Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme -- differentiable modeling -- is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages.

Mitch Aide, a tropical ecologist based in Puerto Rico, thinks we should listen to the earth a lot more than we do now -- and not just listen to it, but record and store its sounds on a massive scale. His aims are not spiritual, but scientific: He, his colleagues, and other experts are developing and deploying audio recorders, data transmission systems, and new artificial intelligence software that together are rapidly expanding scientists' ability to understand ecosystems by listening to them. Today, Aide can nail a cheap digital audio recorder to a tree in Puerto Rico's Luquillo Forest and transmit its recordings to a computer running prototype software, which indicates almost in real time whether any of 25 species of frogs and birds are vocalizing in the forest. The system's apparent simplicity belies its power – Aide thinks that it and similar systems will allow scientists to monitor ecosystems in ways we can't yet imagine. He dreams that one day soon, audio recordings of natural soundscapes will be like rainfall and temperature data, collected from a worldwide network of permanent stations, widely available for analysis, and permanently archived.