Containers and orchestration of numerical ocean model for computational reproducibility and portability in public and private clouds: Application of ROMS 3.6

•Containerized regional ocean-modeling system is implemented in various clouds.•Container-based architecture is useful for reproducibility and portability of ROMS.•Container-based HPC architecture increases flexibility in private and public clouds.•Proposed container-based HPC architecture reduces preparation time for model setup.•Kubernetes-managed container cluster architecture is used for ocean modeling. Various numerical models have been used to understand and predict ocean dynamics. For this reason, many information technology (IT) resources are required for high-resolution global ocean modeling. The development of cloud-computing technologies has enabled earth scientists to easily use numerical ocean models that require high-performance computing (HPC) and message-passing interface (MPI) software in private and public clouds. Although it is easier today to use computing resources than it was in the past, computational reproducibility and portability in diverse IT environments remain crucial issues. This study proposes a model execution architecture for computational reproducibility, portability, and agility based on container-based virtualization and orchestration technologies. We implement a containerized regional ocean-modeling system (ROMS), an MPI-based numerical ocean model that exists in various public or private cloud environments (e.g., personal computers and multiple-node servers). The preparation time for model setup is greatly reduced using our container-based HPC architecture. Containerization of ROMS is tested for its support of the portability of numerical modeling in a wide range of public-cloud environments. When leveraging an abstraction layer of complex and diverse infrastructure environments, we can run the ocean model more easily while obtaining computational reproducibility using a shareable deployment code. This advancement can be used to guide the containerization of various numerical models and to run them in parallel in public and private cloud-computing environments.