Statistics and Machine Learning for Attribution of Extreme Events to Climate Change

Learn statistical and machine learning approaches for attributing extreme weather events to climate change in this lecture by Arpita Mondal. Explore the methodological frameworks used to determine whether specific extreme events like heatwaves, floods, or droughts can be linked to anthropogenic climate change. Discover how statistical techniques and machine learning algorithms are applied to analyze observational data and climate model outputs to quantify the role of human activities in increasing the probability or intensity of extreme weather events. Examine case studies demonstrating the application of these methods to real-world attribution studies, including the challenges of distinguishing climate change signals from natural variability. Understand the importance of proper statistical inference, uncertainty quantification, and the interpretation of attribution results for policy and decision-making. Gain insights into the latest developments in probabilistic event attribution, including the use of ensemble climate simulations and advanced statistical modeling techniques that help scientists communicate the relationship between climate change and extreme weather to stakeholders and the public.