What's next for climate modeling? Five internationally recognized female scientists have joined forces to come up with an answer. They argue: Instead of picking just one path, climate research should integrate multiple approaches.
Climate research currently has a wide range of new tools at its disposal. This has sparked a debate about the merits of each and raised the question which path should be taken. Five internationally renowned female climate scientists are now calling for an integration of multiple approaches rather than an either/or choice. In a recently published analysis, they draw lessons from the past of climate research for the present and future of the field.
The authors, one of whom is Sarah Kang, Director at the Max Planck Institute for Meteorology (MPI-M), trace the history of climate modeling from the first numerical models of the atmosphere and ocean to today's complex Earth system models. Here, the increase in complexity over the decades does not only reflect the increase in computing power, but has also been used as a method to improve understanding. This progress is documented in the assessment reports of the Intergovernmental Panel on Climate Change (IPCC). The Earth system models used there are an important and reliable source of understanding the impacts of climate change.
Looking for the right way forward
However, as climate change is getting more and more noticeable, and the observational record is growing larger and larger, the limitations of climate models are also becoming apparent. This comes at a time when accurate predictions of changes are more important than ever before. Researchers have argued that climate modeling is at a crossroads, and a step change is required to accelerate progress. Among the suggested paths forward are kilometer-scale modeling, parameter tuning, and the use of artificial intelligence (AI). So far, however, none of these approaches has revolutionized climate science on its own.
The five scientists – Simona Bordoni, Sarah Kang, Tiffany Shaw, Isla Simpson, and Laure Zanna –suggest pursuing and integrating several approaches rather than putting all the eggs in one basket. This is a lesson that can be learned from the past, they say: for example, the success of numerical weather prediction was based on increasing resolution, improving the representation of small-scale processes, increasing ensemble size, and on more observations leading to a more accurate knowledge of initial conditions. Even the more recent use of AI in weather forecasting is not a single-handed approach, as AI requires training data derived from physics-based models.
Diversity is key
Building on these lessons from the past, the scientists argue for an integrative approach that recognizes the interdependence of the different approaches and tools and exploits synergies between them. In other words, the researchers suggest embracing diversity – not just in terms of scientific approaches, but also in terms of people, as this will help the field tackle the complex challenges it faces.
Prof. Dr. Sarah M. Kang, Max Planck Institute for Meteorology, sarah.kang@mpimet.mpg.de
Bordoni, S., Kang, S.M., Shaw, T.A., Simpson, I.R., and Zanna, L. The futures of climate modeling. npj Climate and Atmospheric Science 8, 99 (2025). https://doi.org/10.1038/s41612-025-00955-8
Convergent paths toward advances in climate modeling.
Yvonne Schrader/MPI-M
CC BY 4.0 Bordoni, S. et al. npj Clim Atmos Sci 8, 99 (2025). https://doi.org/10.1038/s41612-025-00955-8
Climate models have become increasingly complex since the middle of the last century.
Yvonne Schrader/MPI-M
CC BY 4.0 Bordoni, S. et al. npj Clim Atmos Sci 8, 99 (2025). https://doi.org/10.1038/s41612-025-00955-8
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Convergent paths toward advances in climate modeling.
Yvonne Schrader/MPI-M
CC BY 4.0 Bordoni, S. et al. npj Clim Atmos Sci 8, 99 (2025). https://doi.org/10.1038/s41612-025-00955-8
Climate models have become increasingly complex since the middle of the last century.
Yvonne Schrader/MPI-M
CC BY 4.0 Bordoni, S. et al. npj Clim Atmos Sci 8, 99 (2025). https://doi.org/10.1038/s41612-025-00955-8
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