The field of time series prediction and earth sciences is rapidly evolving, with a focus on developing innovative models and frameworks that can accurately predict complex phenomena. Recent research has emphasized the importance of integrating knowledge from multiple disciplines, such as atmospheric science, computer science, and agricultural science, to improve the accuracy and reliability of predictions. One notable trend is the increasing use of foundation models, which have shown promising results in predicting crop yields, soil greenhouse gas fluxes, and other environmental variables. These models have the potential to simplify the prediction process and reduce the need for extensive feature engineering. Another area of research is the development of hierarchical architectures for time series forecasting, which can capture patterns and relationships at multiple temporal scales. These models have been shown to outperform traditional methods and have significant implications for decision-making in various applications. Noteworthy papers in this area include those that propose novel frameworks for integrating time series data with natural language, such as the ITFormer model, and those that develop new benchmarks for evaluating the performance of machine learning models on tabular data, such as the TabArena system. Overall, the field is moving towards more integrated and interdisciplinary approaches, with a focus on developing models and frameworks that can capture complex relationships and patterns in time series data.