Project Description:
Regional Climate Models (RCMs) are physics-based models that simulate the regional climate. However, RCMs are extremely computationally expensive to run. The goal is to develop a hybrid RCM emulator, driven entirely by AI but guided through physics, with run times at least 1,000 times faster than current RCMs. Additionally, it will dramatically reduce RCM computing costs and enable better uncertainty quantification of climate-related risks for New Zealand. From the preliminary results and international research, it is estimated that an AI-driven RCM would cost between $30,000–100,000 in computational training and runtime. Whereas a physics-based model will cost $30-50M if were to run a 500-member ensemble run of century-scale projections at 12km resolution
This project is carried out in collaboration with the National Insitute of Water and Atmospheric Research (NIWA).
Keywords: Climate Modelling, Self-supervised Learning, Explainable AI
