The Terrestrial Biosphere Model Farm
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...
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| Published in | Journal of advances in modeling earth systems Vol. 14; no. 2; pp. e2021MS002676 - n/a |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Washington
John Wiley & Sons, Inc
01.02.2022
American Geophysical Union (AGU) John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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.
Plain Language Summary
Comparing models is fundamental to our understanding of how the land surface responds to changes in climate. However, these comparisons are challenging to conduct, requiring the organization of multiple, decentralized teams throughout the world. We explored centralizing these models on a single supercomputing system. The models were all run the same way. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) the centralized system takes a lot of burden off individual teams; (b) running models the same way helps to identify differences in how the world is represented in the models; and (c) the system allows us to run many model experiments relatively quickly. The challenges are: (a) lots of models require lots of data storage and transfer needs; (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. Another system, called PEcAn, which has a lot of tools that can help overcome these challenges, can potentially be used in future work.
Key Points
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 |
|---|---|
| AbstractList | 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.
Comparing models is fundamental to our understanding of how the land surface responds to changes in climate. However, these comparisons are challenging to conduct, requiring the organization of multiple, decentralized teams throughout the world. We explored centralizing these models on a single supercomputing system. The models were all run the same way. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) the centralized system takes a lot of burden off individual teams; (b) running models the same way helps to identify differences in how the world is represented in the models; and (c) the system allows us to run many model experiments relatively quickly. The challenges are: (a) lots of models require lots of data storage and transfer needs; (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. Another system, called PEcAn, which has a lot of tools that can help overcome these challenges, can potentially be used in future work.
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 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.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. 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 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. Abstract 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. 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. Plain Language Summary Comparing models is fundamental to our understanding of how the land surface responds to changes in climate. However, these comparisons are challenging to conduct, requiring the organization of multiple, decentralized teams throughout the world. We explored centralizing these models on a single supercomputing system. The models were all run the same way. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) the centralized system takes a lot of burden off individual teams; (b) running models the same way helps to identify differences in how the world is represented in the models; and (c) the system allows us to run many model experiments relatively quickly. The challenges are: (a) lots of models require lots of data storage and transfer needs; (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. Another system, called PEcAn, which has a lot of tools that can help overcome these challenges, can potentially be used in future work. Key Points 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 |
| Author | Schwalm, Christopher R. Sikka, Munish Parazoo, Nicholas C. Dietze, Michael C. Block, Gary L. Wang, Audrey Fisher, Joshua B. Huntzinger, Deborah N. Wei, Yaxing Masri, Bassil Sok, Malen Lawal, Shakirudeen Gagne‐Landmann, Anna Kolus, Hannah R. Levy, Peter E. Guillaume, Alexandre Poletti, Alyssa Schaefer, Kevin M. |
| AuthorAffiliation | 9 School of Earth and Sustainability Northern Arizona University Flagstaff AZ USA 6 Centre for Ecology and Hydrology Penicuik UK 8 Department of Earth and Environment Boston University Boston MA USA 3 Woodwell Climate Research Center Falmouth MA USA 7 Environmental Sciences Division Oak Ridge National Laboratory Climate Change Science Institute Oak Ridge TN USA 4 National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USA 1 Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA 2 Schmid College of Science and Technology Chapman University Orange CA USA 5 Department of Earth and Environmental Sciences Murray State University Murray KY USA |
| AuthorAffiliation_xml | – name: 7 Environmental Sciences Division Oak Ridge National Laboratory Climate Change Science Institute Oak Ridge TN USA – name: 8 Department of Earth and Environment Boston University Boston MA USA – name: 5 Department of Earth and Environmental Sciences Murray State University Murray KY USA – name: 9 School of Earth and Sustainability Northern Arizona University Flagstaff AZ USA – name: 2 Schmid College of Science and Technology Chapman University Orange CA USA – name: 6 Centre for Ecology and Hydrology Penicuik UK – name: 3 Woodwell Climate Research Center Falmouth MA USA – name: 1 Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA – name: 4 National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USA |
| Author_xml | – sequence: 1 givenname: Joshua B. orcidid: 0000-0003-4734-9085 surname: Fisher fullname: Fisher, Joshua B. email: joshbfisher@gmail.com organization: Chapman University – sequence: 2 givenname: Munish surname: Sikka fullname: Sikka, Munish organization: California Institute of Technology – sequence: 3 givenname: Gary L. surname: Block fullname: Block, Gary L. organization: California Institute of Technology – sequence: 4 givenname: Christopher R. surname: Schwalm fullname: Schwalm, Christopher R. organization: Woodwell Climate Research Center – sequence: 5 givenname: Nicholas C. orcidid: 0000-0002-4424-7780 surname: Parazoo fullname: Parazoo, Nicholas C. organization: California Institute of Technology – sequence: 6 givenname: Hannah R. orcidid: 0000-0001-9300-4585 surname: Kolus fullname: Kolus, Hannah R. organization: California Institute of Technology – sequence: 7 givenname: Malen surname: Sok fullname: Sok, Malen organization: California Institute of Technology – sequence: 8 givenname: Audrey surname: Wang fullname: Wang, Audrey organization: California Institute of Technology – sequence: 9 givenname: Anna surname: Gagne‐Landmann fullname: Gagne‐Landmann, Anna organization: California Institute of Technology – sequence: 10 givenname: Shakirudeen surname: Lawal fullname: Lawal, Shakirudeen organization: California Institute of Technology – sequence: 11 givenname: Alexandre surname: Guillaume fullname: Guillaume, Alexandre organization: California Institute of Technology – sequence: 12 givenname: Alyssa orcidid: 0000-0003-2599-9060 surname: Poletti fullname: Poletti, Alyssa organization: California Institute of Technology – sequence: 13 givenname: Kevin M. orcidid: 0000-0002-5444-9917 surname: Schaefer fullname: Schaefer, Kevin M. organization: University of Colorado – sequence: 14 givenname: Bassil orcidid: 0000-0002-1017-5467 surname: Masri fullname: Masri, Bassil organization: Murray State University – sequence: 15 givenname: Peter E. orcidid: 0000-0002-8505-1901 surname: Levy fullname: Levy, Peter E. organization: Centre for Ecology and Hydrology – sequence: 16 givenname: Yaxing orcidid: 0000-0001-6924-0078 surname: Wei fullname: Wei, Yaxing organization: Climate Change Science Institute – sequence: 17 givenname: Michael C. surname: Dietze fullname: Dietze, Michael C. organization: Boston University – sequence: 18 givenname: Deborah N. orcidid: 0000-0003-2998-099X surname: Huntzinger fullname: Huntzinger, Deborah N. organization: Northern Arizona University |
| BackLink | https://www.osti.gov/servlets/purl/1847509$$D View this record in Osti.gov |
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| CitedBy_id | crossref_primary_10_5194_bg_20_5087_2023 crossref_primary_10_1016_j_jhydrol_2023_129515 crossref_primary_10_1016_j_jhydrol_2023_129696 crossref_primary_10_3390_s24103024 crossref_primary_10_1016_j_scitotenv_2022_158062 |
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| ContentType | Journal Article |
| Copyright | 2022 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1029/2021MS002676 |
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| Snippet | Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are... Abstract Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are... |
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| SubjectTerms | Abrupt/Rapid Climate Change Air/Sea Constituent Fluxes Air/Sea Interactions Atmospheric Atmospheric Composition and Structure Atmospheric Effects Atmospheric Processes Avalanches Benefit‐cost Analysis Biogeochemical Cycles, Processes, and Modeling Biogeochemical Kinetics and Reaction Modeling Biogeochemistry Biogeosciences Biosphere Biosphere models Carbon Climate and Interannual Variability Climate change Climate Change and Variability Climate Dynamics Climate Impact Climate Impacts Climate Variability Climatology Computational Geophysics Cryosphere Decadal Ocean Variability Disaster Risk Analysis and Assessment Earth System Model Earth System Modeling Earthquake Ground Motions and Engineering Seismology ecoinformatic ecosystem model Effusive Volcanism ENVIRONMENTAL SCIENCES Explosive Volcanism General Circulation Geodesy and Gravity Geological Global Change Global Change from Geodesy Gravity and Isostasy Hydrological Cycles and Budgets Hydrology Impacts of Global Change Informatics Intercomparison Laboratories Land information systems land surface model Land use Land/Atmosphere Interactions Marine Geology and Geophysics Mass Balance model intercomparison project Modeling Modelling Mud Volcanism Natural Hazards Numerical Modeling Numerical Solutions Ocean influence of Earth rotation Ocean Monitoring with Geodetic Techniques Ocean/Atmosphere Interactions Ocean/Earth/atmosphere/hydrosphere/cryosphere interactions Oceanic Oceanography: Biological and Chemical Oceanography: General Oceanography: Physical Oceans Paleoceanography PEcAn Physical Modeling Policy Sciences Radio Oceanography Radio Science Regional Climate Change Regional Modeling Risk Sea Level Change Sea Level: Variations and Mean Seismology Software Solid Earth Storage Surface Waves and Tides terrestrial biosphere model Terrestrial ecosystems Terrestrial environments Theoretical Modeling Tsunamis and Storm Surges Vegetation vegetation model Volcanic Effects Volcanic Hazards and Risks Volcano Monitoring Volcano Seismology Volcano/Climate Interactions Volcanology Water Cycles |
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| Title | The Terrestrial Biosphere Model Farm |
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