The article "Optimizing Climate Models with Process Knowledge, Resolution, and Artificial Intelligence" by Schneider, Leung, and Wills discusses the urgent need for accelerated progress in climate modeling to effectively address climate change adaptation. The central challenge lies in accurately representing small-scale processes like turbulence and cloud formation, which are not explicitly resolvable in current models. The authors propose a balanced approach that leverages both traditional process-based parameterizations and contemporary artificial intelligence (AI) methods to model subgrid-scale processes. This strategy uses AI to derive data-driven closure functions from observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing model resolution to resolve a larger fraction of small-scale processes can also aid in improving climate predictions, but current feasible horizontal resolutions are limited to around 10 km due to computational constraints. The authors emphasize the importance of synergizing scientific development with advanced AI techniques to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. They highlight the need for process-based parameterizations grounded in governing equations, avoiding artificial scale breaks, incorporating subgrid-scale memory, and coupling different process schemes consistently. The article also discusses the challenges and benefits of increasing model resolution, noting that while higher horizontal resolution can improve the representation of vertical motions, it comes with significant computational costs. Overall, the authors advocate for a comprehensive approach that combines process knowledge, resolution improvements, and AI to enhance the accuracy and reliability of climate models.The article "Optimizing Climate Models with Process Knowledge, Resolution, and Artificial Intelligence" by Schneider, Leung, and Wills discusses the urgent need for accelerated progress in climate modeling to effectively address climate change adaptation. The central challenge lies in accurately representing small-scale processes like turbulence and cloud formation, which are not explicitly resolvable in current models. The authors propose a balanced approach that leverages both traditional process-based parameterizations and contemporary artificial intelligence (AI) methods to model subgrid-scale processes. This strategy uses AI to derive data-driven closure functions from observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing model resolution to resolve a larger fraction of small-scale processes can also aid in improving climate predictions, but current feasible horizontal resolutions are limited to around 10 km due to computational constraints. The authors emphasize the importance of synergizing scientific development with advanced AI techniques to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. They highlight the need for process-based parameterizations grounded in governing equations, avoiding artificial scale breaks, incorporating subgrid-scale memory, and coupling different process schemes consistently. The article also discusses the challenges and benefits of increasing model resolution, noting that while higher horizontal resolution can improve the representation of vertical motions, it comes with significant computational costs. Overall, the authors advocate for a comprehensive approach that combines process knowledge, resolution improvements, and AI to enhance the accuracy and reliability of climate models.