View in EDS HTML Full Text PDF Full Text

The Climate Modeling Alliance Atmosphere Dynamical Core: Concepts, Numerics, and Scaling.

Saved in:
Bibliographic Details
Title: The Climate Modeling Alliance Atmosphere Dynamical Core: Concepts, Numerics, and Scaling.
Authors: Yatunin, Dennis1 (AUTHOR), Byrne, Simon1,2 (AUTHOR), Kawczynski, Charles1 (AUTHOR), Kandala, Sriharsha1 (AUTHOR), Bozzola, Gabriele1 (AUTHOR), Sridhar, Akshay1 (AUTHOR), Shen, Zhaoyi1 (AUTHOR), Jaruga, Anna1 (AUTHOR), Sloan, Julia1 (AUTHOR), He, Jia1,3 (AUTHOR), Huang, Daniel Zhengyu1,4 (AUTHOR), Barra, Valeria1,5 (AUTHOR), Chew, Ray1 (AUTHOR), Boral, Anudhyan6 (AUTHOR), Chen, Yi‐fan6 (AUTHOR), Knoth, Oswald1,7 (AUTHOR), Ullrich, Paul8,9 (AUTHOR), Mbengue, Cheikh1 (AUTHOR), Schneider, Tapio1,6 (AUTHOR) tapio@caltech.edu
Source: Journal of Advances in Modeling Earth Systems. Mar2026, Vol. 18 Issue 3, p1-60. 60p.
Subject Terms: *Atmospheric circulation, Spectral element method, Finite difference method, Parallel processing, Atmospheric thermodynamics, Atmospheric models
Abstract: This paper presents the dynamical core of the Climate Modeling Alliance (CliMA) atmosphere model, designed for efficient simulation of a wide range of atmospheric flows across scales. The core uses the nonhydrostatic equations of motion for a deep atmosphere, discretized with a hybrid approach that combines a spectral element method (SEM) in the horizontal and a staggered finite‐difference method in a height‐based, terrain‐following coordinate in the vertical. This approach leverages the high‐order accuracy and scalability of the SEM, while maintaining the computational efficiency and stability of finite differences on a staggered grid. The model's coordinate‐independent equation set allows for simulations in a variety of geometries and planetary configurations. The use of the specific total energy of moist air as a prognostic variable, along with a consistent thermodynamic formulation, ensures the conservation of energy, air mass, and water mass, even in moist atmospheres and in the presence of subgrid‐scale parameterizations, without ad hoc fixers. A horizontally explicit, vertically implicit (HEVI) timestepping strategy treats fast vertical processes implicitly and further enhances computational efficiency by allowing larger timesteps. The model demonstrates excellent strong and weak scaling on CPUs and GPUs, making it well‐suited for high‐resolution simulations on modern supercomputing architectures, including those on the cloud, which widens access to climate models. Plain Language Summary: We have developed a new model to simulate Earth's and other planet's atmospheres accurately and efficiently. The model can be used to predict weather and study climate change on scales from kilometers to hundreds of kilometers. The model's physical formulation and discretization are designed to ensure that important properties such as the atmosphere's total energy and mass are conserved, which is crucial for preventing model drift in long‐term climate studies. It is also designed to work well on different types of computer architectures, including graphics processing units (GPUs) widely available for cloud computing. This democratizes access to core climate modeling tools. Key Points: The paper introduces a nonhydrostatic dynamical core with consistent moist thermodynamics and total moist energy as prognostic variableThe model uses a hybrid spectral element/finite difference discretization that conserves mass, total moist energy, and waterExcellent CPU/GPU scaling makes the model suitable for cloud computing [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell 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.)
Database: GreenFILE
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: 8gh
DbLabel: GreenFILE
An: 192596608
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The Climate Modeling Alliance Atmosphere Dynamical Core: Concepts, Numerics, and Scaling.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yatunin%2C+Dennis%22">Yatunin, Dennis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Byrne%2C+Simon%22">Byrne, Simon</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kawczynski%2C+Charles%22">Kawczynski, Charles</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kandala%2C+Sriharsha%22">Kandala, Sriharsha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bozzola%2C+Gabriele%22">Bozzola, Gabriele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sridhar%2C+Akshay%22">Sridhar, Akshay</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Zhaoyi%22">Shen, Zhaoyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jaruga%2C+Anna%22">Jaruga, Anna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sloan%2C+Julia%22">Sloan, Julia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Jia%22">He, Jia</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Daniel+Zhengyu%22">Huang, Daniel Zhengyu</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barra%2C+Valeria%22">Barra, Valeria</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chew%2C+Ray%22">Chew, Ray</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Boral%2C+Anudhyan%22">Boral, Anudhyan</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yi‐fan%22">Chen, Yi‐fan</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Knoth%2C+Oswald%22">Knoth, Oswald</searchLink><relatesTo>1,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ullrich%2C+Paul%22">Ullrich, Paul</searchLink><relatesTo>8,9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mbengue%2C+Cheikh%22">Mbengue, Cheikh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schneider%2C+Tapio%22">Schneider, Tapio</searchLink><relatesTo>1,6</relatesTo> (AUTHOR)<i> tapio@caltech.edu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Advances+in+Modeling+Earth+Systems%22">Journal of Advances in Modeling Earth Systems</searchLink>. Mar2026, Vol. 18 Issue 3, p1-60. 60p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Atmospheric+circulation%22">Atmospheric circulation</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+element+method%22">Spectral element method</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+difference+method%22">Finite difference method</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+thermodynamics%22">Atmospheric thermodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper presents the dynamical core of the Climate Modeling Alliance (CliMA) atmosphere model, designed for efficient simulation of a wide range of atmospheric flows across scales. The core uses the nonhydrostatic equations of motion for a deep atmosphere, discretized with a hybrid approach that combines a spectral element method (SEM) in the horizontal and a staggered finite‐difference method in a height‐based, terrain‐following coordinate in the vertical. This approach leverages the high‐order accuracy and scalability of the SEM, while maintaining the computational efficiency and stability of finite differences on a staggered grid. The model's coordinate‐independent equation set allows for simulations in a variety of geometries and planetary configurations. The use of the specific total energy of moist air as a prognostic variable, along with a consistent thermodynamic formulation, ensures the conservation of energy, air mass, and water mass, even in moist atmospheres and in the presence of subgrid‐scale parameterizations, without ad hoc fixers. A horizontally explicit, vertically implicit (HEVI) timestepping strategy treats fast vertical processes implicitly and further enhances computational efficiency by allowing larger timesteps. The model demonstrates excellent strong and weak scaling on CPUs and GPUs, making it well‐suited for high‐resolution simulations on modern supercomputing architectures, including those on the cloud, which widens access to climate models. Plain Language Summary: We have developed a new model to simulate Earth's and other planet's atmospheres accurately and efficiently. The model can be used to predict weather and study climate change on scales from kilometers to hundreds of kilometers. The model's physical formulation and discretization are designed to ensure that important properties such as the atmosphere's total energy and mass are conserved, which is crucial for preventing model drift in long‐term climate studies. It is also designed to work well on different types of computer architectures, including graphics processing units (GPUs) widely available for cloud computing. This democratizes access to core climate modeling tools. Key Points: The paper introduces a nonhydrostatic dynamical core with consistent moist thermodynamics and total moist energy as prognostic variableThe model uses a hybrid spectral element/finite difference discretization that conserves mass, total moist energy, and waterExcellent CPU/GPU scaling makes the model suitable for cloud computing [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=192596608
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1029/2025MS005014
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 60
        StartPage: 1
    Subjects:
      – SubjectFull: Atmospheric circulation
        Type: general
      – SubjectFull: Spectral element method
        Type: general
      – SubjectFull: Finite difference method
        Type: general
      – SubjectFull: Parallel processing
        Type: general
      – SubjectFull: Atmospheric thermodynamics
        Type: general
      – SubjectFull: Atmospheric models
        Type: general
    Titles:
      – TitleFull: The Climate Modeling Alliance Atmosphere Dynamical Core: Concepts, Numerics, and Scaling.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yatunin, Dennis
      – PersonEntity:
          Name:
            NameFull: Byrne, Simon
      – PersonEntity:
          Name:
            NameFull: Kawczynski, Charles
      – PersonEntity:
          Name:
            NameFull: Kandala, Sriharsha
      – PersonEntity:
          Name:
            NameFull: Bozzola, Gabriele
      – PersonEntity:
          Name:
            NameFull: Sridhar, Akshay
      – PersonEntity:
          Name:
            NameFull: Shen, Zhaoyi
      – PersonEntity:
          Name:
            NameFull: Jaruga, Anna
      – PersonEntity:
          Name:
            NameFull: Sloan, Julia
      – PersonEntity:
          Name:
            NameFull: He, Jia
      – PersonEntity:
          Name:
            NameFull: Huang, Daniel Zhengyu
      – PersonEntity:
          Name:
            NameFull: Barra, Valeria
      – PersonEntity:
          Name:
            NameFull: Chew, Ray
      – PersonEntity:
          Name:
            NameFull: Boral, Anudhyan
      – PersonEntity:
          Name:
            NameFull: Chen, Yi‐fan
      – PersonEntity:
          Name:
            NameFull: Knoth, Oswald
      – PersonEntity:
          Name:
            NameFull: Ullrich, Paul
      – PersonEntity:
          Name:
            NameFull: Mbengue, Cheikh
      – PersonEntity:
          Name:
            NameFull: Schneider, Tapio
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19422466
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Journal of Advances in Modeling Earth Systems
              Type: main
ResultId 1