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Emulators of Climate Model Output.

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Title: Emulators of Climate Model Output.
Authors: Tebaldi, C.1 (AUTHOR) ctebaldi@umd.edu, Selin, N.E.2,3,4 (AUTHOR), Ferrari, R.2,5 (AUTHOR), Flierl, G.2 (AUTHOR)
Source: Annual Review of Environment & Resources. Oct2025, Vol. 50 Issue 1, p709-737. 29p.
Subject Terms: *Climate change models, *Sustainability, Emulation software, Atmospheric models, Mathematical optimization, Empirical research, Machine learning
Abstract: Researchers' and decision-makers' demand for climate information has outpaced the ability of computationally intensive Earth system models (ESMs) to provide targeted climate projections, particularly when specific output for specific needs is required. Emulators of ESMs—significantly more efficient computationally—aim to produce such information and have seen an accelerated period of development. Emulators' latest generation greatly varies in method, complexity, requirements, and outputs. Some emulators produce only patterns of average quantities, targeting climate responses to anthropogenic forcings. Others simulate quantities at high temporal and spatial frequency, accounting for the climate system internal variability. We survey and categorize different methods; their advantages and limitations, including statistical approaches of various complexity; and machine learning methods. We discuss how a choice of emulator, based on different methods, inputs, and outputs available, might be or not be fit for purpose, for climate and sustainability science. We identify gaps and research needs informing future developments. [ABSTRACT FROM AUTHOR]
Copyright of Annual Review of Environment & Resources is the property of Annual Reviews Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 188498666
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PubTypeId: academicJournal
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  Data: Emulators of Climate Model Output.
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  Data: <searchLink fieldCode="JN" term="%22Annual+Review+of+Environment+%26+Resources%22">Annual Review of Environment & Resources</searchLink>. Oct2025, Vol. 50 Issue 1, p709-737. 29p.
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  Data: *<searchLink fieldCode="DE" term="%22Climate+change+models%22">Climate change models</searchLink><br />*<searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Emulation+software%22">Emulation software</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Researchers' and decision-makers' demand for climate information has outpaced the ability of computationally intensive Earth system models (ESMs) to provide targeted climate projections, particularly when specific output for specific needs is required. Emulators of ESMs—significantly more efficient computationally—aim to produce such information and have seen an accelerated period of development. Emulators' latest generation greatly varies in method, complexity, requirements, and outputs. Some emulators produce only patterns of average quantities, targeting climate responses to anthropogenic forcings. Others simulate quantities at high temporal and spatial frequency, accounting for the climate system internal variability. We survey and categorize different methods; their advantages and limitations, including statistical approaches of various complexity; and machine learning methods. We discuss how a choice of emulator, based on different methods, inputs, and outputs available, might be or not be fit for purpose, for climate and sustainability science. We identify gaps and research needs informing future developments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Annual Review of Environment & Resources is the property of Annual Reviews Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1146/annurev-environ-012125-085838
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 29
        StartPage: 709
    Subjects:
      – SubjectFull: Climate change models
        Type: general
      – SubjectFull: Sustainability
        Type: general
      – SubjectFull: Emulation software
        Type: general
      – SubjectFull: Atmospheric models
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Empirical research
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Emulators of Climate Model Output.
        Type: main
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            NameFull: Tebaldi, C.
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            NameFull: Selin, N.E.
      – PersonEntity:
          Name:
            NameFull: Ferrari, R.
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            NameFull: Flierl, G.
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          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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              Value: 15435938
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              Value: 50
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              Value: 1
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