Climate Informatics: Machine Learning for the Study of Climate Change

C. Monteleoni, Colorado University (USA)

Abstract: Despite the scientific consensus on climate change, drastic uncertainties remain. Crucial questions about regional climate trends, changes in extreme events, such as heat waves and mega-storms, and understanding how climate varied in the distant past, must be answered in order to improve predictions, assess impacts and vulnerability, and inform mitigation and sustainable adaptation strategies. Machine learning can help answer such questions and shed light on climate change. The talk gave an overview of climate informatics research carried out at Colorado University, focusing on challenges in learning from spatiotemporal data and climate model projections, along with semi- and unsupervised deep learning approaches to studying rare and extreme events, and downscaling temperature and precipitation.