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<p>This review examines recent advancements in climate modeling, focusing on enhancing General Circulation Models (GCMs) through refined representations of complex climate phenomena. Key challenges are explored, including improved data assimilation, advanced parameterization, and the integration of socio-economic dimensions for deeper insights into human-climate interactions. Furthermore, the role of Artificial Intelligence (AI) and Machine Learning (ML) in refining sub-grid parameterizations and reducing model biases is examined, offering a path to improve forecast accuracy and resolution. Emphasizing the importance of phenomenological content in capturing essential climate processes, this work advocates for an approach that marries scientific rigor with ethical accountability, opening new directions in climate model development.</p>

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Advanced climate modeling frameworks: state-of-the-art techniques, uncertainties, and the principle of responsibility

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Jamel Chahed

摘要

This review examines recent advancements in climate modeling, focusing on enhancing General Circulation Models (GCMs) through refined representations of complex climate phenomena. Key challenges are explored, including improved data assimilation, advanced parameterization, and the integration of socio-economic dimensions for deeper insights into human-climate interactions. Furthermore, the role of Artificial Intelligence (AI) and Machine Learning (ML) in refining sub-grid parameterizations and reducing model biases is examined, offering a path to improve forecast accuracy and resolution. Emphasizing the importance of phenomenological content in capturing essential climate processes, this work advocates for an approach that marries scientific rigor with ethical accountability, opening new directions in climate model development.