MIAMI, FL — A groundbreaking development in climate science has emerged from the University of Miami, where researchers have introduced a global atmospheric modeling framework that promises to revolutionize climate education and research. This innovative tool, written entirely in Python and designed to operate on the interactive Jupyter Notebook platform, aims to democratize access to sophisticated climate modeling by removing longstanding technical barriers.
The new framework is a significant departure from traditional climate models, which often rely on complex Fortran code and intricate setups that can be both expensive and time-consuming. By contrast, this open-source, Python-based tool allows users to conduct experiments, analyze data, and visualize results with ease, all within a single notebook environment. This accessibility makes it particularly valuable for educators and students, who can tailor exercises to various levels of complexity, as well as for researchers looking to explore original investigations into atmospheric dynamics.
Breaking Down Barriers in Climate Modeling
Ben Kirtman, dean of the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science and lead author of the study, emphasized the importance of Python’s widespread use and beginner-friendly syntax. “Python’s widespread use—and its clarity for beginners—were critical to our decision,” he stated. “It also supports advanced features like machine learning and artificial intelligence for handling large datasets, which simply aren’t as accessible in traditional Fortran models.”
Kirtman’s decision to recode the models in Python was driven by his observations of students struggling with existing systems. The delays in running experiments often hindered their progress and slowed research momentum. Marybeth Arcodia, a co-author of the study and assistant professor at the Rosenstiel School, experienced these challenges firsthand during her graduate studies. Her research focused on long-term climate scenarios and weather patterns such as the El Niño–Southern Oscillation (ENSO), a complex climate pattern that affects global weather systems.
Innovative Features and Educational Potential
The new framework stands out due to several key innovations. Its Python-based core is not only easy to learn and modify but also allows users to adjust atmospheric settings to experiment with different levels of complexity. The model can simulate real-world influences such as heat sources, land features, and ocean conditions, making it suitable for both educational purposes and advanced research.
“In its first demonstrations, the model successfully replicated global climate patterns associated with El Niño events, highlighting its ability to capture these complex phenomena,” Arcodia noted. This capability underscores the model’s potential to enhance understanding of teleconnections, where climate anomalies in one region affect distant parts of the globe.
Collaboration and Future Directions
The development of this framework was made possible through collaboration with the Frost Institute for Data Science and Computing, which helped manage the substantial datasets required. With its successful initial demonstrations, the framework shows strong potential for both education and scientific discovery. Looking forward, Kirtman is developing an experiential climate modeling course for undergraduate and graduate students, enabling them to design and test their own climate scenarios using the new tool.
To maximize its impact, the framework is available as open-source software on GitHub, ensuring global access for educators, students, and researchers. The study, titled “A Simplified-Physics Atmosphere General Circulation Model for Idealized Climate Dynamics Studies,” was published online on August 22, 2025, in the Bulletin of the American Meteorological Society.
Funding and Acknowledgments
The study received funding from the National Oceanic Atmospheric Administration and the National Science Foundation. The authors expressed gratitude to Brian Mapes, professor of atmospheric sciences at the University of Miami Rosenstiel School, for his helpful discussions. Ben P. Kirtman, the William R Middelthon Chair of Earth Sciences, acknowledged the associated support.
The authors of the study include a diverse team from various institutions, highlighting the collaborative nature of this groundbreaking research. The University of Miami, a private research university with a vibrant academic community, continues to build bridges across geographic, cultural, and intellectual borders, fostering a spirit of innovation and commitment to addressing global challenges.
Founded in 1943, the Rosenstiel School of Marine, Atmospheric, and Earth Science is recognized as one of the world’s premier research institutions. Its research programs focus on improving the understanding and prediction of Earth’s geological, oceanic, and atmospheric systems, with goals ranging from better forecasting of extreme weather to preserving marine species.
