Probabilistic Machine Learning in Polar Earth and Climate Science: A Review of Applications and Opportunities

Kim Bente (The University of Sydney), Judy Kay (The University of Sydney) and Roman Marchant (Commonwealth Scientific and Industrial Research Organisation (CSIRO))

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Climate Science & Modeling Uncertainty Quantification & Robustness


Our world’s climate future is on thin ice. The study of longterm weather patterns in the polar regions is an important building block in tackling Climate Change. Our understanding of the past, the present and the future of the earth system, and the inherent uncertainty, informs planning, mitigation, and adaptation strategies. In this work we review previous applications of machine learning and statistical computing to polar climate research, and we highlight promising probabilistic machine learning methods that address the modelling needs of climate-related research in the Arctic and the Antarctic. We discuss common challenges in this interdisciplinary field and provide an overview of opportunities for future work in this novel area of research.