Title: Machine learning for climate modeling
Abstract: Climate simulations remain one of the best tools to understand and predict global and regional climate change. Yet, these simulations demand immense computing power to resolve all relevant scales in the climate system from meters to 1000’s kilometers. Due to the limited computing resources, many aspects of the physics are missing from climate simulations – e.g., ocean mixing or clouds’ effects on temperature and currents. I will describe some of the key challenges in climate modeling and how machine learning tools can help accelerate progress toward accurate climate simulations and reliable climate projections. I will focus on our work capturing ocean turbulence with a range of machine learning techniques, that we have adapted for fluid flows. This will include deep learning with embedded physics constraints, uncertainty quantification for ocean turbulence, and equation discovery of multiscale physics with genetic programming. Some of our work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions. Yet, many questions remain unanswered, making the next decade exciting and challenging for ML + climate modeling for robust and actionable climate projections.