International Journal of Innovative Computer Science and IT Research

E-ISSN: 3067-1108

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Volume 2 Issue 9
September 2026

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Advanced Computing for Climate Modelling: Integrating AI, High-Performance Computing and Earth-System Science

Author(s) Miska Luoto
Country Finland
Abstract Climate modelling requires the numerical representation of interacting atmospheric, oceanic, terrestrial, cryospheric, and biogeochemical processes across spatial and temporal scales. Increasing model resolution, process complexity, ensemble size, and data volume has made climate science one of the most computationally demanding areas of research. High-performance computing enables large coupled simulations, but higher computational capacity alone cannot resolve persistent problems associated with subgrid processes, structural uncertainty, parameter calibration, data assimilation, and computational energy consumption. Artificial intelligence offers complementary capabilities for learning unresolved processes, constructing emulators, improving parameter estimation, detecting model biases, downscaling coarse outputs, and accelerating data analysis.
This paper examines an integrated AI–high-performance-computing framework for advanced climate modelling. A structured integrative review is combined with a transparent conceptual simulation comparing conventional physics-based and AI–HPC hybrid workflows across six relative computing budgets. The modeled multi-variable error values are illustrative and do not represent observations, operational forecasts, or real climate projections. The simulation suggests that both workflows improve with additional computing resources, but the assumed hybrid workflow achieves a larger reduction in normalized error because it combines physical simulation with data-driven parameterization and computational acceleration.
The analysis emphasizes that machine learning should complement rather than replace physical understanding. Models trained on historical or present-day conditions may fail under unfamiliar climate regimes, while apparently accurate offline parameterizations can produce unstable coupled simulations. Reliable integration therefore requires conservation constraints, uncertainty quantification, independent climate-regime testing, reproducible software, transparent data provenance, and continuous comparison with observations. Other challenges include exascale software adaptation, data-transfer bottlenecks, numerical precision, unequal access to computing infrastructure, and the environmental footprint of large-scale computation. The paper concludes that the most credible pathway is a hybrid climate-modelling architecture in which physical laws provide structure, AI supports selected computational tasks, high-performance computing enables scale, and Earth-system science governs interpretation.
Keywords artificial intelligence; climate simulation; Earth-system modelling; high-performance computing; machine learning; numerical modelling; scientific computing; uncertainty quantification.
Field Engineering
Published In Volume 2, Issue 9, September 2026
Published On 2026-09-05
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E-ISSN: 3067-1108
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