Evidence map›Paper›PMID 35860620›Full record

ArticleJournal of advances in modeling earth systems2022

The Terrestrial Biosphere Model Farm.

Joshua B Fisher, Munish Sikka, Gary L Block, Christopher R Schwalm, Nicholas C Parazoo, Hannah R Kolus, Malen Sok, Audrey Wang, Anna Gagne-Landmann, Shakirudeen Lawal and 8 more

Abstract read
In one paragraph

Article in Journal of advances in modeling earth systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. The Terrestrial Biosphere Model Farm.Journal of advances in modeling earth systems · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Joshua B FisherJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.ORCID https://orcid.org/0000-0003-4734-9085
Munish SikkaJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Gary L BlockJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Christopher R SchwalmWoodwell Climate Research Center Falmouth MA USA.
Nicholas C ParazooJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.ORCID https://orcid.org/0000-0002-4424-7780
Hannah R KolusJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.ORCID https://orcid.org/0000-0001-9300-4585
Malen SokJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Audrey WangJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Anna Gagne-LandmannJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Shakirudeen LawalJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Alexandre GuillaumeJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
Alyssa PolettiJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.ORCID https://orcid.org/0000-0003-2599-9060
Kevin M SchaeferNational Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USA.ORCID https://orcid.org/0000-0002-5444-9917
Bassil El MasriDepartment of Earth and Environmental Sciences Murray State University Murray KY USA.ORCID https://orcid.org/0000-0002-1017-5467
Peter E LevyCentre for Ecology and Hydrology Penicuik UK.ORCID https://orcid.org/0000-0002-8505-1901
Yaxing WeiEnvironmental Sciences Division Oak Ridge National Laboratory Climate Change Science Institute Oak Ridge TN USA.ORCID https://orcid.org/0000-0001-6924-0078
Michael C DietzeDepartment of Earth and Environment Boston University Boston MA USA.
Deborah N HuntzingerSchool of Earth and Sustainability Northern Arizona University Flagstaff AZ USA.ORCID https://orcid.org/0000-0003-2998-099X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Earth System Modelecoinformaticecosystem modelland surface modelmodel intercomparison projectPEcAnterrestrial biosphere modelvegetation model

Identifiers

PMID35860620
PMCPMC9285607

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.