Open AccessThe Terrestrial Biosphere Model Farm
Author(s) -
Fisher Joshua B.,
Sikka Munish,
Block Gary L.,
Schwalm Christopher R.,
Parazoo Nicholas C.,
Kolus Hannah R.,
Sok Malen,
Wang Audrey,
GagneLandmann Anna,
Lawal Shakirudeen,
Guillaume Alexandre,
Poletti Alyssa,
Schaefer Kevin M.,
Masri Bassil,
Levy Peter E.,
Wei Yaxing,
Dietze Michael C.,
Huntzinger Deborah N.
Publication year - 2022
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2021ms002676
Subject(s) - biosphere , computer science , forcing (mathematics) , climate model , coupled model intercomparison project , cloud computing , scale (ratio) , environmental science , climate change , distributed computing , climatology , ecology , physics , quantum mechanics , biology , geology , operating system
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.