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Updated: Jun 27, 2026
06:10
Using Generative Art to Convey Past and Future Climate Transitions
Published on: March 31, 2023
Developing the next-generation climate system models: challenges and achievements
Julia Slingo1, Kevin Bates, Nikos Nikiforakis
1NCAS-Climate, Walker Institute for Climate Research, University of Reading, Reading RG6 6AH, UK. j.m.slingo@reading.ac.uk
Summary
Climate model improvements are slowing. This paper suggests adaptive mesh refinement and new methods for multiscale modeling to better represent Earth system processes and leverage petascale computing for improved weather and climate forecasting.
Area of Science:
- Earth System Science
- Climate Modeling
- Computational Fluid Dynamics
Background:
- Climate models face diminishing returns in accuracy and efficiency improvements.
- Legacy codes struggle with massively parallel tera/petascale computing architectures.
- Current models lack the resolution to fully represent the multiscale nature of the climate system.
Purpose of the Study:
- To explore the potential of adaptive mesh refinement (AMR) for climate and weather forecasting.
- To address the challenges of incorporating biological and human systems into climate models.
- To advocate for a new generation of Earth system models leveraging advanced computational techniques.
Main Methods:
- Review of current issues in climate and Earth system modeling.
- Discussion of adaptive mesh refinement for multiscale representation.
- Exploration of computational fluid dynamics developments for novel approaches.
Main Results:
- Incremental improvements in climate models may be plateauing.
- Existing models are ill-suited for massively parallel computing and multiscale phenomena.
- Integrating biological and human factors presents significant modeling challenges.
Conclusions:
- Adaptive mesh refinement could enhance scaling and spatial scale representation in forecasting.
- Radically different methods are needed to model the complexity of Earth system processes.
- A new generation of climate models is likely necessary to meet future challenges.
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