The Climate Modeling Alliance Atmosphere Dynamical Core: Concepts, Numerics, and Scaling.
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| Title: | The Climate Modeling Alliance Atmosphere Dynamical Core: Concepts, Numerics, and Scaling. |
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| 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] |
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| 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] |
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| ISSN: | 19422466 |
| DOI: | 10.1029/2025MS005014 |
