NSF Award
2425885
Understanding how climate has changed in the past (the study of paleoclimatology) can help societies better adapt to and anticipate future climate change. To fulfill this promise, researchers need to have rapid access to the abundance of datasets and analytical methods available to them. Furthermore, they may need assistance in choosing the most appropriate technique(s) for their data, applying them correctly, and interpreting conflicting or ambiguous results. Artificial intelligence (AI), and in particular large language models (LLMs), can help in this regard. Indeed, they have already proven useful as coding assistants. This project develops an artificial intelligent system that uses the power of generative AI while incorporating paleoclimate knowledge. The resulting system, PaleoPAL, is used in the context of three paleoclimate studies to evaluate its effectiveness. In addition, the project engages with publishers to provide guidelines for study incorporating AI assistants in the research.<br/><br/>PaleoPAL uses Retrieval Augmented Generation (RAG) to incorporate paleoclimate knowledge (e.g., data, software, methods, workflows, literature) into existing LLMs to create an AI assistant as a Jupyter Notebook, an environment familiar to scientists. This AI assistant helps in the investigation of three paleoclimate problems: placing recent El Niño-Southern Oscillation variations in the context of the last 10,000 years, detecting climate tipping points and their potential precursors, and generating empirically-based, low-cost climate projections. The proposal supports training activities that build capacity in the US workforce, and in particular, teaching a diverse cross-section of the next generation of geoscientists to work with AI assistants, learning from them and challenging them as they would a mentor.<br/><br/>This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences is jointly supported by the National Discovery Cloud for Climate initiative within the Office of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
