Advancing Climate Science with Machine Learning

• Main Author: He, Sijie
• Format: Thesis
• Language: English
• Published: 2022
• Subjects: HASH(0x4061e60); Sea ice
• Online Access: https://hdl.handle.net/11299/227920

University of Minnesota Ph.D. dissertation. 2022. Major: Computer Science. Advisor: Arindam Banerjee. 1 computer file (PDF); 145 pages. Climate change is considered one of the greatest challenges for humanity in the twenty-first century. The changing climate affects almost every aspect of people's lives, including but not limited to water, energy, agriculture, ecosystems, economics, safety, and health. In the past decades, due to climate change, extreme events, such as wildfire, droughts, and flooding, have become more frequent and intensive, which can cause devastating economic loss and humanitarian crises. Therefore, skillful climate modeling, which can improve the understanding and predictability of climate behavior, would have immense societal values. In climate science, climate models are used for representing the major climate system components (atmosphere, land, ocean, and sea ice) and their interactions. A climate model consists of mathematical equations derived using fundamental laws of physics, which need to be solved using powerful supercomputers. In general, climate models are an important tool for understanding climate change, and continually become more complete and accurate. Nevertheless, the Earth's climate system is too complex to be fully simulated. The state-of-the-art climate models are not yet perfect for fulfilling all needs in understanding and forecasting climate behaviors, which leaves open opportunities for interdisciplinary climate studies. In the past decades, machine learning (ML), especially deep learning, has achieved remarkable strides in wide-ranging applications. The emergence of climate data with high spatiotemporal resolution also makes it possible to tackle complex climate problems using machine learning techniques. Recent studies have shown the effectiveness of machine learning approaches on various tasks, including weather prediction, climate forecasting, weather extremes detection, etc. The dissertation explores how machine learning techniques can make advances in ...