The Terrestrial Biosphere Model Farm.
The Terrestrial Biosphere Model Farm.
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陆地生物圈模型农场。
DOI：
10.1029/2021ms002676
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发表时间：
2022-03
影响因子：
6.8
通讯作者：
Huntzinger, Deborah N.
中科院分区：
文献类型：
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作者：
Fisher, Joshua B.;Sikka, Munish;Block, Gary L.;Schwalm, Christopher R.;Parazoo, Nicholas C.;Kolus, Hannah R.;Sok, Malen;Wang, Audrey;Gagne-Landmann, Anna;Lawal, Shakirudeen;Guillaume, Alexandre;Poletti, Alyssa;Schaefer, Kevin M.;Masri, Bassil;Levy, Peter E.;Wei, Yaxing;Dietze, Michael C.;Huntzinger, Deborah N.
关键词：
模型相互比较项目（MIP）是我们了解陆地表面如何响应气候变化的基础。然而，MIP的实施具有挑战性，需要在世界各地组织多个分散的建模团队，运行通用协议。我们探索了将这些模型集中在单个超级计算系统上。我们通过陆地生物圈模型农场运行了九个离线陆地生物圈模型：CABLE，CENTURY，HyLand，ISAM，JULES，LPJ ‐ GUESS，ORCHIDEE，SiB ‐ 3和SiB ‐ CASA。所有的模式都被包裹在一个软件框架中，该框架由1901 - 2100年的多尺度合成和陆地模式相互比较项目（MsTMIP）指定的共同强迫数据、旋转和运行协议驱动。我们做了十几个模型实验。我们确定了三大优势和三大挑战。这些好处包括：（a）通过MIP处理多个模型是相对简单的，（B）MIP协议在模型之间一致地运行，这可以减少一些模型输出可变性，以及（c）独特的多模型实验可以提供用于分析的新输出。这些挑战是：（a）技术需求很大，特别是在数据和产出的储存和转移方面;（B）模型版本落后于核心模型开发小组的版本;（c）仍然需要核心模型开发小组的智力投入，以便深入了解模型结果。与开源、基于云的预测生态系统分析器（PEcAn）生态信息系统的合并可能是克服这些挑战的一条途径。我们运行了九个陆地生物圈模型，集中在一个通用的计算框架上。农场允许相对快速和统一地运行多个MIP实验，挑战包括技术需求，模型版本和结果解释
Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ‐GUESS, ORCHIDEE, SiB‐3, and SiB‐CASA. All models were wrapped in a software framework driven with common forcing data, spin‐up, and run protocols specified by the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open‐source, cloud‐based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges. We ran nine terrestrial biosphere models centralized on a common computing framework The Farm allows multiple MIP experiments to be run relatively quickly and uniformly Challenges included technological demand, model versioning, and interpretation of results