The paper discusses the potential of Earth and climate foundation models (FMs) in advancing Earth and climate sciences. These models are designed to be versatile, capable of adapting to a wide range of downstream tasks, and are trained on large, diverse datasets. The authors identify eleven key features that an ideal Earth and climate FM should possess, including geolocation embedding, balanced geographical representations, scale awareness, wavelength embedding, the time variable, multisensory capabilities, task-agnostic learning, and carbon minimization. Additional desirable features include uncertainty quantification, physical consistency, and AI assistants. The paper highlights the importance of large-scale data sources such as Earth observation (EO) data, weather patterns, and climate models in training these FMs. It also discusses the challenges in achieving an ideal FM, including data scarcity, computational costs, and the need for standardized evaluation benchmarks. Current models are limited in their coverage of the required features and often focus on specific aspects of Earth and climate data. The paper outlines the current state of Earth and climate FMs, noting that while there have been significant advances, there are still gaps in terms of spatial resolution, temporal scales, and the integration of different data modalities. It also emphasizes the need for future research to address these gaps, including the development of more efficient and interpretable models, the incorporation of physical consistency, and the improvement of uncertainty quantification. The authors propose a comprehensive approach to designing and training Earth and climate FMs, including the use of dynamic encoders, spatio-temporal analysis, multi-modal learning, and the integration of physical principles. They also discuss the importance of standardized benchmarks for evaluating these models and the need for continued research to improve their performance and applicability in various domains. The paper concludes with recommendations for future research directions, including energy-efficient adaptation, foundation model enabled sciences, machine unlearning, continual learning, adversarial defenses, interpretability, and cross-disciplinary inspiration.The paper discusses the potential of Earth and climate foundation models (FMs) in advancing Earth and climate sciences. These models are designed to be versatile, capable of adapting to a wide range of downstream tasks, and are trained on large, diverse datasets. The authors identify eleven key features that an ideal Earth and climate FM should possess, including geolocation embedding, balanced geographical representations, scale awareness, wavelength embedding, the time variable, multisensory capabilities, task-agnostic learning, and carbon minimization. Additional desirable features include uncertainty quantification, physical consistency, and AI assistants. The paper highlights the importance of large-scale data sources such as Earth observation (EO) data, weather patterns, and climate models in training these FMs. It also discusses the challenges in achieving an ideal FM, including data scarcity, computational costs, and the need for standardized evaluation benchmarks. Current models are limited in their coverage of the required features and often focus on specific aspects of Earth and climate data. The paper outlines the current state of Earth and climate FMs, noting that while there have been significant advances, there are still gaps in terms of spatial resolution, temporal scales, and the integration of different data modalities. It also emphasizes the need for future research to address these gaps, including the development of more efficient and interpretable models, the incorporation of physical consistency, and the improvement of uncertainty quantification. The authors propose a comprehensive approach to designing and training Earth and climate FMs, including the use of dynamic encoders, spatio-temporal analysis, multi-modal learning, and the integration of physical principles. They also discuss the importance of standardized benchmarks for evaluating these models and the need for continued research to improve their performance and applicability in various domains. The paper concludes with recommendations for future research directions, including energy-efficient adaptation, foundation model enabled sciences, machine unlearning, continual learning, adversarial defenses, interpretability, and cross-disciplinary inspiration.