Introduction:

Recent advances in artificial intelligence (AI) technologies are broadly integrated across all stages of climate prediction systems, driving significant innovations. Deep learning-based weather prediction models are rapidly progressing worldwide, demonstrating performance surpassing that of traditional numerical model-based integrated forecasting systems. In ocean modeling, deep learning simultaneously learns local detailed structures and global ocean configurations, precisely simulating complex ocean dynamics from mesoscale eddies to extensive current patterns, thereby substantially enhancing prediction ability. For land surface modeling, deep learning adopts hybrid forms combined with physics-based approaches to more accurately reproduce intricate land responses, with the integration of vegetation water stress modules enabling realistic depictions of land-atmosphere interactions. In data assimilation, AI contributions are prominent, where deep learning methods utilizing automatic differentiation, diffusion models, and image restoration techniques offer superior computational efficiency and expressiveness compared to conventional data assimilation approaches. Recent technologies like Variational AutoEncoders and Score-based Diffusion effectively incorporate high-dimensional nonlinear characteristics, continuously improving ocean and atmospheric initial field performance. Various deep learning methods exhibit potential for high-resolution enhancement of climate prediction outputs, while deep learning-based generative models are primarily employed for post-processing techniques to correct model biases. Beyond technical advances, AI-based technologies provide a strategic pathway toward operational implementation and climate services, enhancing both forecast accuracy and computational efficiency. Collectively, these developments point toward an integrated, next-generation climate prediction framework that bridges physical modeling, data-driven methods, and practical applications.

pdf : Jo et al.(2025)