Key points are not available for this paper at this time.
Numerical Weather Prediction (NWP) has evolved from atmospheric-only formulations to comprehensive Earth System Models (ESMs), driven by advances in model resolution, physical realism, and computational power. While many reviews of ESMs exist, this study discussed the developmental pathway and provided a comparative analysis of atmospheric general circulation models (AGCMs), oceanic general circulation models (OGCMs), coupled atmosphere–ocean general circulation models (AOGCMs), and fully integrated ESMs. This review emphasizes the unique value of coupled models in representing feedback and teleconnections within the Earth system by examining improvements in the simulation of critical climate phenomena. These advances are linked to refined coupling strategies, enhanced parameterization schemes, and high-resolution initiatives. Recent developments in artificial intelligence (AI) and machine learning (ML) are highlighted for their potential to improve predictions, reduce systematic errors, and enable hybrid physics–data models. Ocean models such as Nucleus of European Modeling of the Ocean (NEMO) and Hybrid Coordinate Ocean Model (HYCOM) are assessed for their contributions to mesoscale dynamics, air–sea interactions, and biogeochemical cycling. Nonetheless, persistent challenges remain, including high computational costs, improved cloud and precipitation physics representation, and consistent cross-component initialization. This review concludes by identifying critical research gaps and discussing how AI-augmented physics-based models can support the development of scalable, transparent, and policy-relevant prediction systems.
Waqas et al. (Wed,) studied this question.