区：[1]Department of Earth System Science,Ministry of Education Key Laboratory for Earth System Modeling,Institute for Global Change Studies,Tsinghua University,Beijing,China [2]Joint Innovation Center for Modern Forestry Studies,College of Biology and the Environment,Nanjing Forestry University,Nanjing,China [3]Department of Geography,University of Cambridge,Cambridge,UK [4]School of Life Science,Nanjing University,Nanjing,China [5]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,Guangzhou,China [6]CSIRO Oceans and Atmosphere,Aspendale 3195,Australia [7]Terrestrial Sciences Section,Climate and Global Dynamics,National Center for Atmospheric Research,Boulder,CO 80305,USA [8]Karlsruhe Institute of Technology,Institute of Meteorology and Climate Research/Atmospheric Environmental Research,Garmisch-Partenkirchen,Germany [9]UniversitéParis Saclay,CEA-CNRS-UVSQ,LSCE/IPSL,Gif sur Yvette,France [10]Department of Atmospheric Sciences,University of Illinois,Urbana,IL,USA [11]College of Life and Environmental Sciences,University of Exeter,Exeter,UK [12]Department of Computer Science and Technology,Tsinghua University,Beijing,China [13]Center for Ecosystem Science and Society,Department of Biological Sciences,Northern Arizona University,Flagstaff,AZ,USA

出　　处：《Ecological Processes》2022年第1期222-237,共16页生态过程（英文）

基　　金：This study is supported by the funding from the National Key Research and Development Program of China under grants 2017YFA0604600;YC was supported by National Youth Science Fund of China(41701227).DL is supported by the National Center for Atmospheric Research,which is a major facility sponsored by the National Science Foundation(NSF)under Cooperative Agreement 1852977.DL’s computing and data storage resources,including the Cheyenne supercomputer(https://doi.org/10.5065/D6RX99HX),were provided by the Computational and Information Systems Laboratory(CISL)at NCAR.DSG receives support from the ANR CLAND Convergence Institute.

摘　　要：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.

关 键 词：Soil organic carbon Inter-model comparison Uncertainty analysis Carbon-nitrogen coupling Vertical resolved soil biogeochemistry structure 

分 类 号：S15[农业科学—土壤学]