Machine Learning and Earth System Modeling - From Parameter Calibration to Feature Detection

Explore machine learning applications in Earth system modeling through this conference talk from the Kavli Institute for Theoretical Physics. Delve into parameter calibration and feature detection techniques used to advance climate science understanding. Learn about carbon cycle modeling, land models, and atmospheric river detection. Discover how machine learning is applied to climate model outputs, including front detection, seasonal patterns, and extreme precipitation events. Gain insights into the Climate Net project and the Earth System Data Science Initiative. Understand the challenges and opportunities in using big data and machine learning algorithms to inform society about future climate changes at regional and local scales.